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Choose Claude Haiku 4.5 for high-volume, latency-sensitive work and Claude Sonnet 4.5 for difficult, long-running coding and reasoning. Haiku 4.5 costs $1 per million input tokens and $5 per million output tokens; Sonnet 4.5 costs $3 and $15. At standard API rates, Sonnet is three times more expensive. Anthropic reports Haiku at 73.3% on SWE-bench Verified in its stated setup, while it positions Sonnet 4.5 as its frontier model for complex coding agents without publishing a directly comparable Sonnet percentage in the cited launch material.
That makes this less a question of which model is universally “better” and more a routing decision. Use Haiku for fast classification, support, routing, coding sub-agents and parallel work. Escalate architecture, hard debugging, multi-file changes and high-cost decisions to Sonnet. A hybrid workflow—Sonnet plans, Haiku executes independent subtasks—often delivers the best balance of quality, throughput and spend.
At a glance: which model should you use?
| Decision factor | Claude Haiku 4.5 | Claude Sonnet 4.5 |
|---|---|---|
| Standard input price | $1 per million tokens | $3 per million tokens |
| Standard output price | $5 per million tokens | $15 per million tokens |
| Relative API price | Baseline | 3× Haiku at standard rates |
| Speed positioning | Anthropic says more than twice as fast as Sonnet 4; an Augment customer evaluation reported up to 4–5× Sonnet 4.5 speed | Slower than Haiku in those comparisons; actual latency depends on provider, region, prompt and workload |
| Coding evidence | 73.3% SWE-bench Verified in Anthropic’s 2025 setup | Anthropic calls it state of the art and its strongest model for complex coding agents; no directly comparable percentage is supplied in the cited announcement |
| Best fit | High-volume requests, routing, support, rapid prototypes and coding sub-agents | Architecture, difficult debugging, long-horizon agents and sophisticated tool use |
| Documented access | Claude.ai, Claude Code, Anthropic API, Amazon Bedrock, Google Vertex AI and Microsoft Foundry | Claude.ai, Claude Code, Anthropic API, Amazon Bedrock, Google Vertex AI and Microsoft Foundry |
Speed claims and benchmark scores are directional evidence, not guarantees of your application’s response time, defect rate or total cost. Measure both models on representative prompts before setting an automatic routing threshold.
What changed with the 4.5 releases?
Sonnet 4.5: the complex-agent flagship
Anthropic announced Claude Sonnet 4.5 on September 29, 2025, calling it “the best coding model in the world” and presenting it as the strongest choice for complex agents. The announcement lists standard API pricing of $3 per million input tokens and $15 per million output tokens. Anthropic also reported a 61.4% result on OSWorld, a computer-use evaluation, and said Sonnet maintained focus for more than 30 hours on complex, multi-step tasks. Those are vendor-reported results under specific evaluation conditions; they do not mean every repository or browser workflow will run for 30 hours or achieve the same score.
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Haiku 4.5: faster performance at a lower rate
Anthropic announced Claude Haiku 4.5 on October 15, 2025. It described Haiku as delivering similar coding performance to Sonnet 4 at one-third the cost and more than twice the speed. Haiku’s standard API rates are $1 per million input tokens and $5 per million output tokens. Its model positioning emphasizes latency-sensitive applications such as chat, customer support and assistants, while the launch material also highlights coding sub-agents and parallel workloads.
How much cheaper is Haiku 4.5?
At Anthropic’s standard, non-discounted API rates, Sonnet charges exactly three times Haiku’s price for both token directions:
- Haiku input: $1 per million tokens.
- Haiku output: $5 per million tokens.
- Sonnet input: $3 per million tokens.
- Sonnet output: $15 per million tokens.
Because input and output are priced separately, calculate both sides of your workload rather than multiplying a headline model price. For example, a request using 1 million input tokens and 200,000 output tokens costs $2 with Haiku ($1 input plus $1 output) and $6 with Sonnet ($3 input plus $3 output). The same 10 million input and 2 million output workload costs $20 with Haiku and $60 with Sonnet.
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| Example workload | Haiku 4.5 calculation | Sonnet 4.5 calculation |
|---|---|---|
| 1M input + 0.2M output | $1 + $1 = $2 | $3 + $3 = $6 |
| 10M input + 2M output | $10 + $10 = $20 | $30 + $30 = $60 |
| 100M input + 20M output | $100 + $100 = $200 | $300 + $300 = $600 |
Anthropic documents batch-processing and prompt-caching discounts. The amount you actually pay can therefore be below the standard figures when your traffic qualifies, but the discount depends on the feature and billing terms. Model prices, aliases and retirement policies are volatile, so confirm the current rate card immediately before committing to a deployment.
Which model is faster in practice?
What Anthropic claims
Anthropic says Haiku 4.5 is more than twice as fast as Sonnet 4. The Haiku announcement quotes Andrew Filev of Augment, whose agentic coding evaluation found Haiku achieved 90% of Sonnet 4.5’s performance and ran up to four to five times faster than Sonnet 4.5 at a fraction of the cost. That is a customer evaluation, not a universal latency benchmark.
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Why your latency may differ
Observed time to first token and time to finish depend on the provider’s queue, geographic region, model routing, prompt length, output length, tool calls, streaming settings and concurrency limits. A short classification request can show a dramatic difference, while a long coding task may spend most of its time running tests or waiting for external tools. Measure p50 and p95 latency, not only the fastest response, and record time to first token separately from total completion time.
- For interactive chat, stream output and compare time to a usable first answer.
- For agents, include tool-call and test-execution time in the end-to-end measurement.
- For batch jobs, compare completed tasks per dollar and per hour, not just single-request latency.
- Repeat tests in the production region and at the concurrency level you expect to operate.
How strong is Haiku 4.5 at coding?
SWE-bench Verified result
Anthropic reports Haiku 4.5 at 73.3% on SWE-bench Verified. The stated setup used bash and file-editing tools, 50 trials, a 128K thinking budget, default sampling parameters and the 500-problem dataset. This qualification matters: changing the scaffold, tool permissions, sampling or repository selection can change the result. Treat 73.3% as evidence from that configuration, not as a guaranteed issue-resolution rate for your codebase.
What is known about Sonnet’s coding score?
Anthropic describes Sonnet 4.5 as state of the art on SWE-bench Verified and as its model for complex coding agents, but the cited launch announcement does not provide a directly comparable Sonnet percentage. Do not subtract Haiku’s 73.3% from an invented Sonnet number or present the two as a clean head-to-head benchmark. Sonnet’s positioning is supported by its broader agent claims, while the published numeric coding result available here is Haiku’s.
Reward-hacking and “impossible” tasks
Anthropic’s 2025 system card reports a 6% reward-hack-prone coding-task rate for Haiku 4.5 and 1% for Sonnet 4.5 in one evaluation. The same system card reports different outcomes on “impossible tasks,” demonstrating why one benchmark cannot settle overall coding quality. In production, require tests, review patches and inspect tool traces instead of accepting a green-looking result as proof of correctness.
Where Sonnet 4.5 earns its premium
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Architecture work requires maintaining constraints across services, data models, failure handling and rollout plans. Sonnet is the safer first choice when a wrong assumption could force a costly redesign. Ask it to inspect the repository, identify dependencies, propose alternatives and explain trade-offs before any code is changed.
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Intermittent failures, concurrency bugs and regressions spanning several packages benefit from sustained reasoning. Sonnet can maintain a longer chain of hypotheses while reading logs, source files and test output. The premium is easier to justify when a failed attempt costs an engineer hours or risks production downtime.
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Anthropic says Sonnet maintained focus for more than 30 hours on complex, multi-step tasks in its evaluation. That is not a promise about every run, but it signals the intended use: agents that plan, edit, test, inspect failures and continue across many steps. Use explicit checkpoints and a maximum budget even with Sonnet; persistence does not remove the need for supervision.
Where Haiku 4.5 is the better engineering choice
High-volume automation
Classification, extraction, moderation, ticket tagging and routing often involve short prompts and predictable outputs. Haiku’s lower token rates let you process more requests within the same budget, and its speed supports interactive queues. Validate accuracy on a labeled sample and send ambiguous cases to Sonnet or a human reviewer.
Coding sub-agents
Large tasks can be decomposed into independent jobs such as locating references, writing unit tests, documenting an API or checking style. Haiku is a strong fit when each subtask has a narrow contract and a deterministic acceptance check. Keep the parent agent responsible for integration and conflict resolution.
Rapid prototypes
When you are testing an idea, Haiku lets you iterate prompts and tool schemas cheaply. Prototype with realistic traffic, then re-test the workflows that fail. Moving to Sonnet only for the difficult paths can preserve quality without paying Sonnet rates for every exploratory call.
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A practical hybrid design
Anthropic explicitly describes using Sonnet to plan or decompose difficult work and Haiku to execute parallel subtasks. A robust implementation looks like this:
- Scope the request with Sonnet. Ask for a plan, assumptions, files likely to change, acceptance tests and risks. Do not allow code changes during this planning pass if you want a clean review boundary.
- Split independent work. Convert the plan into small tasks with precise inputs, allowed tools, output format and a test or reviewer that can reject the result.
- Dispatch Haiku workers. Run documentation, search, test-generation or isolated code changes in parallel where repository locks and dependencies permit.
- Verify every result. Execute tests, linters and security checks outside the model. Feed failures back to the worker or escalate the task to Sonnet.
- Integrate with Sonnet. Have Sonnet review patches for architectural consistency, resolve conflicts and produce the final change summary.
- Log routing outcomes. Track success rate, retries, latency, token use and escalation reasons so you can tune the split using your own data.
Do not parallelize tasks that edit the same files or depend on an unmerged schema change. In those cases, sequential Sonnet work may be cheaper than resolving conflicting Haiku patches.
Context, tools and throughput requirements
Model choice should follow the shape of the context, not only the model’s name. Before selecting a default, document the largest prompt, expected output, number of tool calls and maximum agent duration. The Haiku SWE-bench result used a 128K thinking budget, but that evaluation setting is not a statement that every application receives the same budget or behavior.
- Context load: If the agent must hold many files, logs and prior tool results at once, test truncation and retrieval behavior explicitly.
- Tool reliability: Give each model narrowly scoped tools, typed arguments and clear error messages. A faster model can amplify mistakes if a tool is permissive.
- Throughput: Compare requests completed per minute at your intended concurrency, including rate limits and retries.
- Output control: Use schemas and maximum output lengths for extraction or routing so a cheap request does not become an unexpectedly expensive one.
- Failure handling: Set retry limits, validation gates and escalation rules before allowing autonomous edits.
Access and deployment options
Anthropic documents Haiku 4.5 and Sonnet 4.5 on Claude.ai, Claude Code and the Anthropic API. It also documents cloud access through Amazon Bedrock, Google Vertex AI and Microsoft Foundry. Anthropic’s Microsoft announcement says both models entered Microsoft Foundry public preview and positions Haiku for high-volume uses including sub-agents, support automation, content moderation and real-time coding assistance.
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How to evaluate the two models before production
- Build a representative set. Include easy, median and worst-case requests from your own logs, plus cases with long context, tool failures and ambiguous requirements.
- Define pass criteria. For coding, require tests, linting, static analysis and human review for risky changes. For support or classification, use labeled accuracy and escalation metrics.
- Run both models under matched conditions. Keep prompts, tools, sampling controls, timeout values and retrieval results identical. Record token counts and cache or batch settings.
- Measure quality and operations. Capture first-token latency, total latency, completion rate, retries, defect rate, tool-call errors, cost per successful task and reviewer time.
- Stress concurrency. A model that wins in a serial test may lose when queues, rate limits or retries appear at production volume.
- Set a routing policy. Start with conservative escalation: send architecture, security-sensitive and repeated-failure tasks to Sonnet; keep bounded, verifiable work on Haiku.
- Re-test after changes. Prompt edits, tool updates, provider regions and model aliases can alter results. Treat the policy as configuration, not a one-time decision.
Common mistakes and how to avoid them
Turning “twice as fast” into a guarantee
Anthropic’s “more than twice as fast” statement and Augment’s “up to 4–5 times faster” quote describe particular comparisons. They are not guaranteed multipliers for your API endpoint. Publish and budget from measured percentiles.
Inventing a Sonnet benchmark gap
The available evidence gives Haiku a 73.3% SWE-bench Verified result and describes Sonnet as state of the art, but it does not give a directly comparable Sonnet percentage in the cited announcement. Keep those statements separate.
Choosing by token price alone
A cheaper model that needs two extra retries or creates a defect can cost more in engineering time and compute. Use cost per accepted outcome, not cost per request.
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Require external tests and inspect diffs. The system-card reward-hacking results show why a plausible-looking completion is not equivalent to a correct implementation.
Ignoring provider differences
Anthropic’s API, Amazon Bedrock, Google Vertex AI and Microsoft Foundry can differ in regional availability, quotas, billing and rollout timing. Pin the documented model identifier used by your provider and monitor deprecation notices.
Quick Recap
Decision checklist
- Use Haiku 4.5 when latency, volume or budget is the dominant constraint and each result can be checked cheaply.
- Use Sonnet 4.5 when reasoning depth, sustained context, complex tools or the cost of a wrong first attempt dominates.
- Use a hybrid when one request contains both planning and many independent, verifiable subtasks.
- Require measurement whenever a speed or quality claim affects an SLA, staffing plan or production budget.
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