Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback)
Output 71 tokens/sFirst token 146 sContext 1M tokens
Top 5 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.
| # | Provider | Platform | Raw input $/1M | Raw output $/1M | Adjusted $/task |
|---|---|---|---|---|---|
| 1 | Amazon Bedrock | OpenRouter | $10.00 | $50.00 | |
| 2 | OpenRouter | $10.00 | $50.00 | ||
| 3 | Azure | OpenRouter | $10.00 | $50.00 | |
| 4 | AWS Bedrock | AWS Bedrock | $10.00 | $50.00 | |
| 5 | Google Vertex AI | Google Vertex AI | $10.00 | $50.00 |
Composite
7 of 7 inputs · 4 from the model family6 radar axes: DesignArena's two boards share one98.5
includes −0.5 for its Benchmaxxing signal (from 99.0; why, switch off in Options)
AA Coding 81.6Coding Agent v1.4 70.4AA Intelligence 53.4AA Agentic —Epoch ECI 164.5Software ECI 167.4DesignArena 1336/1336
Radar: percentile among all models measured on each input; a gap means not measured.
Benchmark sheet
52 of 140 registered benchmark versions · bars show the percentile among all models measured on each benchmark.
- Epoch ECI · attached
- Software ECI · attached
- DesignArena Web Apps (agentic) · attached
- DesignArena Full-Stack · attached
Agentic
AA-AnalystAgent9757.5%
AA-AnalystAgent published 2026-09-10 ↗ · Published board — Tests analyst tasks using agentic Python execution across fourteen domains.
AA-Briefcase1001,662 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 v21001,764 Elo
GDPval-AA v2 v2 ↗ · Published board — Tests professional knowledge-work deliverables across occupations using AA's Stirrup harness.
Harvey LAB-AA8893.0%
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 v2.1 (AA)10091.4%
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)9752.0%
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 CUA (NeoCognition)10072.0%
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.
ApprenticeBench API (NeoCognition)10070.0%
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.
Vals Index v2 (Vals AI)10068.8%
Vals Index v2 (Vals AI) v2 ↗ · Published board — GDP-weighted average of agentic model performance across finance, coding and legal tasks.
Finance Agent v2 (Vals Index v2)8358.9%
Finance Agent v2 (Vals Index v2) v2 ↗ · Published board — Multi-step financial reasoning tasks.
HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)536.7%
HLAB — Harvey's Legal Agent Benchmark (Vals Index v2) v2 ↗ · Published board — Long-horizon legal work product creation. Vals AI's run of Harvey's Legal Agent Benchmark, reported as accuracy — not comparable with Artificial Analysis' Harvey LAB-AA row.
EBR-bench (Earthborne Rangers, Epoch AI)9257.1%
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.
Coding
Artificial Analysis Coding Agent Index v1.410070.4%
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.
Artificial Analysis Coding Agent Index v1.510062.2%
Artificial Analysis Coding Agent Index v1.5 v1.5 ↗ · Claude Code — Measures coding-agent systems on DeepSWE v1.1, Terminal-Bench 4.0 and SWE-Atlas-QnA.
SciCode (AA subproblems) v1.0.110063.1%
SciCode (AA subproblems) v1.0.1 ↗ · Published board — Tests scientific Python programming with scientist-annotated background information.
SWE-bench Multilingualno percentile89.1%
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 Multimodalno percentile54.7%
SWE-bench Multimodal published 2026-09-10 ↗ · Published board — A 480-instance SWE-bench variant whose issue descriptions include visual elements.
CursorBench 4.0 (Cursor) v4.0no percentile51.8%
CursorBench 4.0 (Cursor) v4.0 ↗ · Published board — Agent evaluation on ambiguous, multi-file tasks drawn from real Cursor sessions.
Vibe Code Bench (Vals Index v2)9790.3%
Vibe Code Bench (Vals Index v2) v2 ↗ · Published board — End-to-end app-building tasks.
Code Migration (Vals Index v2 subset)9760.0%
Code Migration (Vals Index v2 subset) v2 ↗ · Published board — Porting projects to another language, including COBOL modernization.
FrontierCode 1.1 Main (Cognition) v1.1no percentile50.3%
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.
VulcanBench Frontier v4no percentile91.8%
VulcanBench Frontier v4 v4 ↗ · Claude Code — 23 behavioural-reconstruction tasks: the model must repair a replacement implementation of a legacy program until hidden tests confirm it reproduces the program’s real drift from its written spec; the combined score weights functional correctness 50%, lint/complexity 8.5%, security 8.5% and judged Code quality 33%.
KernelBench-CUDA: GLM-5.2 Fused MoE (RTX PRO 6000) vrtx-pro-60006710.2
KernelBench-CUDA: GLM-5.2 Fused MoE (RTX PRO 6000) vrtx-pro-6000 ↗ · Published board — Fuse GLM-5.2’s MoE forward pass (256+shared experts, top-8 routing) as one CUDA kernel over a frozen shape sweep; Triton, vLLM and other DSLs are banned — CUDA/PTX/CUTLASS only.
KernelBench-CUDA: DeepSeek NSA (RTX PRO 6000) vrtx-pro-6000100106.3
KernelBench-CUDA: DeepSeek NSA (RTX PRO 6000) vrtx-pro-6000 ↗ · Published board — Implement DeepSeek’s Native Sparse Attention block (block top-n routing + sparse attention) as a CUDA kernel over a frozen shape sweep.
KernelBench-CUDA: MegaQwen Decode (RTX PRO 6000) vrtx-pro-6000806.43
KernelBench-CUDA: MegaQwen Decode (RTX PRO 6000) vrtx-pro-6000 ↗ · Published board — Improve the known MegaQwen CUDA megakernel geometry for decode-only token throughput at context lengths 2k–128k; prefill is untimed.
KernelBench-CUDA: Grid MinGRU SPS (RTX PRO 6000) vrtx-pro-60006770.9
KernelBench-CUDA: Grid MinGRU SPS (RTX PRO 6000) vrtx-pro-6000 ↗ · Published board — Non-LLM RL simulation: maximise simulation steps per second for a grid world with a MinGRU agent (roofline anchored at 150M peak SPS); fusion optional.
FrontierSWE v210056.3%
FrontierSWE v2 v2 ↗ · proximus — 34 hand-written real-world software tasks: each model attempts every task in its own CLI agent harness for 5 trials per task under a 20-hour budget, scored by the site’s own review protocol; the headline value is mean@5 in percent with best@5/worst@5 bounds.
AA Coding Index10081.6
AA Coding Index published 2026-09-19 ↗ · Published board — Artificial Analysis publishes this board without a version number; we keep the date each result was retained.
Efficiency
CursorBench 4.0 cost per task (Cursor) v4.0no percentile$17.28
CursorBench 4.0 cost per task (Cursor) v4.0 ↗ · Published board — Cursor's published USD cost per task for each CursorBench 4.0 model-effort configuration.
ApprenticeBench CUA cost per task (NeoCognition)62$18.23
ApprenticeBench CUA cost per task (NeoCognition) published 2026-09-14 ↗ · Claude Code — ApprenticeBench's published USD cost per task for each CUA model-harness-effort configuration on the 100-task accounts-payable job.
ApprenticeBench API cost per task (NeoCognition)8$6.95
ApprenticeBench API cost per task (NeoCognition) published 2026-09-14 ↗ · Claude Code — ApprenticeBench's published USD cost per task for each API-board model-harness-effort configuration on the 100-task accounts-payable job.
Vals Index v2 cost per test (Vals AI)0$28.92
Vals Index v2 cost per test (Vals AI) v2 ↗ · Published board — Vals AI's published USD cost per test for each model on the Vals Index v2.
FrontierCode 1.1 Main cost per rollout (Cognition) v1.1no percentile$12.83
FrontierCode 1.1 Main cost per rollout (Cognition) v1.1 ↗ · claude-code — Cognition's published mean USD spend per rollout for each FrontierCode 1.1 Main model and reasoning effort.
Knowledge
Humanity's Last Exam (AA text-only)10059.1%
Humanity's Last Exam (AA text-only) published 2026-09-10 ↗ · Published board — Tests expert-level knowledge on AA's text-only Humanity's Last Exam subset.
AA-Omniscience Index10043.5
AA-Omniscience Index published 2026-09-10 ↗ · Published board — Tests factual reliability while rewarding correct answers and penalizing hallucinations.
Legal Research Bench (Vals Index v2)9755.3%
Legal Research Bench (Vals Index v2) v2 ↗ · Published board — Case and statute research with citation-backed answers.
SimpleQA Verified (run by Epoch AI)9770.8%
SimpleQA Verified (run by Epoch AI) published 2026-09-16 ↗ · Published board — Short fact-seeking questions answered without tools, testing whether a model knows a fact rather than guessing; Google DeepMind's cleaned version of OpenAI's SimpleQA, run by Epoch AI.
Long-context
GDP.pdf (AA)9126.2%
GDP.pdf (AA) published 2026-09-10 ↗ · Published board — Tests professional reasoning over long PDFs with AA document preparation and grading.
AA-LCR v1.110085.3%
AA-LCR v1.1 v1.1 ↗ · Published board — Tests reasoning across multiple long documents with corrected answer keys and grading.
MLCR-AA10071.1%
MLCR-AA published 2026-09-10 ↗ · Published board — Tests medical-record synthesis and reasoning across long, fragmented case documents.
Math
ArXivMath 06/2026 (MathArena) v2026-069091.0%
ArXivMath 06/2026 (MathArena) v2026-06 ↗ · Published board — Research-level math problems with a checkable final answer, drawn from arXiv papers submitted in June 2026, so they postdate most training data.
BrokenArXiv 06/2026 (MathArena) v2026-068084.7%
BrokenArXiv 06/2026 (MathArena) v2026-06 ↗ · 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 Tiers 1–3 v2 (Epoch AI)9790.2%
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.
FrontierMath Tier 4 v2 (Epoch AI)8387.8%
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.
Reasoning
Chess Puzzles (Epoch AI)8847.0%
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.
Mystery Game Puzzles (Epoch AI)9358.0%
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.
AA Intelligence Index10053.4
AA Intelligence Index published 2026-09-19 ↗ · Published board — Artificial Analysis publishes this board without a version number; we keep the date each result was retained.
Safety/Alignment
BullshitBench V1 (clear pushback)8660.0%
BullshitBench V1 (clear pushback) published 2026-09-10 ↗ · Published board — Whether a model rejects the broken premise of 55 deliberately nonsensical prompts (V1 question set) instead of answering them confidently.
BullshitBench V2 (clear pushback)7964.0%
BullshitBench V2 (clear pushback) published 2026-09-10 ↗ · Published board — Whether a model rejects the broken premise of 100 deliberately nonsensical prompts (V2 question set) instead of answering them confidently.
Science
CritPt (AA)9829.7%
CritPt (AA) published 2026-09-10 ↗ · Published board — Tests research-level physics reasoning with Python, symbolic and numerical answers.
GPQA Diamond (AA)9893.7%
GPQA Diamond (AA) published 2026-09-10 ↗ · Published board — Tests graduate-level biology, physics and chemistry knowledge on the Diamond subset.
Tool-use
AutomationBench-AA v1.0.69059.4%
AutomationBench-AA v1.0.6 ↗ · Published board — Tests multi-app SaaS workflows through REST tools on a held-out AutomationBench split.
τ³-Banking (AA) v1.0.19647.2%
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)10076.7%
Excel Modeling Benchmark (Vals Index v2) v2 ↗ · Published board — Building and editing financial models in spreadsheets.
Unusual results
5 threshold-crossing signals flagged · 33 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: 28.91624 USDPeer mean 7.03053 · peer sd 7.84824 · n 31 · families 30Directed z -2.789 · baseline z 1.605 · gap -4.393 · profile n 3228.91624 USDmeasuredobserved 2026-09-18vals.ai ↗Evidence
Axis: Vals Index v2 cost per test (Vals AI) · v2 · Published boardExact value:28.916238USDObserved: 2026-09-18T13:00:43.826192+00:00 · publication date: not recordedObservation id:public:04c8469c757a0176ac395d99 - unusually strong CritPt (AA) published 2026-09-10
Why
Observed: 0.29714 fractionPeer mean 0.03928 · peer sd 0.07596 · n 522 · families 369Directed z 3.395 · baseline z 1.412 · gap 1.983 · profile n 320.29714 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗Evidence
Axis: CritPt (AA) · published 2026-09-10 · Published boardExact value:0.297142857142857fractionObserved: 2026-09-10T21:47:16.627Z · publication date: not recordedObservation id:aa:3e87c73e-a257-495e-9730-367a66229811:critpt - unusually strong AA Intelligence Index published 2026-09-19
Why
Observed: 53.4 pointsPeer mean 15.94317 · peer sd 11.48893 · n 644 · families 483Directed z 3.26 · baseline z 1.416 · gap 1.844 · profile n 3253.4 pointsmeasuredobserved 2026-09-19artificialanalysis.ai ↗Evidence
Axis: AA Intelligence Index · published 2026-09-19 · Published boardExact value:53.4pointsObserved: 2026-09-19 · publication date: not recordedObservation id:legacy:aa_intelligence_index:claude-fable-5.1::maxProtocol: inspect · filedata/raw/artificialanalysis.json - unusually strong Humanity's Last Exam (AA text-only) published 2026-09-10
Why
Observed: 0.59129 fractionPeer mean 0.15283 · peer sd 0.14006 · n 608 · families 449Directed z 3.13 · baseline z 1.42 · gap 1.711 · profile n 320.59129 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗Evidence
Axis: Humanity's Last Exam (AA text-only) · published 2026-09-10 · Published boardExact value:0.591288229842447fractionObserved: 2026-09-10T21:47:16.627Z · publication date: not recordedObservation id:aa:3e87c73e-a257-495e-9730-367a66229811:hle - unusually strong MLCR-AA published 2026-09-10
Why
Observed: 0.71111 fractionPeer mean 0.18737 · peer sd 0.17142 · n 84 · families 61Directed z 3.055 · baseline z 1.422 · gap 1.633 · profile n 320.71111 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗Evidence
Axis: MLCR-AA · published 2026-09-10 · Published boardExact value:0.711111111111111fractionObserved: 2026-09-10T21:47:16.627Z · publication date: not recordedObservation id:aa:3e87c73e-a257-495e-9730-367a66229811:mlcrOverall
Missing coverage · 91 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 2026-09-19 · Data: Artificial Analysis · Data: Epoch AI (CC BY)
SUBSCRIPTION PLAN
GitHub Copilot
Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.
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; “—” means the catalog is active but no public token price is available.
OpenRouter (3)
| Amazon Bedrock | globalamazon-bedrock | $10.00 raw in $/1M | $50.00 raw out $/1M | |
| globalgoogle-vertex/global | $10.00 raw in $/1M | $50.00 raw out $/1M | ||
| Azure | globalazure | $10.00 raw in $/1M | $50.00 raw out $/1M |
AWS Bedrock (1)
| AWS Bedrock | global | $10.00 raw in $/1M | $50.00 raw out $/1M |
Google Vertex AI (2)
| Google Vertex AI | global | $10.00 raw in $/1M | $50.00 raw out $/1M | |
| Google Vertex AI | euEU | $11.00 raw in $/1M | $55.00 raw out $/1M |