Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
Context 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 | OpenRouter | $3.00 | $15.00 | ||
| 2 | Amazon Bedrock | OpenRouter | $3.00 | $15.00 | |
| 3 | Azure | OpenRouter | $3.00 | $15.00 | |
| 4 | Google Vertex AI | Google Vertex AI | $3.00 | $15.00 | |
| 5 | AWS Bedrock | AWS Bedrock | $3.30 | $16.50 |
Composite
6 of 7 inputs · 4 from the model family6 radar axes: DesignArena's two boards share one71.8
AA Coding 63.0Coding Agent v1.4 —AA Intelligence 30.5AA Agentic —Epoch ECI 152.3Software ECI 150.0DesignArena 1205/1223
Radar: percentile among all models measured on each input; a gap means not measured.
Benchmark sheet
39 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-AnalystAgent4020.0%
AA-AnalystAgent published 2026-09-10 ↗ · Published board — Tests analyst tasks using agentic Python execution across fourteen domains.
APEX-Agents-AA5928.0%
APEX-Agents-AA published 2026-09-10 ↗ · Published board — Tests professional-service tasks that require agents to produce locally graded deliverables.
AA-Briefcase581,065 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 v2711,295 Elo
GDPval-AA v2 v2 ↗ · Published board — Tests professional knowledge-work deliverables across occupations using AA's Stirrup harness.
Harvey LAB-AA5786.0%
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.
ITBench-AA6939.8%
ITBench-AA published 2026-09-10 ↗ · Published board — Tests root-cause diagnosis from offline Kubernetes incident snapshots.
Terminal-Bench Hard (AA)9653.0%
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)7171.2%
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)583.0%
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)02.0%
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.
Vals Index v2 (Vals AI)2750.6%
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)3351.0%
Finance Agent v2 (Vals Index v2) v2 ↗ · Published board — Multi-step financial reasoning tasks.
Terminal-Bench 2.1 (Vals Index v2)2357.3%
Terminal-Bench 2.1 (Vals Index v2) v2 ↗ · Published board — Command-line interface problem solving, as run by Vals AI.
HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)425.0%
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.
OSWorld 2.0, June 2026 task release (XLANG Lab) v2026.06.24no percentile8.3%
OSWorld 2.0, June 2026 task release (XLANG Lab) v2026.06.24 ↗ · standard — A computer-use agent completes 108 long-horizon, real-world workflows across 31 self-hosted websites and desktop applications, each taking a person about 1.6 hours.
Coding
SciCode (AA subproblems) v1.0.15950.1%
SciCode (AA subproblems) v1.0.1 ↗ · Published board — Tests scientific Python programming with scientist-annotated background information.
Vibe Code Bench (Vals Index v2)2651.5%
Vibe Code Bench (Vals Index v2) v2 ↗ · Published board — End-to-end app-building tasks.
Code Migration (Vals Index v2 subset)5040.1%
Code Migration (Vals Index v2 subset) v2 ↗ · Published board — Porting projects to another language, including COBOL modernization.
FrontierCode 1.1 Main (Cognition) v1.1no percentile24.3%
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.
AA Coding Index7363.0
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
ApprenticeBench CUA cost per task (NeoCognition)77$14.68
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.
Vals Index v2 cost per test (Vals AI)27$8.40
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$2.90
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.
Instruction-following
IFBench (AA single-turn)6856.6%
IFBench (AA single-turn) published 2026-09-10 ↗ · Published board — Tests precise single-turn instructions with deterministic rule checks.
Knowledge
Humanity's Last Exam (AA text-only)8533.6%
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 Index8912.2
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)4838.5%
Legal Research Bench (Vals Index v2) v2 ↗ · Published board — Case and statute research with citation-backed answers.
SimpleQA Verified (run by Epoch AI)632.8%
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)6515.8%
GDP.pdf (AA) published 2026-09-10 ↗ · Published board — Tests professional reasoning over long PDFs with AA document preparation and grading.
AA-LCR v1.18980.0%
AA-LCR v1.1 v1.1 ↗ · Published board — Tests reasoning across multiple long documents with corrected answer keys and grading.
MLCR-AA7824.4%
MLCR-AA published 2026-09-10 ↗ · Published board — Tests medical-record synthesis and reasoning across long, fragmented case documents.
Reasoning
Chess Puzzles (Epoch AI)153.0%
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.
AA Intelligence Index8830.5
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.
Science
CritPt (AA)753.1%
CritPt (AA) published 2026-09-10 ↗ · Published board — Tests research-level physics reasoning with Python, symbolic and numerical answers.
GPQA Diamond (AA)8487.5%
GPQA Diamond (AA) published 2026-09-10 ↗ · Published board — Tests graduate-level biology, physics and chemistry knowledge on the Diamond subset.
GPQA Diamond (Epoch AI run)2678.8%
GPQA Diamond (Epoch AI run) published 2026-09-18 ↗ · Published board — Epoch AI's own inspect-ai runs of GPQA Diamond, the 198-question graduate-level science multiple-choice set.
Tool-use
AutomationBench-AA v1.0.64720.1%
AutomationBench-AA v1.0.6 ↗ · Published board — Tests multi-app SaaS workflows through REST tools on a held-out AutomationBench split.
τ²-Bench Telecom (AA)6775.7%
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) v1.0.17534.4%
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)4160.2%
Excel Modeling Benchmark (Vals Index v2) v2 ↗ · Published board — Building and editing financial models in spreadsheets.
Vision
MMMU Pro (AA)6273.3%
MMMU Pro (AA) published 2026-09-10 ↗ · Published board — Tests multimodal understanding using challenging ten-option questions.
Unusual results
1 threshold-crossing signal flagged · 35 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 strong Terminal-Bench Hard (AA)
Why
Observed: 0.5303 fractionPeer mean 0.18472 · peer sd 0.16951 · n 432 · families 315Directed z 2.039 · baseline z 0.242 · gap 1.797 · profile n 340.5303 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗Evidence
Axis: Terminal-Bench Hard (AA) · pinned revision 74221fb · Published boardExact value:0.53030303030303fractionObserved: 2026-09-10T21:47:16.627Z · publication date: not recordedObservation id:aa:df8d14e0-3997-4e4d-b4ad-9c047acc9c69:terminalbenchHard
Missing coverage · 104 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)
| Variant | AA Coding | AA Intelligence |
|---|---|---|
| Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) | 63.0 | 30.5 |
| Claude Sonnet 4.6 (Non-reasoning, High Effort) | — | 24.7 |
| Claude Sonnet 4.6 (Non-reasoning, Low Effort) | — | 23.3 |
| Claude Sonnet 4.6 (Adaptive Reasoning, Medium Effort) | — | — |
SUBSCRIPTION PLAN
GitHub Copilot
Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.
Legacy annual Pro/Pro+ request billing only.
GitHub marks this multiplier as subject to change.
Token offers by platform · 11 offers (Adjusted $/task)
Click any underlined price to see how it is estimated and where each input comes from. How we calculate adjusted cost.
11 offers within the active global filters; “—” means the catalog is active but no public token price is available.
OpenRouter (7)
| globalgoogle-vertex/global | $3.00 raw in $/1M | $15.00 raw out $/1M | ||
| Amazon Bedrock | globalamazon-bedrock/global | $3.00 raw in $/1M | $15.00 raw out $/1M | |
| Azure | globalazure/global | $3.00 raw in $/1M | $15.00 raw out $/1M | |
| Amazon Bedrock | globalamazon-bedrock/us | $3.30 raw in $/1M | $16.50 raw out $/1M | |
| Amazon Bedrock | euamazon-bedrock/eu-west-1EU | $3.30 raw in $/1M | $16.50 raw out $/1M | |
| eugoogle-vertex/europeEU | $3.30 raw in $/1M | $16.50 raw out $/1M | ||
| globalgoogle-vertex/us-east5 | $3.30 raw in $/1M | $16.50 raw out $/1M |
Google Vertex AI (2)
| Google Vertex AI | global | $3.00 raw in $/1M | $15.00 raw out $/1M | |
| Google Vertex AI | europe-west1EU | $3.30 raw in $/1M | $16.50 raw out $/1M |
AWS Bedrock (1)
| AWS Bedrock | eu-central-1 (EU cross-region inference profile)EU | $3.30 raw in $/1M | $16.50 raw out $/1M |
T-Systems LLM Hub (1)
| T-Systems LLM Hub | euEU | $3.40 raw in $/1M | $17.02 raw out $/1M |