← All models

Claude Sonnet 5 (Adaptive Reasoning, Max Effort)

★ featured
Anthropic · released 2026-06-30 · 15 offers

Output 82 tokens/sFirst token 118 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.

#ProviderAdjusted $/task
1Google
2Amazon Bedrock
3Azure
4Google Vertex AI
5AWS Bedrock

Composite

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

83.0

AA CodingCoding Agent v1.4AA IntelligenceAA AgenticEpoch ECISoftware ECIDesignArenaAA Coding: percentile 83AA Intelligence: percentile 93Epoch ECI: percentile 70Software ECI: percentile 58DesignArena: percentile 68

AA Coding 71.5Coding Agent v1.4 AA Intelligence 38.4AA Agentic Epoch ECI 156.2Software ECI 154.9DesignArena 1260/1260

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

Benchmark sheet

45 of 140 registered benchmark versions · bars show the percentile among all models measured on each benchmark.

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-AnalystAgent7746.3%

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

  • AA-Briefcase791,355 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 v2871,501 Elo

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

  • Harvey LAB-AA6990.1%

    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)8280.5%

    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)7614.1%

    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)2316.0%

    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)5426.0%

    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)6759.6%

    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)5053.9%

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

  • Terminal-Bench 2.1 (Vals Index v2)6374.5%

    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.

  • τ^τ-bench (Hyper-τ), release v1 (Sierra) vrelease-v1no percentile14.9%

    τ^τ-bench (Hyper-τ), release v1 (Sierra) vrelease-v1 · Claude Code — A coding agent builds a working customer-service agent from realistic evidence (policies, transcripts, recordings, a client API), and is scored by how well that agent then serves simulated customers on 53 held-out tasks.

Coding

  • SciCode (AA subproblems) v1.0.17754.3%

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

  • CursorBench 4.0 (Cursor) v4.0no percentile34.1%

    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)7781.3%

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

  • Code Migration (Vals Index v2 subset)6644.1%

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

  • FrontierCode 1.1 Main (Cognition) v1.1no percentile42.4%

    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.

  • DeepSWE (Datacurve, via Epoch AI)3953.8%

    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.

  • AA Coding Index8371.5

    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

Knowledge

Long-context

  • GDP.pdf (AA)6013.2%

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

  • AA-LCR v1.19582.0%

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

  • MLCR-AA9355.0%

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

Math

  • FrontierMath Tiers 1–3 v2 (Epoch AI)4865.6%

    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)3829.3%

    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)2916.0%

    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)6935.0%

    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 Index9338.4

    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

Science

Tool-use

  • AutomationBench-AA v1.0.66136.5%

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

  • EnterpriseOps-Gym-AA7444.7%

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

  • τ³-Banking (AA) v1.0.18137.3%

    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)6666.3%

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

Vision

Unusual results

1 threshold-crossing signal flagged · 37 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 MLCR-AA published 2026-09-10
    Why
    Observed: 0.55 fraction
    Peer mean 0.18737 · peer sd 0.17142 · n 84 · families 61
    Directed z 2.116 · baseline z 0.604 · gap 1.512 · profile n 36
    0.55 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
    Evidence
    Axis: MLCR-AA · published 2026-09-10 · Published board
    Exact value: 0.55 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:23c86e4a-c769-43c0-a056-79e3cd15834f:mlcrOverall
Missing coverage · 98 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)

VariantAA CodingAA Intelligence
Claude Sonnet 5 (Adaptive Reasoning, Max Effort)71.538.4
Claude Sonnet 5 (Non-reasoning, High Effort)66.428.9
Claude Sonnet 5 (Adaptive Reasoning, Xhigh Effort)34.7
Claude Sonnet 5 (Adaptive Reasoning, High Effort)32.0
Claude Sonnet 5 (Adaptive Reasoning, Medium Effort)28.4
Claude Sonnet 5 (Adaptive Reasoning, Low Effort)24.7

SUBSCRIPTION PLAN

GitHub Copilot

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

Input $2.00 / 1M
Cached input $0.200 / 1M
Cache write $2.50 / 1M
Output $10.00 / 1M
Status GA
Token offers by platform · 12 offers (Adjusted $/task)

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

12 offers within the active global filters; “—” means the catalog is active but no public token price is available.

OpenRouter (8)

Google
Amazon Bedrock
Azure
Amazon Bedrock
Google
Amazon Bedrock
Google
Azure

Google Vertex AI (2)

Google Vertex AI
Google Vertex AI

AWS Bedrock (1)

AWS Bedrock

T-Systems LLM Hub (1)

T-Systems LLM Hub
Claude Sonnet 5 (Adaptive Reasoning, Max Effort) — benchmarks & cost | Benchmark Heaven