Use Claude Haiku 5.5 when latency and throughput matter most, Claude Sonnet 5.5 for the usual balance of speed, quality, and price, Claude Opus 5.5 for difficult coding and agentic knowledge work, and Claude Fable 5.1 when a problem still demands the deepest reasoning and longest-horizon planning. All four list a 1-million-token context window and a 128,000-token maximum output, but they differ substantially in response speed, reasoning strength, and token cost. Choose with a measured evaluation rather than the model name alone, and verify the alias and retirement schedule before putting a system into production.
The short answer: which Claude model should you use?
Anthropic’s current overview says, “If you’re unsure which model to use, start with Claude Opus 5.5 for most workloads.” That is a sensible quality-first default. For many production teams, however, Sonnet 5.5 is the better starting point when interactive speed and recurring cost matter. Move down to Haiku 5.5 for high-volume, well-defined tasks; move up to Opus 5.5 or Fable 5.1 when mistakes, missed edge cases, or extended planning are more expensive than extra tokens and latency.
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| Model | Relative latency | Best fit | Input price per million tokens | Output price per million tokens |
|---|---|---|---|---|
| Claude Haiku 5.5 | Fastest | Classification, extraction, routing, and high-volume responses | From $0.10 | From $0.50 |
| Claude Sonnet 5.5 | Fast | General applications needing a strong speed–quality balance | $2 | $10 |
| Claude Opus 5.5 | Moderate | Long-running agentic coding and demanding knowledge work | $4 | $20 |
| Claude Fable 5.1 | Slower | Hardest reasoning problems and long-horizon agentic tasks | $10 | $50 |
The prices above are Anthropic’s 2026 list prices per million tokens. Haiku’s “from” price can increase for prompts longer than 100,000 tokens. Batch processing, prompt caching, tools, and regional endpoints can change the effective amount you pay.
Choose by the failure you can afford
Model selection is an operational decision, not just a benchmark contest. Start by identifying what a wrong answer costs in your application.
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When latency and throughput are the constraint
Haiku 5.5 is listed as the fastest model. It is the natural choice for tasks that must process many small requests quickly: intent classification, field extraction, triage, routing to another system, and short, predictable transformations. It is also useful as a first-pass filter before sending only difficult cases to a more capable model.
Sonnet 5.5 is listed as fast and is the practical default for interactive assistants, drafting, analysis, and applications where users are waiting for an answer. If a user-facing workflow feels slow on Sonnet, simplifying the prompt, reducing unnecessary context, streaming output, or routing simple requests to Haiku can improve the experience before you move to a more expensive model.
When quality and edge cases dominate
Opus 5.5 is intended for long-running agentic coding and knowledge work. Choose it when the system must follow many constraints, inspect a large body of material, revise its own work, or make decisions across several tool calls. The extra cost can be justified when human review, failed jobs, or production defects cost more than the token difference.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Fable 5.1 is listed as slower and is priced highest. Reserve it for problems that remain unsolved after giving Opus sufficient context and an appropriate effort level: complex reasoning, extended plans, and agents that must maintain a coherent objective over a long sequence of actions. It should be an escalation path, not an automatic choice for every request.
When a wrong answer is cheap and easy to detect
Use Haiku when an incorrect result can be caught by a rule, schema validator, confidence threshold, or human spot check. Examples include assigning a support category from a fixed list, extracting an invoice number, identifying whether a message contains a known intent, or selecting which downstream workflow should run.
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Use Sonnet when the output is open-ended or requires nuanced language but still has a reviewer, a test set, or a deterministic check. Use Opus or Fable when errors are difficult to detect automatically, affect customers or code, or create expensive downstream work.
Current model specifications and prices
Anthropic lists a 1-million-token context window and a maximum output of 128,000 tokens for each of these current models. A context window is the total input the model can consider for a request, including your prompt, conversation history, retrieved documents, tool definitions, and tool results. The maximum output is a ceiling, not a promise that every request will produce that much text.
| Model | Context window | Maximum output | What the price implies |
|---|---|---|---|
| Fable 5.1 | 1,000,000 tokens | 128,000 tokens | Highest per-token cost; use for unusually difficult reasoning |
| Opus 5.5 | 1,000,000 tokens | 128,000 tokens | Higher cost buys stronger performance for demanding work |
| Sonnet 5.5 | 1,000,000 tokens | 128,000 tokens | Balanced price for broad production use |
| Haiku 5.5 | 1,000,000 tokens | 128,000 tokens | Lowest starting price; prompts above 100,000 tokens have higher input pricing |
The shared context limit does not make the models interchangeable. A model can accept the same amount of material while differing in how reliably it follows instructions, reasons across that material, writes code, or uses tools.
How much does each Claude model cost?
Token billing separates input from output. Input includes the instructions and content you send; output is the generated response. For a simple illustration, a request using 1 million input tokens and 250,000 output tokens would have the following base cost before caching, tools, discounts, or endpoint premiums:
| Model | Input calculation | Output calculation | Illustrative total |
|---|---|---|---|
| Fable 5.1 | 1 × $10 = $10 | 0.25 × $50 = $12.50 | $22.50 |
| Opus 5.5 | 1 × $4 = $4 | 0.25 × $20 = $5 | $9.00 |
| Sonnet 5.5 | 1 × $2 = $2 | 0.25 × $10 = $2.50 | $4.50 |
| Haiku 5.5 | 1 × $0.10 = $0.10 | 0.25 × $0.50 = $0.125 | $0.225 |
This example uses Haiku’s starting rates and assumes the prompt does not trigger its higher price for inputs above 100,000 tokens. It is a comparison method, not a forecast of your bill: real usage may have a different input-to-output ratio and additional charge categories.
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Batch API discounts
Anthropic states that Batch API processing discounts both input and output tokens by 50 percent. Batch is appropriate for asynchronous jobs such as overnight classification, bulk document extraction, or evaluation runs where an immediate response is unnecessary. It does not improve interactive latency.
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Prompt-cache writes and reads
Prompt caching has separate multipliers for writing cached content and reading it later. Repeatedly sending a large, unchanged system prompt or reference corpus can therefore have a different effective cost from sending it once without caching. Include both cache-write and cache-read charges in your estimate, and measure the cache hit rate your application actually achieves.
Tools and server-side charges
Tool-use requests count tool schemas, tool-use blocks, and tool-result blocks as input tokens. A request that appears to contain a short user message can therefore consume many more billable input tokens when it includes detailed schemas or large results. Server-side tools can also add usage-based charges. Keep schemas concise, limit returned fields, and truncate or summarize tool results before passing them back to the model.
Regional and multi-region endpoints
Anthropic documents a 10 percent premium over global endpoints for regional or multi-region endpoints for Sonnet 4.5, Haiku 4.5, Opus 4.5, and later model generations. If data residency or routing requirements lead you to one of those endpoints, add the premium to your budget instead of comparing only the global list prices.
A repeatable model-selection process
- Define the failure cost. Write down what happens when the model is wrong: a rejected transaction, a security issue, a coding defect, a missed escalation, or merely a minor wording correction. Include reviewer time and rework, not just API charges.
- Set a latency target. Record the maximum acceptable first-token and full-response times for interactive requests. For asynchronous workflows, define the completion deadline instead. Sonnet and Haiku generally fit interactive targets better; Opus and Fable may be appropriate when quality gains outweigh waiting.
- Separate input and output volume. Estimate average and peak input tokens, average output tokens, conversation history, retrieved documents, tool schemas, and tool results. Do not multiply a single “tokens per request” guess by monthly requests without separating these components.
- Add billing modifiers. Account for prompt-cache writes and reads, the 50 percent Batch discount where applicable, server-side tool charges, and any regional or multi-region premium. For Haiku, determine how often prompts exceed 100,000 tokens.
- Create a fixed evaluation set. Use representative prompts, difficult edge cases, real documents, and realistic tool traces. Keep the set unchanged while comparing models so that improvements are attributable to the model rather than a different sample.
- Score useful outcomes. Track task success, factual or coding errors, reviewer corrections, p95 latency, and total cost per successful task. A cheaper model that requires two review passes may cost more per accepted result than a more capable model.
- Route by difficulty. A single application can use Haiku for routine classification, Sonnet for ordinary requests, and Opus for escalations. Define explicit escalation signals such as low confidence, failed validation, unresolved tool errors, or a request involving several dependent steps.
- Confirm the deployment identifier. Model names and platform IDs are not interchangeable. Verify the exact alias in the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, or Claude Platform on AWS integration you operate.
- Check lifecycle notices before launch. Review Anthropic’s current deprecation information and schedule a recurring check. A model that is available during development may have a dated retirement notice by the time you deploy.
Practical choices for common workloads
High-volume classification and routing
Start with Haiku 5.5. Give it a closed label set, a strict output schema, and representative borderline examples. Validate the returned label in code. Escalate only ambiguous or invalid cases to Sonnet rather than paying Sonnet’s rate for every message.
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Customer-facing chat and writing assistance
Start with Sonnet 5.5 when users expect a quick answer with dependable language quality. Keep the conversation window focused by summarizing old turns and retrieving only relevant records. Escalate to Opus when the request combines policy interpretation, multiple documents, and a consequential action.
Software development and code review
Use Sonnet for routine generation, explanation, and small edits. Test Opus 5.5 for repositories that require broad architectural understanding, multi-file changes, debugging across tools, or long-running coding agents. Use Fable 5.1 only when your evaluation shows a material gain on the hardest unresolved cases. Regardless of model, run linters, tests, type checks, and security scans outside the model.
Large-document analysis
All four models list a 1-million-token context, but loading the maximum possible context is not automatically useful. Retrieve relevant sections, preserve document structure, and measure whether additional context improves task success. Haiku’s higher input pricing above 100,000 tokens makes very large prompts especially important to cost-test.
Long-horizon agents
Use Opus 5.5 for agents that plan, call tools, inspect results, and revise their approach over many steps. Choose Fable 5.1 when the agent still fails on planning depth or sustained reasoning after you have improved prompts, tool schemas, and state management. Set step limits, timeouts, and human approval gates so a model cannot consume unbounded tokens or perform irreversible actions.
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Lifecycle, aliases, and compatibility risks
Model availability changes on a schedule. Anthropic records Sonnet 4.5 as deprecated on September 30, 2026, with retirement on November 30, 2026; Sonnet 5.5 is the recommended replacement. Sonnet 4 and Opus 4 retired on June 15, 2026. Opus 4.1 retired on August 5, 2026, and Haiku 3 retired on April 20, 2026.
These dates show why an application should not hard-code an old alias indefinitely. Keep the model identifier in configuration, monitor deprecation announcements, and run your evaluation set against the replacement before the retirement date. Platform-specific identifiers can differ even when they refer to the same model family, so verify the identifier in each hosting service.
Generation-specific parameter changes
Anthropic documents that non-default temperature, top_p, and top_k values return errors on Claude 4.7 and later models. If you are migrating an older integration, remove those overrides or set only values supported by the target model. Test request validation in a staging environment before switching production traffic.
Common selection mistakes and how to correct them
- Choosing by the model name alone: run the same fixed prompts and score successful outcomes, not just a few impressive examples.
- Comparing only input prices: output tokens, tool blocks, cache operations, and regional routing can dominate the bill.
- Sending every request to the strongest model: route routine, verifiable work to Haiku or Sonnet and reserve Opus or Fable for cases that demonstrate a need.
- Assuming a larger context solves retrieval problems: irrelevant documents increase cost and can make the answer less reliable; retrieve and summarize deliberately.
- Ignoring p95 latency: an acceptable average can hide timeouts and poor experiences during peak traffic.
- Leaving old parameter overrides in place: Claude 4.7-and-later integrations can fail when non-default sampling parameters are sent.
- Deploying an alias without a retirement plan: record the current model, replacement candidate, evaluation results, and a calendar date for the next lifecycle check.
A decision rule you can apply today
Pick Haiku 5.5 if the task is high-volume, latency-sensitive, structured, and easy to validate. Pick Sonnet 5.5 if you need a capable general model with fast responses and predictable cost. Pick Opus 5.5 if the work involves difficult coding, broad knowledge synthesis, or a tool-using agent whose mistakes are expensive. Pick Fable 5.1 only when your evaluation demonstrates that Opus cannot reliably solve the hardest reasoning or long-horizon cases.
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When you genuinely have no information about the workload, Anthropic’s official starting recommendation is Opus 5.5. Treat that as a quality-first baseline, then test Sonnet and Haiku for the portions of the workload where their lower latency and price meet your success criteria. Recheck prices, endpoint rules, aliases, and retirement notices whenever you change the deployment.
Quick Recap
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