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Reviews and evidence

Claude Fable 5.1 · Official source · Platform telemetry

OpenRouter: Provider Performance and Benchmark Snapshot for Claude Fable 5.1

The September 2, 2026 OpenRouter snapshot shows P50 latency of 3.76–7.34 seconds and throughput of 26–48 tok/s across four providers, plus GPQA Diamond, TAU-Bench, structured-output errors, and availability; it is routing-layer telemetry.

Official sourcePlatform telemetryEdited 2026-09-20

Test conditions

Source-specific observation
The page is for `anthropic/claude-fable-5.1`, released September 1, 2026; Azure, Anthropic, Bedrock BYOK, and Vertex P50 latency/throughput use different windows from the one-week averages.
Published conditions
AutoExacto reports GPQA Diamond at 87.4%–90.0% and TAU-Bench at 76.8%–79.4% by provider; full item sets, repeats, effort, and sampling are not published, and auto-routing fails over on provider errors.

Key data and applicable tasks

One-sentence takeaway

OpenRouter's live page shows Fable 5.1 delivering roughly 44–56 tok/s in one-week average throughput across multiple Providers, with roughly 4.18–6.01 seconds of average first-chunk/request latency, and provides routing snapshots for GPQA Diamond, TAU-Bench, and structured-output error rates. It is suitable for choosing an integration layer, not for replacing controlled capability evaluations.

Use cases

  • Suitable tasks: Comparing latency, throughput, availability, and structured-output performance when connecting to the same model through Azure, Anthropic, Amazon Bedrock BYOK, and Google Vertex.

  • Unsuitable tasks: Inferring the model's reasoning ability, long-horizon Agent success rates, or the end-to-end experience across different clients; Provider routing and traffic composition affect the results.

  • Applicable model version: anthropic/claude-fable-5.1 on OpenRouter.

  • Applicable clients, Agents, or APIs: OpenRouter routing; the page lists high-traffic applications such as Hermes Agent, Claude Code, and Kilo Code, but this is not a controlled comparison experiment for those applications.

  • Recommended reasoning tier and parameters: The page does not disclose the effort, prompt, output length, or sampling settings corresponding to this telemetry; production integrations should fix these variables independently.

Test method/data

The page displays P50 runtime data for each Provider:

  • Standard endpoint pricing is $10/million tokens for input, $50/million tokens for output, and $0.25/million tokens for cache reads.

  • Provider P50: Azure latency 14.96s and throughput 56 tok/s; Anthropic latency 5.97s, throughput 45 tok/s, and availability 99.91%; Amazon Bedrock (BYOK) latency 11.58s and throughput 53 tok/s; Google Vertex latency 4.84s, throughput 65 tok/s, and availability 99.35%.

  • Average throughput over the past week: Vertex 56 tok/s, Bedrock 46 tok/s, and Anthropic 44 tok/s; average first-chunk/request latency: Vertex 4.18s, Anthropic 5.96s, and Azure 6.01s; end-to-end average latency was 12.51s, 16.20s, and 16.15s, respectively.

  • AutoExacto Benchmarks: GPQA Diamond—automatic routing 90.9%, Anthropic 86.5%, Vertex 84.9%, and Azure 86.3%; TAU-Bench—Anthropic 79.3%, Vertex 76.7%, and Azure 74.7%. The page does not provide the complete question sets or repetition counts for the automatic-routing and Provider results.

  • Structured-output error rates: Anthropic averaged 8.33%, Bedrock 33.12%, and Azure 38.51%; cache hit rates were approximately 85.26%–86.82%.

  • At collection time, the page showed OpenRouter uptime of 100% and availability of 99.92% over the past 3 days, while "no-routing" availability, which does not bypass Provider failures, was 98.25%.

Conclusion

OpenRouter's evidence is primarily useful for deployment-layer decisions: Vertex led in throughput and latency in this page snapshot, while the Anthropic endpoint had a lower structured-output error rate; if a task depends on JSON/structured calls, Provider differences may affect stability more than the model name itself. In production, fix the provider filter, routing strategy, and retry method before measuring again.

Limitations and risks

  • This is platform live telemetry, not a public controlled benchmark report; the page does not provide the complete input set, output-length distribution, effort, sampling parameters, or statistical confidence intervals.

  • P50, past-week averages, and past-three-days uptime use different time windows and cannot be treated as one unified metric.

  • OpenRouter automatically switches when a Provider fails; availability with routing enabled cannot be directly compared with availability with routing disabled.

  • BYOK, billing, caching, and data-retention policies differ; the page specifically notes that Anthropic's data-retention rules do not allow zero data retention. Production compliance requires a separate check.

  • Performance data changes with Provider queueing, region, traffic, and the page's time window; this article preserves only the 2026-09-08 snapshot.

Reproduction recommendations

  1. Fix the OpenRouter provider filter, region, routing strategy, model snapshot, prompt length, output limit, and effort.

  2. Send repeated requests to each Provider using the same input set, recording TTFT, end-to-end latency, throughput, structured-output parsing success rate, retries, and fallback.

  3. Report integration-layer metrics separately from capability evaluations; do not use the provider GPQA/TAU snapshots as a substitute for a complete model benchmark.

What this supports

  • It supports choosing providers, routing, and structured-output retry policies, then reproducing TTFT, end-to-end latency, throughput, and parse success with the provider filter fixed.

What this does not support

  • It cannot establish pure model reasoning ability or production success; queueing, region, caching, time windows, and automatic failover change the telemetry.

Method, limits, and reproduction

The figures, task set, reasoning tier, and client conditions apply only to the listed source and collection snapshot. Different versions, harnesses, or providers must not be compared directly; undisclosed parameters remain unknown.

For a reproduction, fix the model version, provider or client, reasoning tier, tools, task-set version, sample count, and collection date, and record failures, retries, and human corrections. Full steps are in the source notes below.

Original source

OpenRouter Model Page · OpenRouter · Original publication date 2026-09-02 · Site edit date 2026-09-20

Open original source

Claude Fable 5.1

Compare Claude Fable 5.1 in Tabbit

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Read the full analysis

Overview · English

Claude Fable 5.1: What It Is, What Changed, and How to Get Access

A sourced Claude Fable 5.1 overview covering model specs, the 75% cache read discount, Mythos 5.1 lineage, benchmark limits, and access routes.

Full review · English

Claude Fable 5.1 Review: Exceptional Agentic Work, Real Cost Trade-offs

A source-led Claude Fable 5.1 review covering coding, agentic work, pricing, benchmark limits, community feedback and Tabbit's unverified access boundary.

Pricing · English

Claude Fable 5.1 Pricing: Rates, Effort Costs, and Real Bills

Claude Fable 5.1 pricing explained: the full API rate card, the 75% cache read cut, the effort cost ladder, plan rules, worked budgets, and a local calculator.

Related reviews

Reddit community: Skepticism about Fable 5.1 benchmarks and task-tiered experienceA September 2, 2026 Reddit discussion combines reactions to the official chart with quota and safety-guardrail anecdotes, warning that benchmarks may diverge from daily coding; comments share no common task, version, client, or logs.Claude Fable 5.1 Official Release: Multiple Benchmarks, Cost Tiers, and Safety BoundariesAnthropic’s September 2026 release separates Fable 5.1’s coding, knowledge-work, and long-running-task claims from production-safeguard boundaries; Browserbase reports 82% browser-agent completion, while Rogo reports equal Finance accuracy with 20% fewer tokens.Artificial Analysis: Claude Fable 5.1 Intelligence Index, Task Breakdowns, and CostArtificial Analysis’s September 1, 2026 pre-release evaluation gives Fable 5.1 max an Intelligence Index of 66, HLE 59.1%, Terminal-Bench v2.1 91.4%, and SciCode 62.0%; its default server-side fallback supplied about 4% of output tokens from Opus 4.8 or Opus 5.SimpleBench: Claude Fable 5.1's Everyday Reasoning and Human BaselineThe SimpleBench snapshot collected on 2026-09-08 records Fable 5.1 at 86.6% on AVG@5, above the 83.7% average of nine non-expert participants; the set has 200+ multiple-choice questions on spatial, temporal, social, and language-trap reasoning.Claude Fable 5.1 Official Prompting Methods: Writing Density, Batched Tool Calls, and Task CompletionAnthropic’s Fable 5.1 page connects writing density, user-visible progress, independent tool batching, and complete task closure; it is a behavior guide, not a fact-verification template.Reddit Prompting Techniques: Long-form Analysis, Counterarguments, and Concise ExecutionA r/claude user turns long-form analysis into three steps: ask what questions to raise, argue the strongest countercase, then execute against explicit criteria; it is a single-user Fable 5.1 workflow report, not a controlled evaluation.Reddit case: Fable 5.1 + Blender MCP generates a large world region in one shotA r/ClaudeAI Blender MCP case combines opposing-faction art direction, regional assets, aerial shots, and a 30–60-second camera path in one prototype task; the author also reports weak individual assets, so it is not a production-ready result.