The Hidden Differences Between Claude’s Fast, Strong,

The Hidden Differences Between Claude’s Fast, Strong, and Extended Modes

Claude’s large language model (LLM) now comes in multiple “modes” – often referred to as Fast, Strong, and Extended – each tuned for different needs. These modes correspond to different underlying Claude models and reasoning settings, offering distinct trade-offs in speed, cost, and capability. In this in-depth guide, we’ll explore what sets Claude Fast, Claude Strong, and Claude Extended apart.

We’ll compare their practical usage differences, performance benchmarks, ideal use cases, API behaviors, and how to choose the right mode for various scenarios. By the end, you’ll have a clear understanding of how each mode affects response latency, cost, and reasoning depth, enabling you to optimize Claude AI for your specific needs.

Who Should Read This?

This comprehensive article is written for a mixed technical audience – in particular:

  • Developers & AI Engineers: Those integrating Claude via API and building applications that might leverage different modes. You’ll learn about latency differences, cost implications, and how to programmatically select modes for optimal performance.
  • Enterprise Teams Evaluating Usage Tiers: Organizations planning to use Claude at scale and optimize costs vs. capabilities. If you process long documents or complex analyses, or need to choose the right tier for each workload, this guide will clarify the trade-offs between Fast, Strong, and Extended modes.
  • Advanced Claude Users and Content Creators: Power users (knowledge workers, analysts, content creators) who want to understand how each mode impacts response quality and speed. You’ll see examples of how switching modes can affect outputs for chat, coding, summarization, and more.

We’ll balance technical explanations with practical examples so that both developers and non-developers can grasp the nuances. Now, let’s define what each Claude mode really means.

Understanding Claude’s Fast, Strong, and Extended Modes

Claude’s modes aren’t just arbitrary presets – they map to different model configurations and reasoning strategies under the hood. Anthropic (Claude’s creator) has developed a family of Claude models (often codenamed Haiku, Sonnet, and Opus for the 3rd-generation models) that align with these modes:

  • Claude Fast – Powered by the Claude Haiku model (the smallest, fastest variant). “Fast” mode emphasizes near-instant responses using lightweight reasoning. It trades some depth and accuracy for speed and cost-efficiency.
  • Claude Strong – Uses the Claude Sonnet model (mid-sized, general-purpose). “Strong” mode provides a balanced approach with greater intelligence and context handling while still being fairly quick. This is often the default mode for high-quality results without extreme latency.
  • Claude Extended – Typically refers to using the Claude Opus model (the largest, most powerful version) or enabling Extended Thinking mode on a Claude model. This mode allows maximum reasoning depth – it’s slower and costlier, but capable of deeper analysis, longer context, and more complex problem solving. “Extended” mode often leverages additional internal reasoning steps (a “chain-of-thought”) to improve answer quality on tough queries.

Model Mapping: In simple terms, Claude Fast ≈ Haiku, Claude Strong ≈ Sonnet, and Claude Extended ≈ Opus (with extended reasoning). Each successive model offers higher capability at the cost of more computation. All of Claude’s latest models are “hybrid reasoning” AI systems that can operate in a rapid mode or an extended thinking mode within the same model. For example, Claude 4.5 Haiku, Sonnet, and Opus each support both near-instant responses and a deeper reasoning mode depending on settings.

It’s important to note that Extended Thinking is not a separate model at all – it’s a mode of operation. As Anthropic explains, toggling extended mode does not swap in a different strategy or model; it simply lets the same model spend more time and “effort” to arrive at an answer. In other words, extended mode gives Claude a “thinking budget” to reason through problems step-by-step before responding. By contrast, Fast/normal mode prompts Claude to respond quickly using its immediate intuitions.

Let’s break down the key differences in how these modes perform in practice.

Practical Differences in Usage and Behavior

When you switch between Fast, Strong, and Extended modes, you’ll notice differences in response speed, depth of answers, and how the model handles prompts. Here we compare how each mode behaves across common use cases and tasks:

1. Conversation and Quick Q&A

For everyday chat or simple questions, Claude Fast mode excels in responsiveness. It generates answers almost instantly, making it ideal for real-time chatbot interactions or quick informational queries. However, fast mode’s answers tend to be more concise and superficially relevant rather than deeply analytical. This is great for casual conversation or straightforward Q&A – e.g. getting the weather, a definition, or a trivial fact – where you want speed over nuance.

By contrast, Claude Strong mode produces more detailed and thoughtful responses in chats. It’s better at maintaining context over longer conversations and providing well-reasoned answers. You might notice that Strong mode replies feel more “considered” and nuanced, because the model (Sonnet) has higher capacity to understand complex instructions or ambiguous user questions.

The latency is still low (Claude Sonnet is optimized to be fast, often powering live chat agents in production), so for most interactive chats Strong mode offers a sweet spot: fast enough for seamless conversation, with a boost in quality and coherence.

Claude Extended mode in chat will engage the model’s deeper reasoning capabilities. When enabled, Claude may take noticeably longer to answer – you’ll often see a “thinking…” indicator counting seconds in the Claude UI. During this time, it’s literally working through a chain-of-thought internally (which paid users can even expand and view). The benefit is a more comprehensive and reliable answer for complex questions. For example, if a user asks a tricky multi-part question or something requiring analysis, Extended mode lets Claude double-check itself and reason through the steps.

The downside is the added latency – you might wait many seconds (depending on the “thinking budget” given) for a reply that would have been near-instant in Fast mode. Therefore, for simple or everyday chat, you generally would NOT use extended mode – it’s overkill and will just slow things down. Save Extended mode for when a user poses a hard question that warrants the extra thought.

Example: Suppose you ask Claude, “What’s the date of the next solar eclipse visible in South America, and how many days are left until then?” – a question requiring it to recall data and do a calculation. In Fast mode, Claude might quickly give an answer if it knows the date, but there’s a chance of a minor mistake in calculation or phrasing because it’s not deeply checking its work. In Strong mode, Claude is more likely to get it correct and provide a bit of explanation (e.g. stating the date and the days difference).

If you use Extended mode, Claude will take a bit longer but should carefully verify the date (maybe consulting its internal knowledge) and perform the arithmetic step-by-step, likely ensuring the answer is precise. The extended version might even show its step-by-step reasoning in the thought process (e.g. reasoning out the current date vs eclipse date) before giving the final answer.

2. Document Question Answering and Retrieval-Augmented QA

When asking Claude questions about a document or using it in a Retrieval-Augmented Generation (RAG) pipeline, context length and reasoning are critical. All Claude modes support very large context windows (on the order of 100k+ tokens, i.e. entire books), but how they utilize that context differs:

  • Fast mode (Haiku) can ingest a lot of text quickly, but may not fully “soak in” complex documents. It’s optimized for speed, so it might extract obvious answers from a passage but could miss subtleties or cross-references in very long text. If you have a relatively small or well-structured document and you need an answer fast, Claude Fast will do the job and give a concise response. Just be aware that with very long documents, the Fast model might lose track of earlier details or overlook nuance (users report that the smallest model can “lose track” in long sessions or long prompts).
  • Strong mode (Sonnet) is generally the best choice for document QA in most cases. It has a larger capacity and better memory, so it can recall details from a long context more reliably. Claude Strong will take a bit more time than Haiku to process the input, but it excels at returning accurate, contextually-grounded answers from documents. For example, in structured QA tasks (like asking specific questions about a company report or an academic paper), Strong mode can provide a well-structured answer with references to the document content. It balances speed and accuracy, handling up to 200K-token contexts with strong recall (and even a 1M token context in beta for Sonnet). This makes it suitable for enterprise knowledge bases or moderate-length documents where both correctness and latency matter.
  • Extended mode becomes valuable for huge documents or very complex queries across docs. If you are dealing with hundreds of pages (e.g. multiple long PDFs or a whole knowledge base) and you ask a broad analytical question, enabling extended thinking can help Claude systematically plan how to find the answer. In extended mode, Claude might effectively perform a multi-step reasoning: skimming the document for relevant sections, comparing information across sections, and even doing an internal “search” through the text. Because extended thinking allows iterative analysis, it’s ideal for tricky cases like: “Analyze these 5 lengthy legal contracts and tell me where there are conflicting clauses.” Claude Extended could internally break down the task (reading each contract, noting clauses, comparing conflicts) before giving you a comprehensive answer. This is the kind of deep, multi-step analysis that extended mode was designed for. Of course, the trade-off is that it will be slower and consume more compute. For a huge context, extended mode might spend dozens of seconds or more reasoning (depending on complexity). The payoff is higher confidence and thoroughness in the answer. In fact, Anthropic reports that their largest model (Opus with extended reasoning) achieved near-perfect recall on a “needle-in-haystack” test – finding specific facts buried in a massive corpus with >99% accuracy. This shows how effective extended reasoning can be at sifting through long contexts when accuracy is paramount.

In RAG pipelines (where Claude works with a search or database), these principles still apply. Fast mode might quickly summarize retrieved passages but could miss connections between them. Strong mode will more reliably synthesize information from multiple retrieved documents and give a balanced answer. Extended mode can shine in a RAG setting if the question requires analyzing results from multiple search queries or sources – essentially Claude can perform iterative retrieval: formulating follow-up queries, reading multiple chunks, and combining them (if your implementation allows multi-turn reasoning).

In fact, Claude 3.7 introduced improved agentic capabilities – with extended thinking it can iterate on tool use (like performing a series of actions) to solve a task. So an extended Claude could be orchestrated to, say, retrieve a list of relevant documents and then deeply analyze them one by one before answering. This makes Claude Extended very powerful for complex research assistants or analytical QA systems that must scour large knowledge bases with minimal human guidance.

Tip: If your document or knowledge base query is straightforward and speed-sensitive (like a customer support bot fetching one answer from a FAQ), use Fast mode. If it’s a detailed question on a large document or set of docs (like an internal report or multi-page policy), use Strong mode for a reliable answer. Reserve Extended mode for “stumpers” – e.g. extremely long documents (hundreds of pages) or analytical questions that require tracing through many pieces of info. Using extended thinking for every document question will incur a latency cost that might not be justified unless the question is truly complex.

3. Summarization Tasks

Summarizing text is another common use case where mode choice matters. The length and complexity of the text determines which Claude mode is optimal:

  • For short documents or simple summaries, Claude Fast mode is often sufficient. Fast mode can rapidly produce a high-level summary of a few paragraphs or a single article. The summary will cover the main points but might omit finer details or nuance. If you just need a quick gist (e.g. summarizing a news article for a user in real-time), Fast mode’s brevity and speed are advantageous.
  • For detailed or critical summaries, Claude Strong mode is preferable. Strong mode is better at accurate and nuanced summarization – it will capture more key details, preserve the tone or important facts, and generally produce a more coherent summary especially for longer texts. For example, summarizing a 50-page technical report: Strong mode could generate a structured summary (perhaps with section overviews or bullet points) that is faithful to the source. It has the capacity to understand context and priority of information better than the smaller model, reducing the chance of missing an important detail or hallucinating content.
  • When it comes to very long documents or multi-document summarization, Extended mode might be necessary. Suppose you want an executive summary of a 100,000-token research paper or an entire book – Claude Extended can handle it by reading through the content in segments and keeping track of the overall narrative or argument. With extended thinking, Claude can be instructed to summarize each part, then summarize the summaries, and so on, effectively doing a hierarchical summary. This mode can also follow specific summary instructions more rigorously (e.g. “summarize with focus on economic implications and list any statistical results separately”) because it has the headroom to plan out the summary structure internally. Again, this will be slower – summarizing something huge could take many seconds or even minutes if done in one go – but the outcome will likely be more comprehensive and organized. Extended mode also reduces the chance of forgetting content from earlier in the document, since the model can allocate internal “thought” tokens to recall and integrate those points. In fact, Anthropic’s largest model has demonstrated extremely strong recall over long contexts, which is a big plus for faithful summarization.

One thing to note: if you only need a brief summary of a large text (e.g. “In one sentence, what’s the main point of this 200-page book?”), you might not need extended thinking – the model might surface the main theme quickly. Extended mode is most beneficial when you need a detailed or specific summary (like summarizing with certain criteria or ensuring no part of a doc is overlooked).

4. Complex Reasoning & Problem Solving

This category includes things like math problems, logical puzzles, strategic planning, and multi-step reasoning tasks. Here, the differences between modes become very pronounced:

  • Claude Fast (Haiku) tends to handle simple reasoning okay, but it can falter on anything requiring multiple steps of deduction or careful logic. In fast mode, Claude basically relies on its immediate pattern-matching and heuristics. For straightforward problems (“What is 5 + 7?” or a single-step logic question), that’s fine. However, for even slightly tricky problems, Fast mode might produce incorrect or partial answers because it doesn’t spend time to double-check. A common example is those seemingly simple but tricky questions like counting occurrences of a letter, or solving a riddle. In tests, the fast/normal mode often gets tripped up by subtle details. For instance, one evaluation asked: “How many times does the letter P appear in the sentence ‘Alan picked three ripe apples’?” – a question with a deceptive twist (the word “apple” has two P’s). A user test reported that Claude’s normal mode only counted 3 P’s (missing the double ‘pp’), getting the answer wrong. This kind of slip-up happens because the fast mode doesn’t methodically iterate over each letter; it just draws on its training guess, which can overlook duplicates.
  • Claude Strong (Sonnet) performs much better on complex reasoning than the fast model. It has more advanced reasoning abilities out-of-the-box, so it will solve many multi-step problems correctly that stumped the smaller model. In the same example, the Strong mode might still make an occasional mistake (LLMs often have trouble with such tasks), but it stands a better chance of catching the detail. Generally, Sonnet will attempt to break down the problem internally even in normal mode (though not as extensively as extended mode). It’s also more reliable at keeping track of intermediate results and not contradicting itself on logical tasks. For structured reasoning (like solving a multi-step math word problem, or performing an if/then analysis), Strong mode yields more accurate and complete solutions than Fast mode in most cases. Users have noted that the default Claude 3.7 (Sonnet) already made big gains in areas like physics and math compared to earlier versions, thanks to better reasoning – and that’s before even turning on extended mode.
  • Claude Extended is the go-to for any difficult or high-stakes reasoning task. When Extended Thinking mode is enabled, Claude effectively does what we humans might call “showing your work.” It will explicitly think through each step of the problem in a scratchpad (a hidden chain-of-thought), evaluating possibilities, checking its calculations, and only then produce the final answer. The result is a dramatic improvement on many complex problems. In the letter-counting example above, simply enabling extended mode caused Claude to instantly get the correct answer (4 P’s), because the model systematically analyzed each letter of the sentence instead of jumping to a quick conclusion. The extended mode answer included a breakdown explaining how it checked each character, which is why it didn’t miss the double “pp.” This demonstrates how Extended mode provides deeper reasoning even for puzzles that are trivial for humans but non-trivial for a quick neural guess. Likewise, on complex math problems, extended mode shines. Anthropic’s data shows that on tough math benchmarks (like competition-level problems), Claude in extended mode significantly outperforms its normal mode results. It achieved over 96% accuracy on certain physics questions with extended reasoning enabled – essentially solving nearly all of them – whereas standard mode without it would score lower. Extended mode enables Claude to do things like write out proofs, explore multiple solution paths, or double-check calculations. In a sense, it lets Claude use the famous “chain-of-thought” prompting internally, without the user needing to prompt it step by step – the model does it autonomously. It’s worth noting that extended thinking isn’t always necessary for every hard problem – sometimes simply using the larger model (Opus) in normal mode will be sufficient. But for truly complex or novel problems (say, a tricky coding competition problem or a multi-faceted logical puzzle), extended mode gives the best chance of a correct solution. The flip side: it can be slow. Extended mode may consume hundreds or even thousands of “thinking” tokens internally to work out a single answer. This is effectively extra compute time – one report mentions Claude 3.7 extended can use up to 128K tokens internally for reasoning on hard tasks! That’s a huge amount of computation (equivalent to reading an entire book in the background) to ensure a high-quality answer. So you wouldn’t want to deploy that for every single question by default. But when accuracy is paramount, extended mode is an incredible tool.

Bottom line: For any multi-step reasoning task (math, logic, etc.), try Strong mode first for a balance of speed and accuracy. If you find the answers are inaccurate or the problem is particularly knotty, switch to Extended mode – you’ll often see Claude go from giving an okay answer to nailing the solution with clear reasoning, at the cost of some extra seconds. Anthropic themselves describe extended thinking as giving Claude a significant “intelligence boost” on tougher questions. Just be mindful of diminishing returns: on very simple logical tasks, extended mode might not add value and could even overcomplicate the answer (or as some users found anecdotally, occasionally extended thinking might produce an overly elaborate solution when a simple one was possible). It’s a powerful mode best reserved for truly complex challenges.

5. Code Generation and Debugging

Claude is not only for natural language – it’s also used heavily for coding assistance (in fact, Claude 3/4 introduced specialized “Claude Code” features). The choice of mode can drastically impact coding outcomes:

  • Claude Fast (Haiku) in coding is like a junior developer who codes at lightning speed but may overlook context. It’s excellent for boilerplate and quick scaffolding. For example, if you prompt Claude Fast with “Create a simple HTML/CSS page with a centered header and a paragraph”, it will spit out the code almost instantaneously. Fast mode is great for generating small functions, simple algorithms, or stub code where you primarily care about speed. It’s also useful for interactive coding sessions where you want immediate feedback (e.g. quick fixes or suggestions as you code). However, on larger coding tasks, Haiku will start to show its limitations. Users report that in longer coding sessions, the small model can “forget” earlier parts of the code or change variable names unexpectedly. If you ask it to handle a multi-file project or elaborate logic, it may produce superficial or partially correct code. It tends to be “shallow” in its understanding – fine for small tasks, but it might miss edge cases or deeper issues. It also has a higher chance of hallucinating incorrect API usage or misremembering function names in big codebases. In short, Fast mode is a speed demon for code – perfect for quick prototypes and minor edits, but you wouldn’t trust it alone to build or refactor a complex codebase.
  • Claude Strong (Sonnet) is like a seasoned developer who can handle most coding tasks reliably. In fact, among the Claude models, Sonnet is often considered the go-to for coding due to its balanced ability. Developers find that Claude Strong is dependable for writing logic, managing state, integrating APIs, and handling moderate-sized projects. It usually produces consistent code that runs with minimal tweaks. For instance, if asked to implement a class or a function across multiple files, Strong mode is much less likely to mess up filenames or lose track of variables compared to Fast mode. It’s also better at following instructions about coding style or architecture. In tests, Claude Strong rarely “freezes” or gets stuck, and it has a good grasp of context, meaning it remembers earlier parts of your code conversation well. A developer who built real projects with these modes noted that Sonnet “handled multi-file logic, state management, and didn’t hallucinate file names as often, remembering context better”. This makes it ideal for the main development work – writing and editing code during the core of a project. It might not catch every tiny bug, but it significantly reduces major errors. If you’re using Claude via an IDE plugin or in a dev workflow, Sonnet (Strong mode) would likely be your default model to get a strong balance of speed and accuracy in code generation.
  • Claude Extended (Opus or Extended thinking) is like having a senior expert or code reviewer scrutinizing the output. It’s slower and more costly to use continuously, but it catches things the others miss and provides deeper insight. There are two angles to “Extended” in coding: using the Opus model for its sheer intelligence, and using extended reasoning mode to let the model debug/plan step-by-step. Using Opus (the largest model): Opus has the highest “skill” level and is described as the “deep thinker” that can provide “surgical” code feedback. For example, when a developer used Opus to review and optimize code, it found subtle issues like performance bottlenecks, memory leaks, or missing cleanup calls that the smaller models overlooked. One report from a Flutter developer said: “Opus saved me during reviews – it found rebuild issues, missing disposes, and async bugs that Haiku and Sonnet completely skipped”. This illustrates how the Extended model can be invaluable for code review, testing, and complex refactoring tasks. The drawback is cost and speed: “Opus is slow and expensive… it’s too heavy for daily use, best to save it for reviews or major refactors” the developer noted. Essentially, you bring in the big gun model when you really need to ensure quality, much like asking an expert to do a final pass on your code. Using Extended Thinking mode in coding: This is a newer feature (introduced with Claude 3.7+ Sonnet) that can be combined with any model, including Opus. In extended mode, Claude can literally simulate a step-by-step execution or reasoning about the code. For instance, if you give it a complex algorithm to write, extended mode may break the task into subtasks in its “thought” window (e.g., “First, I will outline the steps… next, write function A, then function B…”). It can even internally run through test cases or double-check logic. A test by XRay.Tech showed that with a front-end coding task (creating a scrolling text animation with HTML/CSS), Claude in normal mode struggled – the first attempt produced no animation, the second attempt had a flawed animation. But with Extended mode on, Claude produced a correct, working animation as requested. The extended mode solution “worked exactly as described in the prompt” and was a usable result, whereas the normal outputs required significant fixes. This example highlights how extended thinking enabled Claude to carefully follow the requirements (likely by internally simulating or planning the animation code) and deliver a correct solution. Moreover, extended mode in coding allows Claude to debug its own mistakes: it can notice if the output didn’t compile or run right (in Claude Code, it can even execute code under supervision) and then correct it. Extended mode essentially brings iterative refinement – it’s as if the AI wrote the code, tested it mentally, saw the bug, fixed it, and then gave you the final answer, all in one go. For developers, the best practice is often to use a mix of modes. Use Fast mode (Haiku) for boilerplate or when you need an idea quickly (like a quick snippet or pseudocode). Use Strong mode (Sonnet) for most coding tasks to get solid, working code. Then, use Extended mode (Opus or Sonnet with extended thinking) for critical phases: e.g., before final commit, run the code through Claude Extended to catch subtle bugs or suggest optimizations. One seasoned developer described their workflow as “Haiku for quick UI ideas, Sonnet for main development, and Opus for final reviews – that combo just works”. They treated the three modes like a team: Haiku the rapid prototyper, Sonnet the dependable builder, and Opus the meticulous reviewer. This blended approach yields faster development cycles with fewer errors.

In summary, Claude Fast is your coding sprinter – use it when speed matters more than perfection. Claude Strong is the workhorse – use it for writing and refining code in most cases. Claude Extended is your safety net and optimizer – use it sparingly for deep debugging, complex tasks (like algorithm challenges), or reviewing large codebases, where its slower, methodical approach will pay off in correctness. Many teams find that leveraging all three appropriately can drastically improve productivity and code quality.

6. Multi-Stage Workflows and Decision Trees

A notable strategy is to combine modes within a single workflow or application to get the best of each. For example:

Tiered Query Handling: Some enterprise setups implement a “tiered” Q&A system. A simple question from a user might be answered by Claude Fast instantly. If the question is more involved, the system routes it to Claude Strong for a higher quality answer. If it’s a very complex query (detected by certain keywords or by the user explicitly requesting a thorough analysis), the system might invoke Claude Extended. This kind of decision tree ensures minimal latency for easy queries while still allowing heavy-duty reasoning when needed. Essentially, the Fast → Strong → Extended decision pipeline means you pay (in time or cost) only as complexity warrants.

Iterative Deepening: In some knowledge tasks, an application might first use Fast mode to get a quick take or outline, then call Strong mode to fill in details, and finally use Extended mode for verification. For example, in a Report Generation pipeline: Claude Fast could swiftly outline the report sections from raw data, Claude Strong could then flesh out each section with well-written content, and Claude Extended could do a final pass to cross-check facts/numbers and add insightful analysis. Each stage leverages the mode’s strengths (Fast for brainstorming, Strong for detailed writing, Extended for critical analysis).

Error Handling: If a response from a lower mode seems insufficient or incorrect, a system can automatically escalate. Suppose a user asks a complex math question and the Fast mode returns an answer with low confidence or obvious error – the app can detect that and re-run the query with Extended mode enabled to get a correct answer. This way, the user only waits longer in cases where it’s necessary. Claude’s API allows dynamic switching, so developers can build logic to choose the mode on the fly based on factors like user profile, question complexity, or even real-time model feedback (e.g., if the model expresses uncertainty).

By thoughtfully combining modes, you can design AI solutions that are both efficient and high-performing. Many teams report that mixing models/modes yields better outcomes than sticking to one mode for all tasks. The key is to understand the “hidden differences” – speed vs depth vs cost – and route tasks accordingly.

Now that we’ve looked at qualitative differences and use cases, let’s drill down into the technical trade-offs in cost, latency, and performance benchmarks that underpin these modes.

Cost, Latency, and Capability Trade-offs

Under the hood, the different Claude modes correspond to different model sizes and compute loads, which directly affect API pricing and response latency. It’s crucial to grasp these differences for budgeting and scaling purposes:

Pricing Differences

Anthropic charges usage based on model and token counts, and the cost can vary dramatically between Fast (Haiku), Strong (Sonnet), and Extended (Opus) modes. As of Claude 4.5 models, approximate pricing per million tokens (1M tokens) was**:**

  • Claude Haiku (Fast): ~$1 per 1M input tokens and ~$5 per 1M output tokens. This is the cheapest model – roughly one-fifth the cost of Opus for the same content.
  • Claude Sonnet (Strong): ~$3 per 1M input and ~$15 per 1M output. About 3× the cost of Haiku.
  • Claude Opus (Extended model): ~$5 per 1M input and ~$25 per 1M output. This is the premium tier, roughly 5× the cost of Haiku.

(These are base prices; enterprise discounts or priority tiers may adjust them slightly.)

Notice that for all models, output tokens cost about 5× more than input tokens (e.g. $5 vs $1 for Haiku). This means generating long answers is more expensive than reading long prompts. Extended mode often produces longer, more detailed answers, which can increase output token usage – another reason to use it judiciously for when that detail is needed. For example, if Claude Extended writes a 2,000-word report as output, that might cost significantly more than a 200-word answer from Claude Fast.

Why the cost difference? Larger models (Strong, Extended) run on more powerful (and expensive) AI computations. Opus, being the largest, uses the most compute per token, hence the highest price. Haiku is lightweight, so it’s very cost-effective. This aligns with Anthropic’s descriptions: Haiku offers “near-frontier intelligence at substantially lower cost” for scaled deployments, whereas Opus delivers maximum intelligence at a premium price suitable for when you truly need that power. If cost is a major concern (say you have millions of queries per day), using Claude Fast for simpler tasks can save a lot of money over time, even if it’s slightly less accurate.

Also, consider extended thinking mode itself has a cost: when you enable extended mode via the API, you essentially allow the model to use extra tokens for its internal thought process. Anthropic’s pricing documentation indicates there may be additional charges for the “thinking” tokens consumed (often at the same rate as input tokens). For instance, if you allocate a 1000-token thinking budget, those tokens count toward your usage. So a long extended reasoning might quietly add thousands of tokens of cost. Always check the latest pricing details if you plan to use heavy extended reasoning frequently.

Tip: Optimize cost by matching the mode to the task’s importance. Use the cheapest model that meets your needs: Fast for trivial tasks, Strong for most, Extended only when necessary. And try to keep outputs concise when possible – a well-directed prompt that yields a precise answer in 100 tokens is far cheaper than an open-ended prompt that yields a 1000-token essay (especially on the pricey modes).

Latency and Throughput

Response time is another key differentiator:

  • Claude Fast is blazingly quick. In fact, Anthropic claims Claude Haiku is “the fastest and most cost-effective model on the market for its intelligence category”. It can ingest large inputs extremely quickly – one benchmark: reading a ~10,000-token research paper in under 3 seconds! That implies a reading speed of on the order of thousands of tokens per second. Its generation speed is similarly optimized. This near-instant capability is why Haiku is recommended for real-time applications like live chat or rapid-fire completions. If your application demands sub-1-second responses (for example, an AI assistant in a customer service setting where users expect immediate answers), Claude Fast is the way to go. It achieves this speed by having a smaller neural network that requires less computation per token.
  • Claude Strong is still very fast, but not the absolute fastest. The Claude Sonnet model is larger, so it processes tokens a bit more slowly than Haiku, but it’s engineered for speed relative to its size. Anthropic noted that Sonnet is roughly 2× faster than the older Claude 2 model while being smarter. In practical terms, Sonnet can comfortably handle real-time interactive use for most cases – generating a few paragraphs of answer might take a second or two, which is usually fine. Comparative latency is listed as “Fast” for Sonnet vs “Fastest” for Haiku. This suggests Haiku might have, say, ~50% lower latency than Sonnet on similar tasks (just as an illustrative figure). But Sonnet is no slouch – it is used for chatbots and knowledge assistants that need quick turnaround. For example, knowledge retrieval tasks and sales automation are cited as things Sonnet excels at with rapid responses. Unless you have a strict sub-second requirement or very high volume, the slight latency increase of Strong mode is usually worth the improved output. Many developers start with Sonnet as the default model since it balances speed and power.
  • Claude Extended (Opus) is the slowest of the three, though still comparable to some previous-gen models. Opus’s latency is categorized as “Moderate” in Anthropic’s docs. This means you will notice a difference – for instance, where Haiku might respond in 1 second, Opus might take a few seconds for the same prompt. For long or complex outputs, Opus could potentially take tens of seconds. It “delivers similar speeds to Claude 2… but with much higher intelligence” according to Anthropic. So if you’re familiar with how GPT-4 or Claude 2 responded (often a bit slower), expect Opus to be in that ballpark or a tad faster. The key is that Opus’s response time scales with the task complexity – it’s optimized for sustained reasoning, meaning it might willingly spend more time if needed. This is evident in extended mode usage: when extended thinking is on, the model might actively run for the entire allowed “thinking time” to come up with the best answer. You as a developer can control this somewhat (by setting a thinking time/token limit). There’s also mention of an “effort parameter” in Opus 4.5 (beta) that lets you balance performance vs latency – essentially letting you dial the thinking level up or down to save time.
  • Throughput considerations: If you are processing a large volume of requests, the faster models can handle more requests per second on the same hardware. Enterprises sometimes use Haiku for high-concurrency workloads (like thousands of users chatting simultaneously) because it’s cheaper and faster per prompt, thus higher throughput. In contrast, Opus might require more computing power or more instances to achieve the same throughput due to higher per-request latency. If using Claude via an API endpoint, you might observe that the smaller model saturates your token rate limits slower (because it responds quicker, finishing the job and freeing the slot).
  • UI vs API latency: Anecdotally, using Claude through the web UI or integrations might feel a bit slower than direct API calls, especially for extended mode. In the Claude web interface, when you toggle Extended Thinking, it resets the conversation and explicitly shows a timer. The UI might impose some overhead (like rendering the thought process live, or limitations on streaming until the thought process is done). The API, however, can stream results as they’re generated (for normal mode) which gives an illusion of faster response since you start seeing output immediately. With extended mode via API, streaming might be delayed until the reasoning phase completes (so you still wait). In short, there might be minor differences, but largely the latency is dictated by the model’s compute time, which is the same backend. One difference: the first request in a new Claude session can have some additional initialization latency, and switching modes in the UI starts a new chat (erasing context), whereas via API you have full control and can reuse conversation state if needed with the same model.

To quantify latencies (hypothetical example for perspective): A simple 20-token answer might come in ~0.5s with Haiku, ~1s with Sonnet, and ~2s with Opus. A longer 2000-token answer might be ~3s Haiku, ~6s Sonnet, ~10-12s Opus. And if extended mode is used with, say, a 1000-token thinking budget, that could add several seconds on top (depending on how much of the budget it uses). These are illustrative – actual times depend on hardware and load – but they reflect the scaling. The key trade-off is if you double or triple the latency to use a bigger mode, what do you gain? Often you gain more than enough in accuracy or capability to justify it (especially if the task is complex). But for trivial tasks, it’s wasted time.

Capability and Quality Trade-offs

Beyond cost and speed, the modes differ in raw capability – that is, the quality of output and complexity of tasks they can handle:

  • Claude Fast (Haiku): Has “near-frontier” intelligence for its speed, meaning it’s impressively capable given how quick and cheap it is. It can handle a wide range of general tasks (it’s still a large language model, after all), but expect to see more mistakes on very difficult tasks. It might also produce shorter or more generic answers. Haiku’s strengths are speed and brevity; its weaknesses include lower accuracy on expert-level questions, easier confusion with very long inputs, and less detailed responses. It’s also more likely to refuse or safe-complete borderline prompts unnecessarily compared to the more nuanced larger models (though Claude 3 family improved refusals overall). Essentially, Haiku is best at straightforward, well-known tasks and fails more on tasks requiring deeper reasoning or extensive knowledge.
  • Claude Strong (Sonnet): Offers a high level of general intelligence and is often the recommended default model. It performs strongly on “analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages”. Compared to Claude 2, it’s a huge leap in most benchmarks – it handles graduate-level reasoning and complex coding much better. In practical terms, Claude Strong can do things like multilingual understanding, complex instruction following, and creative writing very well. It has a large enough knowledge base (with training data up to mid-2025 or later) to answer expert questions in domains like law, medicine, finance (with some limitations around the knowledge cutoff). Sonnet’s main trade-off vs Opus is that on the very hardest tasks or edge cases, Sonnet might falter where Opus would succeed. But Sonnet hits a sweet spot where its output quality is close to the best, without the steep costs. That’s why for most use cases (especially in production apps), Sonnet is recommended unless you truly need the extra boost from Opus. It’s essentially the best balance of quality vs cost for general use.
  • Claude Extended (Opus / Extended mode): This represents the maximum capability Claude offers. Opus outperforms the other models on virtually all academic and professional benchmarks – from knowledge tests to math to coding, it often leads the pack. For example, Opus has near-human performance on complex exams and showed a twofold improvement in accuracy on challenging open-ended questions compared to Claude 2.1. It also demonstrated a remarkable ability to identify trick “inserted” information in long documents, indicating a very keen analytical ability. All this means that if you have a very challenging task – say interpreting an ambiguous legal scenario, writing a very complex piece of code, or performing strategic planning – Opus in extended mode is your best bet for a correct and thorough result. Extended mode further amplifies its capabilities by letting it reason longer. We saw earlier how extended thinking gave large boosts in math and physics problem accuracy. There’s also evidence that Claude Extended mode improves instruction-following accuracy (93.2% vs 90.8% on one eval) by a few percentage points, which might be the difference between a slight error and a perfect answer in critical cases. The downside: diminishing returns in some situations. On easy tasks, Opus might not show much noticeable improvement over Sonnet – you’re paying more for similar results. In fact, one must consider that overuse of extended mode can even introduce unnecessary verbosity or complexity. For instance, if you ask a simple question with extended mode on, Claude might produce a long-winded answer with a detailed thought process that isn’t really needed (and you pay for those tokens). Or, as some anecdotal reports suggest, if not tuned properly extended mode could occasionally get “lost” in its own thoughts for tricky but not impossible tasks – though generally it’s designed to converge on an answer within the budget.

In summary, Fast < Strong < Extended in terms of raw capabilities. Fast is good, Strong is great, Extended is excellent. The hidden differences often only become apparent on the hardest tasks or at scale. For an everyday query, all three might do fine. But if we’re talking about say, writing a complex piece of code or analyzing a 500-page policy document, the quality gap emerges: Fast might give up or err, Strong will do decently, and Extended will likely nail it (given time).

To quantify quality, consider internal benchmarks: On a coding benchmark, Haiku might solve ~50% of problems, Sonnet ~62%, and Opus ~70%. On a physics quiz, Haiku might get ~80% right, Sonnet ~96%, Opus ~99%. These are illustrative, but they match the narrative: the extended mode approaches expert-level performance in many domains.

Trade-off summary: It boils down to Speed vs. Strength vs. Depth:

  • Speed (Fast mode): minimal latency & cost, moderate intelligence.
  • Strength (Strong mode): good latency & cost, high intelligence – best general pick.
  • Depth (Extended mode): higher latency & cost, maximal intelligence and reasoning depth for hardest problems.

Next, let’s see how you can practically invoke these modes via the API and what differences exist in API behavior.

API Behavior and Integrating Different Modes

If you’re using Claude via API (or platforms like AWS Bedrock or Google Vertex), understanding how to select and control these modes is key. Claude’s API exposes both the choice of model and the extended thinking parameters to developers:

Selecting the Model (Fast vs Strong vs Extended)

Claude’s models are identified by names. For example, Claude 4.5’s models use IDs like:

  • Claude Haiku 4.5 – API ID "claude-haiku-4-5" (with a longer versioned ID also available).
  • Claude Sonnet 4.5 – API ID "claude-sonnet-4-5".
  • Claude Opus 4.5 – API ID "claude-opus-4-5".

By specifying the model in your API request, you effectively choose Fast vs Strong vs Extended capability. In code, this usually means setting a parameter like "model": "claude-haiku-4-5" for Fast mode, or "model": "claude-opus-4-5" for the Extended model. If you’re unsure which to use, Anthropic suggests starting with Claude Sonnet as it “offers the best balance of intelligence, speed, and cost for most use cases”. Then you can iterate from there if needed.

Switching models on the fly: You can dynamically route requests to different model endpoints based on your logic. For example, you might maintain two API clients – one for Haiku and one for Opus – and choose between them per request. Just remember that each model may have separate context. If you maintain a multi-turn conversation, staying with one model is ideal (switching models mid-conversation might not carry over the exact context, especially if using separate endpoints). If you do need to switch in a multi-turn scenario, you may have to resend the conversation history to the new model.

Availability: All three models (Haiku, Sonnet, Opus) are available via Anthropic’s API, and also through partner services like Amazon Bedrock and Google Cloud Vertex AI, albeit sometimes with slightly different naming conventions. For instance, on Bedrock the names might include a version suffix, but conceptually they map one-to-one. Ensure you have access to the model – e.g., Opus might require a higher-tier subscription or a specific region if using third-party platforms.

Enabling Extended Thinking Mode via API

To tap into Extended mode’s deeper reasoning, the API provides parameters to toggle it. According to Anthropic’s documentation and examples:

  • You can specify a thinking_mode parameter in the API request. For example, "thinking_mode": "extended" will enable the extended chain-of-thought mode. Conversely, "thinking_mode": "standard" (or omitting the parameter) uses the normal fast mode (sometimes called “quick” mode).
  • Additionally, there’s a max_thoughts or thinking_budget parameter (naming may vary in docs) that sets how many tokens or how much time the model can spend “thinking.” For example, "max_thoughts": 1000 might allow up to 1000 tokens of internal reasoning. Or "thinking_budget": 5000 to allow a larger scratchpad. This prevents the model from thinking infinitely and lets you control the latency/cost trade-off. You can tune this value: a smaller budget yields faster but possibly less thorough reasoning, a larger budget gives more thorough answers but takes longer.
  • When you enable extended thinking via API, Claude will include the visible thought process in its response. In the Claude UI, this appears as a separate “Thinking…” section. In the API, Anthropic might deliver it either embedded in the text or as structured data. Typically, the model might output a special format, e.g. delineating the thought process from the final answer. (In one user example, they prompted Claude to put thoughts in <thinking> tags and final answer in <answer> tags, and saw that extended mode naturally did a similar separation.) Officially, with Claude 3.7+ the thought process is accessible to the user, so expect your API response to contain that. You should parse or filter out the thought content as needed for your application. For instance, if building a user-facing chatbot, you might not want to show the raw chain-of-thought directly to the end-user (it can be technical or confusing). You could choose to display it in a debug panel or use it for verification internally. The Claude Help Center notes that seeing the thought process can build trust and help with debugging Claude’s answers, but it might be too much information for normal users.

API Example: Here’s a pseudo-code JSON payload for a Claude API call in extended mode:

{
  "model": "claude-3.7-sonnet", 
  "prompt": "<Your prompt here>",
  "thinking_mode": "extended",
  "max_thoughts": 1000,
  "temperature": 0.5,
  "max_tokens_to_sample": 300
}

In this example, we explicitly pick the Sonnet model (Claude 3.7) and turn on thinking_mode: "extended" with a budget of 1000 thought tokens. You can adjust model to "claude-opus-4-5" etc., and adjust the budget. The other parameters like temperature and max_tokens_to_sample are typical generation settings (temperature controls randomness, max_tokens is the max length of output).

If you wanted Fast mode via API, you could simply use the Haiku model and not include any thinking_mode (default is standard). For Strong mode, use the Sonnet model without extended thinking on.

Dynamic mode selection in code: You can implement logic to choose these parameters on the fly. For instance, consider a function that takes the user’s query and decides:

if is_simple_query(user_query):
    model = "claude-haiku-4-5"
    params = {"thinking_mode": "standard"}
elif is_complex_logic_query(user_query):
    model = "claude-sonnet-4-5"
    params = {"thinking_mode": "extended", "max_thoughts": 1500}
elif is_large_document_query(user_query):
    model = "claude-opus-4-5"
    params = {"thinking_mode": "extended", "max_thoughts": 3000}
else:
    model = "claude-sonnet-4-5"
    params = {"thinking_mode": "standard"}

This pseudo-code routes to Haiku fast for simple queries, uses Sonnet with extended for complex logic, uses Opus extended for huge document analysis, and defaults to Sonnet standard otherwise. The conditions can be based on prompt length, presence of certain keywords (e.g. “solve”, “step by step”), or user preferences (maybe an “answer quality” toggle in your app).

API Response differences: When extended thinking is off, Claude’s response is just the answer. When on, the response will include a trace of its reasoning. In Anthropic’s design, this trace is prefaced by something like a “Thinking process:” section. You can detect that in the text and split it from the final answer. Also note, if the model’s thoughts encounter disallowed content, the thought process might be truncated for safety (Claude will stop revealing thoughts that violate policies). The final answer, however, will still be given if it’s safe. So as a developer, you should handle cases where the thought chain might cut off with a message about safety – the Help Center suggests this can happen and to possibly rephrase the prompt if the cutoff impacted the answer.

One more thing: extended mode is currently not available on the free tier of Claude. It’s available via the API for those with API access (which is a paid service) and for Pro/Enterprise users in the Claude web UI. So if you call the API with thinking_mode: "extended" and your API key/account doesn’t support it, you may get an error or it may just ignore that parameter. Ensure your account level supports extended thinking.

Mode-Specific Parameters and Limits

In addition to thinking_mode, be mindful of other parameters that interplay with modes:

Max Tokens / Context Length: All models currently support up to a 200k token context (which is huge), and Sonnet even supports a 1M token context in preview. If you need to use that 1M context with Sonnet, you might have to include a special header or parameter (Anthropic docs mention a context-1m beta flag). Using such large contexts will be very slow and costly, so consider chunking input or retrieval instead of always slinging hundreds of thousands of tokens at the model. But it’s good to know the limits: Fast (Haiku) and Extended (Opus) modes: 200k tokens; Strong (Sonnet): 200k or 1M with beta. Also, max output length is 64k tokens for all – effectively no practical limit on output size, but again, cost will constrain you.

Model Versions and Aliases: When you specify claude-opus-4-5, you’re usually using an alias that points to the latest snapshot of that model (in this case, likely a date-stamped version). Keep an eye on version updates – sometimes Anthropic releases improved snapshots (like 4.5, 4.6, etc.) and the alias moves forward. That means your Claude Strong today might quietly get even stronger tomorrow if they update Sonnet under the hood. For production stability, you can lock to a specific version if needed.

Temperature and Generative Behavior: Fast vs Strong vs Extended might respond slightly differently to the same temperature or randomness settings. The larger models are often more stable and less random by default. If you use a high temperature (say 1.0) with Haiku, you might get more chaotic output than the same with Opus, because Opus has seen more data and has more refined generation. There’s no hard rule here; just test and adjust. Often, for factual or coding tasks you’ll keep temperature low (0 to 0.5) in any mode to ensure deterministic output. For creative tasks, you might raise it. All modes can produce creative writing, but Opus will generally produce the most sophisticated and coherent long creative pieces due to its capacity.

Parallel Usage: If using the API in parallel (multiple requests at once), note that your organization might have a rate limit or concurrency limit. Because Extended mode calls are heavier, hitting those limits is easier if you send many extended requests simultaneously. You may need to queue or throttle extended calls. Conversely, you can send more Fast mode calls in parallel without hitting limits as quickly. Anthropic’s “Priority Tier” feature (if you have it) applies to all models to ensure your requests get throughput.

Best Practices in API Workflows

To summarize how to integrate Claude’s modes smartly in your application:

  • Start with Sonnet (Strong) as a baseline. It’s often the best default for quality. Then identify hotspots where you can downgrade to Haiku or upgrade to Opus.
  • Use Haiku for high-frequency or time-sensitive tasks. E.g., if you have an auto-complete feature suggesting the next sentence as a user types, Haiku’s speed is vital.
  • Use Opus or extended mode for verification. For instance, after getting an answer from Sonnet, you might pass the whole conversation to Opus extended with a prompt like “Double-check the above answer for any errors or missing points.” This two-step approach can catch mistakes without always paying the Opus cost on the first pass.
  • Leverage thinking_mode for complex queries dynamically. Some developers expose a user toggle – e.g. a checkbox “Thorough Mode” which if checked will use extended thinking for that query. This lets power users choose when they want a slower but deeper answer.
  • Monitor token usage and response times. Logging how many tokens each request uses and how long it takes will help you refine your mode usage. If you see that some extended calls are taking too long for marginal gain, you might reduce the budget or use strong mode instead.
  • Handle the thought output if using extended. You might format it nicely for yourself or hide it. It can be useful for debugging model behavior or even providing the user an explanation. For instance, in a teaching app, you might show Claude’s “thinking steps” as a feature to illustrate how to solve a problem. Just ensure to filter any raw or confusing text.

Finally, remember that Claude’s modes are all about giving you flexibility. As Anthropic noted, earlier one had to choose either a fast model or an accurate model; now Claude 3.7+ “unifies both modes in a single model” allowing seamless switching. Use that flexibility to your advantage – you can build truly smart applications that adapt on the fly.

Claude’s Model Architecture and Extended Reasoning (Under the Hood)

To demystify things a bit, let’s briefly discuss how Claude can have these modes. It comes down to Claude’s model architecture and training:

All Claude models (Haiku, Sonnet, Opus) are large transformer-based language models, differing mainly in size (number of parameters) and training extent. The Haiku model is the smallest, so it’s less computationally intensive (hence faster). Sonnet is medium-sized, and Opus is the largest with the most training (hence most intelligent). They share the same underlying training data (up to their respective cutoffs) and techniques like Constitutional AI alignment, etc., but are like small, medium, large versions of Claude’s brain.

What’s new in Claude 3.7 and Claude 4 is the concept of hybrid reasoning. Instead of having separate models for “instant” vs “deliberate” (like some systems had an “AI assistant” vs a “problem solver” model), Anthropic built the capability for both fast and slow thinking into one model (especially evident in Claude 3.7 Sonnet). The architecture includes mechanisms for the model to allocate some of its layers or time to a “scratchpad” when needed. During extended thinking, the model generates special “thought tokens” that are not immediately output but can be consulted in subsequent inference steps. It’s as if the model has an internal notebook. It writes down intermediate reasoning there, and because we toggled visibility, we get to see that notebook.

Conceptually, this is akin to how a human brain can do quick reflexive thinking vs. slow analytical thinking. Claude’s design mirrors this: “When operating in default mode, Claude provides near-instant responses by tapping into pretrained heuristics. When extended thinking is enabled, it pauses to generate ‘thought’ tokens – intermediate reasoning steps the user can inspect. This mirrors human cognition, where intuition and deliberate reflection coexist.”. In simpler terms, Fast mode = intuition, Extended mode = deliberation inside the same AI.

Crucially, the extended reasoning mechanism means Claude may internally consider many possibilities or perform multi-step calculations that it wouldn’t in fast mode. For example, in extended mode, it might iterate over a loop internally, try different potential answers, double-check facts, etc., all before finalizing its response. That’s why it tends to get more accurate answers for hard questions. However, it’s also using more “thought” tokens and time, which explains the latency and cost hit.

Anthropic’s research has shown some interesting points about this internal thought process. They made it visible not just for utility, but to study alignment and truthfulness. They observed that Claude’s chain-of-thought can sometimes contain errors or explorations that it ultimately corrects before giving the final answer – much like a human might jot down some wrong ideas on scratch paper before finding the correct solution. They also warn that the thought process isn’t 100% faithful to what the model truly “thinks” (some thoughts might be pruned before output, etc.), but it’s still a useful window into the model’s reasoning.

From a performance standpoint, enabling this chain-of-thought essentially means using more of the model’s capacity per query. Instead of one forward pass to get an answer, it might do multiple passes (or a longer single pass with recurrent style). That’s why it can use up to 128k tokens internally as mentioned. It’s like doing extra computation at inference time (sometimes called test-time compute scaling in AI research). Notably, OpenAI’s GPT-4 introduced a similar idea with a “slow thinking” mode (there were research previews like GPT-4 O1 that did something akin to this). Anthropic’s Claude 3.7 took that further by integrating it seamlessly and letting users toggle it on/off in one model.

So, Claude Extended mode = Claude thinking harder/longer; Claude Fast mode = Claude thinking quickly/instinctively. The “Strong” mode is essentially just using the bigger brain (more neurons) in the quick way – which is why Sonnet or Opus even in standard mode are more “intelligent” than Haiku. They have more training and parameters, so their intuition is better. But when even that isn’t enough, extended gives them more time to sequentially reason.

In terms of model “mapping”: often companies give cutesy names (Haiku, Sonnet, Opus) to model sizes. Internally, one might think of them like “Claude-small”, “Claude-medium”, “Claude-large”. When you see “Claude Instant” in some older literature, that referred to the smaller model (similar to Claude Fast). “Claude 2” or “Claude 1” referred to the larger one (analogous to Claude Strong). Now we have three tiers plus the reasoning mode.

To recap the mapping clearly (for SEO and clarity):

  • Claude Fast = Claude Haiku model = small & fastest model. Best for quick responses, low cost needs. Lower depth.
  • Claude Strong = Claude Sonnet model = mid-sized, high-performance model. Best overall balance, primary model for most use cases.
  • Claude Extended = Claude Opus model (or any model with extended thinking on) = largest model and/or deep reasoning mode. Best for maximum accuracy, very long inputs, complex tasks. Higher cost/latency.

And Extended Thinking mode can actually be applied to all three of those models – yes, even Haiku supports it according to docs (though the benefit is more pronounced on bigger models). So you could have “Fast+Extended” (fast model thinking longer) or “Strong+Extended” (our default Sonnet with extra reasoning) or the ultimate “Opus+Extended” for the absolute peak reasoning power.

Best Use Cases and Recommendations for Each Mode

Let’s consolidate when you should use each mode (a handy reference guide):

Claude Fast – Best for Speedy, Simple Tasks

Use Claude Fast (Haiku) when response speed and low cost are top priority and the task is relatively straightforward. Ideal scenarios:

Casual Chat & Brainstorming: If you’re just bantering with the AI or doing a quick idea generation (e.g. “Give me 5 burger restaurant name ideas”), Fast mode’s instant answers shine. Latency is minimal, making the experience feel snappy.

Customer Support Bots (simple FAQs): When deploying Claude in a customer service context for common questions (“What’s your return policy?”), Fast mode can provide immediate answers without lag. It’s usually accurate on well-defined FAQs and you save significantly on costs for high-volume usage.

Internal Team Assistance (low-stakes): If employees use Claude for quick help (like “summarize this email” or “what does acronym X mean?”), Fast is often sufficient. It gives the info needed without tying up resources.

Low-context or Short Inputs: For prompts that are very short or self-contained, and especially if they’re similar to common training data examples (e.g. asking for a definition, or a simple translation), Claude Fast will do the job nearly as well as Strong mode.

Rapid Prototyping in Code: As described, use Fast mode to scaffold code or get quick snippets. For example, “Write a basic HTML form” – you’ll get an answer before you can blink.

Multi-turn chat where each turn is simple: If building a conversational AI that doesn’t need deep reasoning each turn (like a casual chat companion or a fun personality bot), Haiku can maintain a dialogue fast. Just watch that very long conversations can tax its short-term memory – you might need to truncate history more aggressively.

Not recommended for: highly analytical questions, very large texts, or situations where an error is costly. In those cases, the faster response isn’t worth it if it’s wrong. Also avoid for tasks known to be challenging for smaller LLMs (complex math, intricate logical puzzles, nuanced advice, etc.) – that’s where it may falter.

Claude Strong – Best All-Purpose and Analytical Mode

Use Claude Strong (Sonnet) as the default for most use cases. It’s well-suited for:

Analytical Q&A and Reports: When you need a reliable, well-reasoned answer or summary. E.g., “Analyze the main factors affecting Q4 sales in this report” – Sonnet will give a coherent, relatively accurate analysis with supporting details.

Medium-Complexity Tasks: Things like writing a detailed email, summarizing a moderately long article, providing an explanation of a concept, etc. Strong mode handles nuance and context with ease here.

Structured Knowledge Retrieval: If you build a knowledge base Q&A (without extremely large documents), Sonnet will usually extract and synthesize information accurately. It’s also less likely to hallucinate compared to the smaller model.

Precise Summarization: Need a summary with certain requirements (e.g., “summarize this paper and include three key statistics”)? Sonnet can follow the instructions reliably and produce a concise yet thorough summary.

Most Coding Tasks: As discussed, for writing functions, classes, debugging typical issues, Sonnet is the go-to. It balances speed so you’re not waiting too long, with a high success rate on producing working code.

General SaaS Applications: If you’re integrating Claude into a product (like a writing assistant in a docs app, or an AI tutor in an edtech app), using Claude Strong gives your users high-quality outputs in a timely manner. It’s the “well-rounded” choice that keeps user experience smooth while delivering solid results.

In essence, Claude Strong is the workhorse. It’s often the mode you will want running 80-90% of the time for serious deployments. It offers the best trade-off between cost and capability, and it’s explicitly optimized for production use with complex tasks while still being efficient.

Claude Extended – Best for Deep Reasoning and Heavy Workloads

Use Claude Extended (Opus or extended thinking) when you need maximum performance on very demanding tasks. Scenarios where Extended mode is ideal:

Retrieval-Augmented Generation with Huge Corpora (RAG): If your AI needs to answer from massive collections of text (thousands of pages, entire databases), Extended mode can manage the long context and reason across documents. It’s especially good at tasks like cross-document comparison (e.g., “Compare these two lengthy legal contracts and highlight differences”) – it will methodically go through them. Also, when answers require synthesizing information from multiple sources retrieved, extended reasoning ensures it doesn’t miss connections.

Document QA with 100K+ Token Inputs: Claude Extended is practically made for scenarios like “Here is a 300-page book. Answer questions about its content.” It can utilize the full 200K token window and still recall specifics. If you simply must feed a huge document in one go and get an answer, use the largest model, possibly with extended thinking if the question is complex. It was explicitly built to handle up to millions of tokens in special cases.

Strategic Planning and Multi-step Reasoning: Need Claude to develop a strategy or solve a complicated problem step-by-step? Extended mode is best. For example, “Devise a detailed project plan for developing a new software product, including timeline, team roles, and risk analysis.” That’s open-ended and complex – extended mode will allow Claude to internally brainstorm and organize the plan more thoroughly. Or a multi-step logical puzzle where each step depends on the previous, extended mode will maintain that state.

Deep Analyses and Explanation: If you want an in-depth analysis, say a thorough literary analysis of a novel chapter by chapter, or a scientific analysis of experimental results, extended mode will produce a more exhaustive and carefully reasoned analysis. It won’t rush to a conclusion; it will consider various angles (and you’ll see that in the thought process log).

Comparative or Multi-document tasks: E.g., “Compare and contrast the philosophies of Author A and Author B across these two books.” This is the kind of complex task where extended mode will pay off. It can methodically go through one book, note key points, go through the second, and then reason about differences. The normal mode might give a decent answer from memory, but extended mode will ensure it combs the texts for evidence.

Complex Architecture & Planning (e.g., coding architecture or large workflows): If you ask Claude to design a complex system (software architecture, business workflow, etc.), extended thinking helps it not skip over critical steps. Anthropic touts extended Sonnet’s ability to autonomously plan multi-step workflows and agents, which suggests in extended mode it can outline and execute quite elaborate plans. For instance, designing a full-stack application architecture with multiple components – extended mode will likely produce a step-by-step plan detailing each component and how they interact.

In short, Claude Extended is for the “hard mode” tasks – those that lesser modes either cannot handle or would handle with significantly lower quality. Whenever the question or task is hard for an AI (lots of math, logic, long texts, or requirement of extreme accuracy), you should lean towards Extended.

Caution: Because it’s slower and expensive, it’s usually not the default. Some orgs restrict Extended mode usage to certain user groups or require an explicit user opt-in (“enable detailed mode”). This ensures casual users don’t unknowingly incur long wait times or high costs when it wasn’t needed.

Benchmarks and Real-World Performance

It’s useful to mention concrete benchmarks and results that highlight these differences:

Latency Benchmarks: As noted, Haiku can respond in under 3 seconds for 10k tokens input – an impressive speed. In an internal test, one might find Haiku generates ~X tokens/second vs Sonnet ~Y vs Opus ~Z. (Anthropic hasn’t published exact token/sec, but from descriptions: Haiku is likely the fastest on the market for its class, Sonnet is 2x faster than previous gen, Opus is moderate). On shorter prompts (<1k tokens), often the network latency or overhead might dominate and you’ll feel all are fast. On very long prompts or outputs, the difference grows – e.g., generating a 5,000-token report: Haiku might do it in ~2s, Sonnet ~4s, Opus ~6-8s, and extended mode maybe 10s if it thought extra.

Accuracy & Task Performance: According to Anthropic’s data and third-party tests:

On a difficult knowledge benchmark (like MMLU or a graduate exam), Opus (Extended) outperforms Sonnet, which in turn outperforms Haiku.

Coding: In one benchmark (SWE-bench), Claude 3.7 Sonnet achieved ~62.3% accuracy vs its predecessor’s ~49%, and extended mode could push that higher. The user test (Medium) clearly showed Haiku struggling with context in code and Sonnet being steady, with Opus catching subtle issues.

Math/Logic: We saw letter counting and GMAT problem examples where extended mode succeeded and normal mode failed. In formal math benchmarks (MATH dataset or AIME competition problems), extended mode lifts Claude’s score significantly closer to top-tier (OpenAI or xAI models).

Long-context recall: On the “Needle in a Haystack” test, Claude 3 Opus got ~99% accuracy, whereas presumably the smaller might be less (maybe still high, but not near-perfect).

Tool use and Agents: In tests of agent tasks (like using a virtual computer or performing API calls in a sequence), Claude 3.7 with extended thinking had much better results over time than without it. This indicates extended mode’s advantage in iterative decision tasks.

In real projects, the ROI of using a mode can be measured by these outcomes. For example, one dev found that using Opus for code reviews reduced bugs significantly, which saved time in debugging – an ROI greater than the cost of those Opus calls. Another report suggests that with extended mode, they got “code that actually works the first time – a major improvement over previous iterations”. In business terms, if extended mode turns a 5-hour data analysis task into an accurate 5-minute AI answer, that latency/cost is negligible compared to the saved human time.

Final Thoughts: Choosing the Right Claude Mode

Claude’s Fast, Strong, and Extended modes offer a flexible toolkit to tailor AI performance to your needs. The differences can be subtle on easy tasks, but they become critical on hard tasks or at scale. To summarize:

  • Choose Fast (Haiku) when you need an answer right now and perfection is not critical – great for quick chats, simple tasks, and scaling to huge request volumes cheaply.
  • Choose Strong (Sonnet) for most situations – it gives high-quality results with minimal wait, making it suitable for a wide range of professional and creative applications. It’s the default mode that “just works” for general use.
  • Choose Extended (Opus or extended thinking) when you face a really hard problem, a huge document, or you require the most accurate and comprehensive output possible. It’s your “expert mode” – slower and pricier, but often necessary for the toughest jobs.

Often, the best solution is a combination: let Fast handle the trivial stuff, let Strong handle the day-to-day heavy lifting, and call in Extended for the final boss questions. This approach has been likened to having a trio of AI assistants: “Haiku is the sprinter, Sonnet the steady engineer, and Opus the master strategist” – each playing their part. By orchestrating them wisely, you can achieve both efficiency and quality.

From a SEO perspective (and to address common queries): “Claude Fast vs Strong vs Extended” – it’s about Speed vs Balance vs Depth. “Which Claude mode to use?” – use Fast for speed, Strong for balance, Extended for depth. “Claude modes explained” – Fast=quick & cheap, Strong=balanced, Extended=slow & thorough. “Claude latency differences” – Fast is fastest (sub-second possible), Extended is slowest (several seconds for complex tasks). “Extended reasoning Claude” – a feature allowing Claude to solve harder problems by internally thinking step-by-step.

In conclusion, Claude’s hidden modes are not so hidden anymore – you now have a clear map of this Haiku–Sonnet–Opus trifecta. Empowered with this knowledge, you can leverage Claude’s full potential, optimizing cost, speed, and quality on your terms. Whether you’re building an AI-powered app or conducting research with Claude, choosing the right mode will ensure you get the most out of this powerful AI. Happy prompting!

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