The Tool Desk
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Choose Claude Haiku 5.5 when latency and throughput dominate, Claude Sonnet 5.5 for the usual balance of speed, quality, and price, Claude Opus 5.5 for demanding coding and agentic knowledge work, and Claude Fable 5.1 when the hardest reasoning or longest-horizon agent tasks justify slower, more expensive inference. If you are genuinely unsure, Anthropic’s current models overview says to start with Opus 5.5, then evaluate whether Sonnet or Haiku can meet your quality target at lower cost.
The right choice is not determined by the model name alone. Compare representative tasks using success rate, reviewer corrections, p95 latency, and total cost per successful task. Check the exact alias, hosting platform, pricing modifiers, and retirement notices before production deployment.
Claude model choice at a glance
All four current models list a 1-million-token context window and a maximum output of 128,000 tokens. They differ mainly in response speed, reasoning depth, and token price.
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|---|---|---|---|---|
| Claude Haiku 5.5 | Fastest | From $0.10; higher pricing applies to prompts above 100,000 tokens | From $0.50 | High-volume classification, extraction, routing, and other latency-sensitive work |
| Claude Sonnet 5.5 | Fast | $2 | $10 | Interactive applications and the default speed-intelligence-cost balance |
| Claude Opus 5.5 | Moderate | $4 | $20 | Long-running agentic coding and demanding knowledge work |
| Claude Fable 5.1 | Slower | $10 | $50 | Tasks that remain unsolved at higher-effort Opus quality, especially difficult reasoning and long-horizon agents |
The prices above are Anthropic’s 2026 list prices per million tokens. They are not a complete invoice estimate: batching, prompt-cache reads and writes, tool calls, endpoint region, and prompt length can change the effective amount.
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What each Claude model is designed to do
Claude Haiku 5.5: throughput and low latency
Haiku 5.5 is the fastest model in the current lineup and the least expensive at its starting rate. It is the practical first choice when a system must process many small requests quickly: intent classification, document or field extraction, queue routing, lightweight moderation, and similar predictable transformations.
Use Haiku when an occasional difficult edge case can be caught by validation, a fallback model, or human review. It is less appropriate when one response must reconcile ambiguous evidence, maintain a long chain of dependencies, write production code without close review, or plan several tool calls. The low listed price also has an important qualification: prompts above 100,000 tokens use higher Haiku input pricing, so a very large context can narrow the apparent savings.
Claude Sonnet 5.5: the balanced default
Sonnet 5.5 is listed as fast and costs $2 per million input tokens and $10 per million output tokens. It is the sensible starting point for interactive assistants, business workflows, drafting, summarization with judgment, and applications where users notice delay but still need reliable reasoning.
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Sonnet is often the best production default after evaluation because it provides a large step up from a simple high-volume classifier without Opus pricing. It can also serve as a second-stage model: let Haiku route or pre-process requests, then send only uncertain or high-value cases to Sonnet.
Claude Opus 5.5: difficult coding and agentic knowledge work
Opus 5.5 has moderate relative latency and costs $4 per million input tokens and $20 per million output tokens. Anthropic positions it for long-running agentic coding and knowledge work, where the model must preserve context, make multiple decisions, use tools, and recover from intermediate errors.
Choose Opus when failure costs more than the token difference. Examples include a code change that touches several subsystems, a research or analysis workflow with many dependent steps, and an internal agent whose mistakes require substantial expert correction. If the task succeeds with Sonnet at the same review standard, Opus is unnecessary overhead; if Sonnet repeatedly misses edge cases, Opus is the next model to test.
Claude Fable 5.1: maximum reasoning effort
Fable 5.1 is listed as the slowest and most expensive option at $10 per million input tokens and $50 per million output tokens. Anthropic directs readers to it for demanding reasoning and long-horizon agentic work. The model should be reserved for tasks that remain unsolved even when Opus is given sufficient context and effort.
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How speed, quality, and cost interact
Latency
The lineup’s relative order is Fable 5.1 (slower), Opus 5.5 (moderate), Sonnet 5.5 (fast), and Haiku 5.5 (fastest). Treat these labels as directional rather than as a guaranteed number of seconds. Actual p95 latency depends on prompt size, output length, concurrent traffic, tool calls, endpoint, and service conditions.
For a user-facing chat box, start testing Sonnet and Haiku. For an asynchronous queue, moderate or slow latency may be acceptable if the model prevents expensive rework. Measure p50 and p95 response times on your own representative prompts instead of choosing from the ordering alone.
Reasoning and failure cost
Move upward from Haiku to Sonnet to Opus as the cost of a missed constraint, coding defect, or incorrect conclusion rises. Use Fable only when the problem remains unsolved at higher-effort Opus quality. A cheaper response is not cheaper if reviewers must rewrite it or an agent makes an irreversible change.
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Token economics
Input and output tokens are priced separately. Long instructions, retrieved documents, conversation history, tool schemas, and tool results all increase input usage; a verbose answer increases output usage. A model with a higher per-token price can still lower total cost per successful task if it needs fewer retries, fewer tool calls, or less human correction.
Understanding Claude pricing beyond the headline rate
Base token prices
| Model | Illustrative input calculation | Illustrative output calculation |
|---|---|---|
| Fable 5.1 | 2 million input tokens × $10 = $20 | 500,000 output tokens × $50 = $25 |
| Opus 5.5 | 2 million × $4 = $8 | 500,000 × $20 = $10 |
| Sonnet 5.5 | 2 million × $2 = $4 | 500,000 × $10 = $5 |
| Haiku 5.5 | 2 million × the applicable Haiku input rate | 500,000 × the applicable Haiku output rate |
The table is an arithmetic illustration using the listed rates, not a forecast of your bill. Haiku’s displayed “from” prices are minimum starting rates, and prompts above 100,000 tokens have a higher Haiku input price.
Batch API discount
Anthropic states that Batch API processing discounts both input and output tokens by 50 percent. Batch is therefore useful for offline classification, backfills, nightly enrichment, and other jobs that do not need an immediate response. Confirm that your workflow qualifies as batch processing before applying the discount to a budget.
Prompt caching
Prompt caching has separate write and read multipliers. A repeated long system prompt or document set can cost less on cache reads than sending the full content every time, but the initial cache write and subsequent read rates must be included in the calculation. Track cache-hit behavior; a design that rarely reuses a prompt may pay write costs without receiving enough reads to benefit.
Regional and multi-region endpoints
Regional or multi-region endpoints carry a 10 percent premium over global endpoints for Sonnet 4.5, Haiku 4.5, Opus 4.5, and later model generations. The premium can be justified by data-residency or routing requirements, but include it explicitly in estimates rather than applying global rates to a regional deployment.
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Tool-use charges
Tool-use requests count tool schemas, tool-use blocks, and tool-result blocks as input tokens. Server-side tools can also add usage-based charges. An agent that repeatedly sends large schemas or full tool results may consume more input tokens than the user’s visible prompt suggests. Minimize redundant schemas, return only the necessary fields, and record tool-token usage separately from ordinary conversation tokens.
Which Claude model should you use for common workloads?
Classification, extraction, and routing
Start with Haiku 5.5 when labels are well defined and errors can be checked. Build a confidence or validation path that escalates ambiguous records to Sonnet. If the classifier handles very long prompts, price the over-100,000-token Haiku tier rather than assuming the starting rate.
Interactive customer or employee assistants
Test Sonnet 5.5 first when users expect a quick answer with useful reasoning. Haiku may be sufficient for narrow FAQ lookup or intent routing, while Opus can be reserved for escalations that involve several documents, tools, or consequential decisions.
Software development
Use Sonnet for routine code generation, explanation, refactoring, and test drafting. Move to Opus for repository-wide changes, difficult debugging, architecture decisions, and agents that must run a long sequence of coding tools. Fable is a specialist escalation for especially difficult reasoning after Opus has been evaluated and found insufficient. Regardless of model, run tests, inspect diffs, and require approval before production changes.
Knowledge work and research synthesis
Sonnet handles many summaries and structured reports. Opus is better suited when the workflow must reconcile conflicting material, preserve a large dependency graph, or operate through multiple research and retrieval steps. Fable is appropriate only when the unresolved problem demands the additional reasoning level and the slower response is acceptable.
Long documents and large context
Each current model lists a 1-million-token context window and a 128,000-token maximum output. A large window does not mean every prompt should contain a million tokens. More context can increase cost, latency, and distraction. Retrieve the smallest evidence set that supports the task, preserve stable instructions through caching when reuse is high, and test whether quality actually improves as context grows.
A repeatable model-selection process
1. Define the failure cost
List what goes wrong when the model fails: a wrong label, a missed edge case, a coding defect, a compliance issue, or hours of reviewer work. Assign a practical cost to correction or escalation. This prevents a low token price from dominating a high-consequence decision.
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2. Set a latency target
Specify an acceptable p50 and p95 response time for the user or queue. Interactive applications usually begin with Haiku or Sonnet; asynchronous pipelines can test Opus or Fable when the quality gain outweighs delay.
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3. Separate input and output volume
Estimate monthly input and output tokens independently. Include system prompts, conversation history, retrieved text, tool schemas, tool results, expected retries, and maximum answer length. Add cache writes, cache reads, batch eligibility, and any regional premium.
4. Build a fixed evaluation set
Use representative prompts, including normal cases, edge cases, long contexts, ambiguous requests, and known historical failures. Keep the set fixed while comparing models so that a change in score reflects the model rather than an easier sample.
5. Measure successful-task cost
Record task success, reviewer corrections, p50 and p95 latency, input and output tokens, tool calls, retries, and total spend. Calculate cost per successful task, not only cost per million tokens. A model that is twice as expensive per token may be cheaper per accepted result if it avoids rework.
6. Test routing and fallbacks
A single model is not mandatory. A common pattern is Haiku for routing, Sonnet for ordinary work, and Opus for escalations. Define the escalation signal before launch—such as failed validation, low confidence, tool-loop exhaustion, or a high-impact task class—so that routing is measurable rather than ad hoc.
7. Confirm the deployment identity
Record the exact model alias and the platform that serves it. Platform identifiers differ across the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. A configuration copied from one platform may not be a valid identifier on another.
Context limits, output limits, and prompt design
The listed 1-million-token context window is shared as a capability across the current models, while the maximum output is 128,000 tokens. Those ceilings are not targets. Very long prompts can increase latency and input cost, and an output limit does not guarantee that the model will usefully fill it.
- Keep durable instructions short and stable so prompt caching can be effective when the same content is reused.
- Retrieve relevant passages instead of appending an entire corpus by default.
- Limit tool results to fields the next decision actually needs.
- Set an output budget appropriate to the task and validate structured responses.
- Measure quality at several context sizes; a larger window can add irrelevant material as well as useful evidence.
Lifecycle, aliases, and retirement risk
Model selection is also a maintenance decision. Anthropic’s lifecycle records Sonnet 4.5 as deprecated on September 30, 2026, with retirement scheduled for 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.
Retirement dates apply to the documented model versions and can change the amount of migration time available. Check the lifecycle and deprecation notices before launch and on a scheduled basis. Pin an intentional alias where the platform supports it, record the successor model, and keep an evaluation set ready so a replacement can be tested before traffic is moved.
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Migration checklist
- Inventory every model alias, platform identifier, region, and endpoint used in production.
- Match each old alias to its documented replacement and retirement date.
- Run the fixed evaluation set on the replacement, including long-context and tool-use cases.
- Compare success rate, corrections, p95 latency, token usage, and cost per successful task.
- Canary the replacement on a small share of traffic, monitor errors, and retain a rollback path until results are stable.
- Update prompts and SDK configuration only after confirming the target platform’s identifier and parameter support.
Generation-specific parameter compatibility
Anthropic documents that non-default temperature, top_p, and top_k values return errors on Claude 4.7 and later models. Treat this as a configuration compatibility issue: do not assume sampling parameters accepted by an older model will work unchanged on a newer generation. Test the request with documented defaults, remove unsupported overrides, and make parameter validation part of deployment checks.
Common selection mistakes and fixes
Choosing only by token price
Symptom: Haiku appears cheapest, but reviewers spend more time correcting results. Fix: measure cost per successful task and add escalation for uncertain cases.
Using Opus for every request
Symptom: Quality is high, but latency and spend are excessive for routine work. Fix: benchmark Sonnet on the ordinary task set and reserve Opus for defined high-complexity routes.
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Symptom: Large prompts are slow, costly, and still miss the important passage. Fix: improve retrieval and context selection, then compare quality at smaller and larger context sizes.
Forgetting tool tokens
Symptom: billed input is much higher than the visible user text. Fix: count schemas, tool-use blocks, and tool results, and trim repeated or unnecessary fields.
Ignoring endpoint premiums
Symptom: the invoice exceeds a global-rate estimate. Fix: apply the documented 10 percent regional or multi-region premium where it applies and keep the endpoint in the cost model.
Waiting until retirement day to migrate
Symptom: an alias stops working before the replacement has been evaluated. Fix: monitor lifecycle notices, test successors early, and maintain a canary and rollback plan.
Quick Recap
Practical recommendations
- Need the fastest high-volume processing: start with Haiku 5.5 and validate or escalate uncertain outputs.
- Need a general interactive default: start with Sonnet 5.5 and test Haiku for narrow, repetitive routes.
- Need difficult coding or long agent runs: evaluate Opus 5.5 against Sonnet using repository-level and tool-use tasks.
- Need the hardest reasoning: try Fable 5.1 only after Opus has been tested and shown insufficient.
- Unsure where to begin: follow Anthropic’s guidance to start with Opus 5.5, then use measured results to decide whether Sonnet or Haiku meets the same acceptance criteria at lower latency or cost.
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