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Claude Fable 5 (Max, Opus 4.8 Fallback)

deprecated by benchmark source
Anthropic · released 2026-06-09 · 9 offers

Context 1M tokens

Top 4 cheapest providers (Adjusted $/task)

The same list price can give a different adjusted $/task (caching, token efficiency) — click a price for its inputs.

Within the active global provider, residency and confidentiality filters.

#ProviderAdjusted $/task
1Azurevia OpenRouter — Price via OpenRouter: OpenRouter's price for Azure's endpoint (https://openrouter.ai/anthropic/claude-fable-5/providers), collected 2026-11-03.
2Amazon Bedrockvia OpenRouter — Price via OpenRouter: OpenRouter's price for Amazon Bedrock's endpoint (https://openrouter.ai/anthropic/claude-fable-5/providers), collected 2026-11-03.
3Googlevia OpenRouter — Price via OpenRouter: OpenRouter's price for Google's endpoint (https://openrouter.ai/anthropic/claude-fable-5/providers), collected 2026-11-03.
4Google Vertex AIdirect — Direct price: Google Vertex AI's own price list (https://cloud.google.com/vertex-ai/generative-ai/pricing), collected 2026-11-03.

Composite

7 of 7 inputs · 4 from the model family6 radar axes: DesignArena's two boards share one

82.4

AA Coding v4.xCoding Agent v1.4AA Intelligence (est.)AA AgenticEpoch ECISoftware ECIDesignArenaAA Coding v4.x: percentile 94Coding Agent v1.4: percentile 95AA Intelligence (est.): percentile 96Epoch ECI: percentile 85Software ECI: percentile 85DesignArena: percentile 76

AA Coding v4.x 76.5Coding Agent v1.4 67.2AA Intelligence (est.) 46.0AA Agentic —Epoch ECI 163.0Software ECI 164.4DesignArena 1247/1280

Radar: percentile among all models measured on each input; a gap means not measured.

Benchmark sheet

57 of 319 registered benchmark versions · bars show the percentile among all models measured on each benchmark.

8 of 59 values are Anthropic's own claims (†), not independent measurements; a matching independent result replaces a claim as soon as one exists.

Composite attachments (used in the score, not counted as exact benchmarks):
  • Epoch ECI · attached
  • Software ECI · attached
  • DesignArena Web Apps (agentic) · attached
  • DesignArena Full-Stack · attached
Compare this model →

Agentic

  • AA-AnalystAgent8448.8%

    AA-AnalystAgent published 2026-09-10 → · Published board — Tests analyst tasks using agentic Python execution across fourteen domains.

  • AA-Briefcase911,530 Elo

    AA-Briefcase published 2026-09-10 → · Published board — Tests multi-week professional knowledge-work projects with linked tasks and large source collections.

  • GDPval-AA v2941,631 Elo

    GDPval-AA v2 v2 → · Published board — Tests professional knowledge-work deliverables across occupations using AA's Stirrup harness.

  • Harvey LAB-AA9693.6%

    Harvey LAB-AA published 2026-09-10 → · Published board — Tests legal-work deliverables across practice areas on Harvey's private task set. Artificial Analysis' run, graded by one LLM judge against task rubrics — not the same run or scale as Vals AI's HLAB row.

  • Terminal-Bench Hard (AA)10062.9%

    Terminal-Bench Hard (AA) pinned revision 74221fb → · Published board — Tests a pinned 44-task hard subset of terminal-based work using Terminus 2.

  • Terminal-Bench v2.1 (AA)8984.6%

    Terminal-Bench v2.1 (AA) v2.1 → · Published board — Tests terminal-based work on the 89-task verified refresh using Terminus 2.

  • Terminal-Bench v4.0 (AA)9242.4%

    Terminal-Bench v4.0 (AA) v4.0 → · Published board — Tests terminal-based work on the 66-task release using mini-SWE-agent v2.4.6.

  • ApprenticeBench API (NeoCognition)8545.0%

    ApprenticeBench API (NeoCognition) published 2026-09-14 → · Claude Code — An agent completes the same 100 sequential accounts-payable tasks in the Odoo ERP with diminishing mentoring, using dedicated application API calls instead of the screen interface.

  • ApprenticeBench CUA (NeoCognition)6234.0%

    ApprenticeBench CUA (NeoCognition) published 2026-09-14 → · Claude Code — A computer-use agent operates the Odoo ERP through its screens across 100 sequential accounts-payable tasks with diminishing mentoring.

  • EBR-bench (Earthborne Rangers, Epoch AI)6039.5%

    EBR-bench (Earthborne Rangers, Epoch AI) published 2026-09-18 → · Published board — Learning-capability test: models repeatedly play the obscure campaign board game Earthborne Rangers with note-taking, and the score measures whether results improve across playthroughs.

  • PostTrainBench v1.110041.8%

    PostTrainBench v1.1 → · Claude Code — Agents retrain four small base models (gemma-3-4b-pt, SmolLM3-3B-Base, Qwen3-1.7B-Base, Qwen3-4B-Base) for each of 7 benchmark families; the leaderboard value is the weighted mean over base models × benchmarks, aggregated over the 2–3 listed runs per agent.

  • Terminal-Bench v4.0developer's claim44.5%† self-reported by the developer

    Terminal-Bench v4.0 → · Claude Code — A benchmark to measure and evolve with the frontier of agent work, whose homepage leaderboard reports resolution rate on Terminal-Bench 4.0 tasks.

  • Terminal-Bench v4.0developer's claim42.0%† self-reported by the developer

    Terminal-Bench v4.0 → · Claude Code in --bare mode — A benchmark to measure and evolve with the frontier of agent work, whose homepage leaderboard reports resolution rate on Terminal-Bench 4.0 tasks.

  • Finance Agent v2 (Vals Index v2)7056.3%

    Finance Agent v2 (Vals Index v2) v2 → · Published board — Multi-step financial reasoning tasks.

  • HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)7911.3%

    HLAB — Harvey's Legal Agent Benchmark (Vals Index v2) v2 → · Published board — Long-horizon legal work product creation. The index column is Harvey's Legal Agent Benchmark's own published standalone score, reported as accuracy — not comparable with Artificial Analysis' Harvey LAB-AA row.

  • Terminal-Bench 2.1 (Vals Index v2)8180.5%

    Terminal-Bench 2.1 (Vals Index v2) v2 → · Published board — Command-line interface problem solving; the page states that each index column is that benchmark's own published standalone score, so the runner is not asserted here.

  • Vals Index v2 (Vals AI)8966.0%

    Vals Index v2 (Vals AI) v2 → · Published board — GDP-weighted average of agentic model performance across finance, coding and legal tasks.

Coding

  • Artificial Analysis Coding Agent Index v1.49267.2%

    Artificial Analysis Coding Agent Index v1.4 v1.4 → · Claude Code — The retained AA Coding Agent Index measures coding-agent systems using the earlier three-component implementation.

  • SciCode (AA subproblems) v1.0.19861.0%

    SciCode (AA subproblems) v1.0.1 → · Published board — Tests scientific Python programming with scientist-annotated background information.

  • DeepSWE (Datacurve, via Epoch AI)8169.7%

    DeepSWE (Datacurve, via Epoch AI) published 2026-09-15 → · mini-SWE-agent — Pass rate of coding agents on original, long-horizon software engineering tasks, run by Datacurve with the mini-swe-agent harness.

  • FrontierCode 1.1 Main (Cognition)developer's claim51.6%† self-reported by the developer

    FrontierCode 1.1 Main (Cognition) v1.1 → · Claude Code — Whether a maintainer would merge the agent's pull request, on tasks crafted by open-source maintainers and graded with tests, rubrics and verifiers.

  • SWE-bench Multilingualdeveloper's claim86.6%† self-reported by the developer

    SWE-bench Multilingual published 2026-09-10 → · Published board — A 300-instance SWE-bench variant with tasks from 42 repositories across 9 programming languages.

  • SWE-bench Multimodaldeveloper's claim54.1%† self-reported by the developer

    SWE-bench Multimodal published 2026-09-10 → · Published board — A 480-instance SWE-bench variant whose issue descriptions include visual elements.

  • Code Migration (Vals Index v2 subset)8854.6%

    Code Migration (Vals Index v2 subset) v2 → · Published board — Porting projects to another language, including COBOL modernization.

  • Vibe Code Bench (Vals Index v2)10090.4%

    Vibe Code Bench (Vals Index v2) v2 → · Published board — End-to-end app-building tasks.

  • FrontierCode 1.1 Extended (Cognition)developer's claim63.6%† self-reported by the developer

    FrontierCode 1.1 Extended (Cognition) v1.1 → · Claude Code — Whether a maintainer would merge the agent's pull request, on the full 150-task FrontierCode set crafted by open-source maintainers and graded with tests, rubrics and verifiers.

  • AA Coding Index v4.x · frozen 2026-10-279476.5

    AA Coding Index v4.x · frozen 2026-10-27 → · Published board — Artificial Analysis withdrew the Coding Index with Intelligence Index v5.0 (2026-10-28). These are the last v4.x values, frozen with their own date (2026-10-27).

Efficiency

Instruction-following

Knowledge

Long-context

  • GDP.pdf (AA)8824.0%

    GDP.pdf (AA) published 2026-09-10 → · Published board — Tests professional reasoning over long PDFs with AA document preparation and grading.

  • AA-LCR v1.19482.3%

    AA-LCR v1.1 v1.1 → · Published board — Tests reasoning across multiple long documents with corrected answer keys and grading.

  • MLCR-AA9964.4%

    MLCR-AA published 2026-09-10 → · Published board — Tests medical-record synthesis and reasoning across long, fragmented case documents.

Math

  • FrontierMath Tier 4 v2 (Epoch AI)8890.2%

    FrontierMath Tier 4 v2 (Epoch AI) v2 → · Published board — The hardest, research-level tier of Epoch AI's unpublished FrontierMath problems, run by Epoch AI on its private v2 set.

  • FrontierMath Tiers 1–3 v2 (Epoch AI)8987.0%

    FrontierMath Tiers 1–3 v2 (Epoch AI) v2 → · Published board — Unpublished, expert-written mathematics problems from undergraduate to research level with automatically checkable answers, run by Epoch AI on its private v2 set.

  • ArXivMath 06/2026 (MathArena)7585.4%

    ArXivMath 06/2026 (MathArena) 2026-06 → · Published board — Research-level math problems with a checkable final answer, drawn from arXiv papers submitted in June 2026; each edition is restricted to papers published within the last month to reduce training-data contamination.

  • BrokenArXiv 06/2026 (MathArena)4047.8%

    BrokenArXiv 06/2026 (MathArena) 2026-06 → · Published board — Plausible but false proof statements taken from June 2026 arXiv papers; a model scores by refusing to prove them and saying the statement is false as written.

  • FrontierMath Erdős (Epoch AI)380.0%

    FrontierMath Erdős (Epoch AI) published 2026-09-26 → · Published board — 68 open problems posed or studied by Paul Erdős, formalised in Lean 4, where a model agent must produce a complete Lean proof or disproof that passes the Comparator checker.

Reasoning

  • Chess Puzzles (Epoch AI)8041.0%

    Chess Puzzles (Epoch AI) published 2026-09-18 → · Published board — Best-move selection on 100 novel chess positions generated programmatically by Epoch AI, each with a single Stockfish-verified best move; probes spatial reasoning and planning.

  • LiveBench 2026-06-259483.0

    LiveBench 2026-06-25 → · Published board — A periodically refreshed suite of objectively graded tasks with separate category scores and an overall category mean.

  • Mystery Game Puzzles (Epoch AI)8552.0%

    Mystery Game Puzzles (Epoch AI) published 2026-09-18 → · Published board — Best-move selection on 100 mid-game positions of a well-known game whose identity Epoch deliberately keeps undisclosed, generated programmatically like Chess Puzzles.

Science

Tool-use

  • AutomationBench-AA v1.0.67654.1%

    AutomationBench-AA v1.0.6 → · Published board — Tests multi-app SaaS workflows through REST tools on a held-out AutomationBench split.

  • EnterpriseOps-Gym-AA10051.1%

    EnterpriseOps-Gym-AA published 2026-09-10 → · Published board — Tests enterprise workflows through MCP tools against resettable application environments.

  • τ²-Bench Telecom (AA)9998.5%

    τ²-Bench Telecom (AA) published 2026-09-10 → · Published board — Tests dual-control telecom agents that coordinate tool use with a simulated user.

  • τ³-Banking (AA) v1.0.18238.1%

    τ³-Banking (AA) v1.0.1 → · Published board — Tests banking support agents that retrieve policies and change account state through tools.

  • Excel Modeling Benchmark (Vals Index v2)9573.7%

    Excel Modeling Benchmark (Vals Index v2) v2 → · Published board — Building and editing financial models in spreadsheets.

  • MCPMark Verified5086.6%

    MCPMark Verified verified → · Published board — AI agents complete realistic multi-step tasks through Model Context Protocol servers for Notion, GitHub, a filesystem, Postgres and a Playwright browser, each checked by the task's own verification script; Verified is the maintainers' version-pinned, stabilized 127-task set.

Vision

  • Chartography 100-tasks6734.8%

    Chartography 100-tasks → · Published board — Can a model read the charts professionals rely on — Kaplan-Meier curves, candlesticks, contour maps, Sankey diagrams, Bode plots, wind roses — from the image alone, to an expert-set precision?

  • GDP.pdf 100-tasks8629.8%

    GDP.pdf 100-tasks → · Published board — Can a model answer real professional questions about the PDFs work runs on — manuals, wiring diagrams, filings, forms — so that every rubric criterion an expert wrote is met?

  • Furniture Assembly Benchmark (Epoch AI)4535.8%

    Furniture Assembly Benchmark (Epoch AI) published 2026-09-26 → · Published board — Spatial-reasoning test by Epoch AI: given a photo of a partly or fully assembled IKEA product and its official manual, the model must say whether the build is correct and, if not, name the step with the mistake.

Unusual results

5 threshold-crossing signals flagged · 40 eligible benchmark families

How flags are calculated

Heuristic screen, not statistical significance: benchmark families are correlated and source uncertainty is unknown. Peer evidence needs ≥ 20 independently measured matched configurations from ≥ 10 distinct model families. A flag needs a directed population z-score of magnitude ≥ 1.5 and a gap of ≥ 1.5 from the leave-one-benchmark-family-out mean z in the same direction, over ≥ 5 other benchmark families.

  • unusually weak Vals Index v2 cost per test (Vals AI)
    Why
    Observed: 28.72507 USD
    Peer mean 6.89364 · peer sd 7.83661 · n 38 · families 37
    Directed z -2.786 · baseline z 1.238 · gap -4.023 · profile n 39
    28.72507 USDmeasured· 28.73 USD per rolloutobserved 2026-09-26vals.ai ↗
    Evidence
    Axis: Vals Index v2 cost per test (Vals AI) · v2 · Published board
    Exact value: 28.725071 USD
    Published mean cost: 28.725071 USD per rollout
    Observed: 2026-09-26T04:22:13.903077+00:00 · publication date: not recorded
    Observation id: public:8ff5dba3134e2db88a520a2b
  • unusually strong CritPt (AA) published 2026-09-10
    Why
    Observed: 0.28571 fraction
    Peer mean 0.04412 · peer sd 0.08179 · n 537 · families 370
    Directed z 2.954 · baseline z 1.09 · gap 1.864 · profile n 39
    0.28571 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗
    Evidence
    Axis: CritPt (AA) · published 2026-09-10 · Published board
    Exact value: 0.2857142857 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:cd55210d-358e-4df1-ba9c-9acb5f186cc9:critpt
  • unusually strong Humanity's Last Exam (AA text-only) published 2026-09-10
    Why
    Observed: 0.55468 fraction
    Peer mean 0.15894 · peer sd 0.14559 · n 623 · families 450
    Directed z 2.718 · baseline z 1.096 · gap 1.622 · profile n 39
    0.55468 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗
    Evidence
    Axis: Humanity's Last Exam (AA text-only) · published 2026-09-10 · Published board
    Exact value: 0.554680259499537 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:cd55210d-358e-4df1-ba9c-9acb5f186cc9:hle
  • unusually strong MLCR-AA published 2026-09-10
    Why
    Observed: 0.64444 fraction
    Peer mean 0.18737 · peer sd 0.17142 · n 84 · families 61
    Directed z 2.666 · baseline z 1.098 · gap 1.569 · profile n 39
    0.64444 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗
    Evidence
    Axis: MLCR-AA · published 2026-09-10 · Published board
    Exact value: 0.644444444444444 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:cd55210d-358e-4df1-ba9c-9acb5f186cc9:mlcrOverall
  • unusually strong Terminal-Bench Hard (AA)
    Why
    Observed: 0.62879 fraction
    Peer mean 0.18472 · peer sd 0.16951 · n 432 · families 315
    Directed z 2.62 · baseline z 1.099 · gap 1.521 · profile n 39
    0.62879 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗
    Evidence
    Axis: Terminal-Bench Hard (AA) · pinned revision 74221fb · Published board
    Exact value: 0.628787878787879 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:cd55210d-358e-4df1-ba9c-9acb5f186cc9:terminalbenchHard
Missing coverage · 262 benchmark versions

No result does not mean a zero, or that the model was never tested. Collection failures and disputed versions retain their distinct status.

Variants / reasoning settings

Artificial Analysis snapshot 2026-11-03 · Intelligence Index v5.0 (est. = estimated from v4, not measured) · Coding Index v4.x frozen 2026-10-27 · Data: Artificial Analysis · Data: Epoch AI (CC BY)

VariantAA CodingAA Intelligence
Claude Fable 5 (Max, Opus 4.8 Fallback)76.546.0est.
Fable 5 (low) (with fallback)——
Fable 5 (xhigh) (with fallback)——
Fable 5 (medium) (with fallback)——
Fable 5 (high) (with fallback)——

SUBSCRIPTION PLAN

GitHub Copilot

Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.

Input $10.00 / 1M
Cached input $1.00 / 1M
Cache write $12.50 / 1M
Output $50.00 / 1M
Status GA
Token offers by platform · 6 offers (Adjusted $/task)

Click any underlined price to see how it is estimated and where each input comes from. How we calculate adjusted cost.

6 offers within the active global filters, 3 more outside them; “price n/a” means the provider serves the model but publishes no direct token price we could collect — no other channel's price is shown in its place.

OpenRouter (4)

Prices via OpenRouter — what OpenRouter charges for each provider's endpoint, not the provider's own direct price. Source: openrouter.ai/anthropic/claude-fable-5/providers, collected 2026-11-03.

Azure
Amazon Bedrock
Google
Google

Google Vertex AI (2)

Direct prices from Google Vertex AI's own price list. Source: cloud.google.com/vertex-ai/generative-ai/pricing, collected 2026-11-03.

Google Vertex AI
Google Vertex AI
Claude Fable 5 (Max, Opus 4.8 Fallback) — benchmarks & cost