Same unit rates do not mean the same task cost
This is a maintenance comparison for existing Opus integrations, not a current-flagship recommendation. Our dataset labels both versions legacy. That label is not a retirement deadline or a guarantee that either model is available to your account; confirm the exact model ID, endpoint, and any sunset notice in Anthropic’s model documentation.
The generated table separates input, cached input, and output rates. The chart plots input and output rates separately, not benchmark quality. It deliberately does not claim that an upgrade is free: two models with matching rates can consume different amounts of context, reasoning, and output to complete a task.
| Decision | Keep the existing version when… | Test the alternative when… |
|---|---|---|
| Prompt behavior | Your production prompts meet their acceptance target | Instruction adherence or formatting needs improvement |
| Coding agents | Patches pass tests with little rework | Failed tool calls and retries dominate cost |
| Document workflows | Extraction and citations are reliable | Your document set exposes recurring quality gaps |
| Operations | Availability and latency meet your needs | Account access, limits, or lifecycle notices require a change |
| Pricing data: How we verify → | ||
Do not infer a universal performance winner from the version number. The original release-era benchmark claims are not a substitute for an evaluation on your own prompts, tools, and documents. For a new build, include newer candidates from Anthropic pricing and OpenAI pricing; our current model shortlist explains how to narrow the test set.
Model a real workload before switching
For an illustrative monthly workload of 10 million uncached input tokens and 2 million output tokens, calculate:
Base token cost = 10 × input rate + 2 × output rate.
Use each row’s rates in the generated table, or enter those counts in the inline calculator. With matching base rates, identical token counts produce matching base bills. Any practical cost difference must then come from usage, caching, optional tools, or billing modes rather than the headline rate.
The full token calculator supports cached-input scenarios. Treat cache reads and cache writes separately: writing a cache is not priced like reading one, and shorter versus longer retention can change the bill. Confirm current cache-write and batch terms in Anthropic’s official pricing documentation. The chart is a synchronous base-rate comparison, not a batch or cache-write quote.
Track cost per accepted task alongside latency and reviewer time. A model that needs fewer retries may be cheaper even when its unit rates are identical; a longer answer that needs more correction can make a seemingly harmless model change expensive.
A practical migration checklist
- Capture a representative evaluation set, including malformed tool responses, long context, and the outputs your parser rejects.
- Pin the exact available model IDs. Keep prompts, tool schemas, and token limits comparable for the first pass.
- Compare accepted results, total billed tokens, retries, latency, and human corrections. Test supported effort settings separately rather than assuming identical behavior.
- Recheck cache-hit rates and measure cache-write charges after changing the model or prompt prefix.
- Roll out gradually with a rollback path; do not switch every production call just because the rate card matches.
Stay on a working integration while validating an alternative if there is no imminent lifecycle constraint. If availability requires migration, prioritize compatibility and quality testing before tuning costs. The API cost forecasting guide shows how to include retries and operating overhead.
Need speech output after the reasoning step? Evaluate ElevenLabs for a separately billed voice layer. It does not replace the Opus API, include Claude tokens, or change this comparison’s rates.
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