Claude Fable 5.2vsClaude Opus 5.5

Claude Fable 5.2 lists twice the input and output token rate of Claude Opus 5.5 on the live catalog: $7.20 and $36.00 against $3.60 and $18.00. The provider list rates in the table are $8.00/$40.00 and $4.00/$20.00.

For a request with 10,000 uncached input tokens and 1,000 output tokens, those GPTProto rates come to about $0.054 for Claude Opus 5.5 and $0.108 for Claude Fable 5.2. That illustration is input plus output only. Cache reads, cache writes, tool calls, and retries are extra, and a second attempt can erase the gap.

Billed output can include thinking, not only the visible answer. Catalog cache reads are $0.18 per million tokens for Claude Fable 5.2 and $0.18 for Claude Opus 5.5. Cache writes are $9.00 and $4.50. Those are GPTProto catalog rates. Anthropic's own cache price is not a quote for this route.

Anthropic reports that, in early FrontierCode 2.0 testing, Fable at high effort scored about level with Opus at max effort for roughly half the cost per task. That is an evaluation result, not a GPTProto discount or a forecast for your workload. Keep Opus where it already finishes the task, and test Fable where extra attempts are the expensive part.

Check the live Claude Fable 5.2 and Claude Opus 5.5 pages before you budget a production route. This comparison reads the same catalog those pages use. It does not copy a price from OpenRouter.

Anthropic's model cards list a 1M-token context window and a 128K maximum output for both, with text and image input and text output. Fable 5.2 was released on November 9, 2026, and Opus 5.5 on September 22, 2026. The comparison table shows a context, output, latency, or release figure only when this page can read it. Matching published limits are not a quality advantage. Test retrieval at the lengths you actually send.

Anthropic labels both models Moderate for comparative latency. Its claim of up to 30% faster output compares Fable 5.2 with Fable 5.1, not with Opus 5.5. Measure first-response time and full task duration separately, because planning, thinking, and tool calls can dominate an agent's runtime.

Both keep adaptive thinking on. Anthropic's Claude API defaults to high effort for Fable and medium effort for Opus. A comparison that silently uses those defaults is not an equal-effort test. Record the effort you would deploy next to cost and success rate.

Anthropic also publishes an optional fast mode for both models, at a higher official rate than the standard price. The GPTProto figures on this page are the standard catalog rates and do not include that mode. Availability depends on the endpoint you call.

For Fable's native API, Anthropic documents a 400 error when tool choice is forced, and instructs developers to omit temperature, top_p, and top_k. Adaptive thinking cannot be turned off. Fable also requires 30-day retention unless Anthropic expressly authorizes zero data retention. Do not assume a GPTProto route forwards every native setting, or that a no-training statement means zero retention.

Anthropic's November 9, 2026 announcement compares these two versions directly. At the highest effort shown, Fable scores 49.1% on FrontierCode 2.0 against 45.5% for Opus, 69.9% against 62.3% on FrontierSWE v2, 51.4% against 42.5% on AutomationBench, 74.2% against 67.7% on Humanity's Last Exam with tools, and 73.4% against 64.4% on Chartography without tools. On AA-Briefcase v1.1, Fable's rubric is 66.1% and Opus's is 61.2%, while Opus leads the Elo, 1800 to 1785. Those are Anthropic-reported results with safeguards, not a GPTProto speed or cost measurement. Rubric and Elo are different measures.

Fable's lead on the coding and reasoning rows makes it a candidate for repository-wide changes, hard debugging, and research that needs repeated investigation. It does not make every short coding request a Fable task. If Opus already produces accepted changes with few retries, the higher rate has to improve the outcome enough to justify it.

Build the set from work you already run: a routine code change, a failure that is expensive when it ships, and a long-context example. Send the same instructions, tools, and acceptance checks to both models. Record effort, accepted completions, total billed tokens, retries, full runtime, and review time. Keep difficult cases separate from routine ones so an average does not hide where escalation helps.