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GPT-5.5 (xhigh)

deprecated by benchmark source
OpenAI · released 2026-04-23 · 12 offers

Context 922K tokens

Top 5 cheapest providers (Adjusted $/task)

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Within the active global provider, residency and confidentiality filters.

#ProviderAdjusted $/task
1Azure
2Amazon Bedrock
3Azure AI Foundry
4AWS Bedrock
5T-Systems LLM Hub

Composite

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

80.5

AA CodingCoding Agent v1.4AA IntelligenceAA AgenticEpoch ECISoftware ECIDesignArenaAA Coding: percentile 90Coding Agent v1.4: percentile 67AA Intelligence: percentile 93Epoch ECI: percentile 85Software ECI: percentile 70DesignArena: percentile 25

AA Coding 74.9Coding Agent v1.4 61.0AA Intelligence 38.6AA Agentic Epoch ECI 159.1Software ECI 158.9DesignArena 1132/1099

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

Benchmark sheet

54 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-AnalystAgent8750.0%

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

  • APEX-Agents-AA9037.7%

    APEX-Agents-AA published 2026-09-10 · Published board — Tests professional-service tasks that require agents to produce locally graded deliverables.

  • AA-Briefcase631,136 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 v2771,396 Elo

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

  • Harvey LAB-AA6086.3%

    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-AA8845.8%

    ITBench-AA published 2026-09-10 · Published board — Tests root-cause diagnosis from offline Kubernetes incident snapshots.

  • Terminal-Bench Hard (AA)9860.6%

    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)8784.3%

    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)7814.6%

    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)6820.0%

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

  • ApprenticeBench API (NeoCognition)5027.0%

    ApprenticeBench API (NeoCognition) published 2026-09-14 · Codex — 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)5357.4%

    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)3751.8%

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

  • HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)333.8%

    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 percentile13.0%

    OSWorld 2.0, June 2026 task release (XLANG Lab) v2026.06.24 · batch tool — 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.

  • EBR-bench (Earthborne Rangers, Epoch AI)6234.3%

    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 v1.1no percentile27.2%

    PostTrainBench v1.1 · Codex CLI — 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.

Coding

  • Artificial Analysis Coding Agent Index v1.47761.0%

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

  • SciCode (AA subproblems) v1.0.18455.8%

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

  • Vibe Code Bench (Vals Index v2)4269.8%

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

  • Code Migration (Vals Index v2 subset)7545.5%

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

  • FrontierCode 1.1 Main (Cognition) v1.1no percentile43.0%

    FrontierCode 1.1 Main (Cognition) v1.1 · codex — 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)6567.0%

    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.

  • SWE Atlas Codebase QnA (Scale AI)4445.4%

    SWE Atlas Codebase QnA (Scale AI) published 2026-09-15 · Published board — How well a coding agent answers deep questions about a real codebase, graded against expert rubrics.

  • SWE Atlas Test Writing (Scale AI)3342.6%

    SWE Atlas Test Writing (Scale AI) published 2026-09-15 · Published board — Whether a coding agent writes production-grade tests for real repositories, graded with rubrics and LLM judges.

  • SWE Atlas Refactoring (Scale AI)4044.8%

    SWE Atlas Refactoring (Scale AI) published 2026-09-15 · Published board — Whether a coding agent restructures production code while preserving its behaviour, graded by tests and rubrics.

  • GSO software optimization, Opt@1 (UC Berkeley) vopt1-1028940.2%

    GSO software optimization, Opt@1 (UC Berkeley) vopt1-102 · OpenHands — An agent gets a real codebase and a performance test and must make the code as fast as an expert developer's optimization; 102 tasks across 10 codebases and 5 languages.

  • VulcanBench Frontier v45078.5%

    VulcanBench Frontier v4 v4 · Codex — 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%.

  • AA Coding Index9074.9

    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

Instruction-following

Knowledge

Long-context

  • GDP.pdf (AA)8121.2%

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

  • AA-LCR v1.19984.3%

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

  • MLCR-AA6317.8%

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

Math

  • ArXivMath 06/2026 (MathArena) v2026-066083.6%

    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) v2026-067069.4%

    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)7985.3%

    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)6972.5%

    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

  • Mystery Game Puzzles (Epoch AI)8956.0%

    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 Index9338.6

    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

Vision

Unusual results

2 threshold-crossing signals flagged · 38 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 CritPt (AA) published 2026-09-10
    Why
    Observed: 0.27143 fraction
    Peer mean 0.03928 · peer sd 0.07596 · n 522 · families 369
    Directed z 3.056 · baseline z 0.907 · gap 2.15 · profile n 37
    0.27143 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
    Evidence
    Axis: CritPt (AA) · published 2026-09-10 · Published board
    Exact value: 0.271428571428571 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:1f054429-397e-4fdb-9e71-67bc92c1735e:critpt
  • unusually strong Terminal-Bench Hard (AA)
    Why
    Observed: 0.60606 fraction
    Peer mean 0.18472 · peer sd 0.16951 · n 432 · families 315
    Directed z 2.486 · baseline z 0.922 · gap 1.563 · profile n 37
    0.60606 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
    Evidence
    Axis: Terminal-Bench Hard (AA) · pinned revision 74221fb · Published board
    Exact value: 0.606060606060606 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:1f054429-397e-4fdb-9e71-67bc92c1735e:terminalbenchHard
Missing coverage · 89 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
GPT-5.5 (xhigh)74.938.6
GPT-5.5 (high)71.637.3
GPT-5.5 (medium)71.534.2
GPT-5.5 (low)60.930.7
GPT-5.5 (Non-reasoning)56.523.2

SUBSCRIPTION PLAN

GitHub Copilot

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

Input $5.00 / 1M
Cached input $0.500 / 1M
Output $30.00 / 1M
Status GA

Legacy annual Pro/Pro+ request billing only.

Multiplier 57.00×
Effective cost $2.280 / request

Legacy annual-plan multiplier.

Token offers by platform · 10 offers (Adjusted $/task)

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

OpenRouter (4)

Azure
Azure
Azure
Amazon Bedrock

Azure AI Foundry (4)

Azure AI Foundry
Azure AI Foundry
Azure AI Foundry
Azure AI Foundry

AWS Bedrock (1)

AWS Bedrock

T-Systems LLM Hub (1)

T-Systems LLM Hub
GPT-5.5 (xhigh) — benchmarks & cost | Benchmark Heaven