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Evals

Last Accessed: 2026-10-07 (Claude Haiku 5.5 additions; earlier rows retain their snapshot dates below). Key:
  • ∅=source null/absent, not zero.
  • Values are rounded to 1 decimal from the public model leaderboard and Intelligence Index pages. Each row includes the model’s release date.
  • PDF, MLCR, Open, Ent, and Analyst are generally published only for models shown on the default Intelligence Index chart. The GPT-6.1 Sol model page publishes PDF at all five efforts and MLCR at max; other missing values remain ∅.
  • A chart label may show the nearest integer of idx.
  • idx: Intelligence Index points, aggregate performance across knowledge, reasoning, coding, and agentic work.
  • Brief: AA-Briefcase, long-horizon business knowledge work producing spreadsheets, presentations, and memos; normalized Elo clamp((Elo-500)/2000)*100.
  • Gn: GDPval-AA, economically valuable professional work across occupations; normalized Elo clamp((Elo-500)/2000)*100.
  • Omni: Omniscience Index, factual knowledge reliability, rewarding correct answers and penalizing incorrect guesses without penalizing abstention; -100 to 100.
  • Open: Openness Index points, model availability and transparency of training data and methodology, not task-solving ability.
  • Other score columns are percent.
  • OA: Omniscience accuracy, factual recall across knowledge domains.
  • ONH: Omniscience non-hallucination, avoiding incorrect guesses when unable to answer fully; the ONH rate (partial+notattempted)/(incorrect+partial+notattempted), not 1 minus the hallucination rate.
  • PDF: GDP.pdf All-pass, reasoning over long professional documents while satisfying every task-specific criterion.
  • Auto: AutomationBench-AA, completing multi-step SaaS workflows without guardrail violations.
  • TB4: Terminal-Bench 4.0, agentic coding and terminal work across software engineering, systems administration, data processing, model training, and security.
  • TB21: Terminal-Bench 2.1, an earlier terminal-task suite covering coding, systems administration, data processing, model training, and security.
  • TBh: Terminal-Bench Hard, the legacy hard terminal-task subset testing coding, systems administration, and data processing.
  • Sci: SciCode, writing scientific Python code to solve scientist-curated research problems, graded by execution tests.
  • HLE: Humanity’s Last Exam, expert-level academic knowledge and reasoning across mathematics, sciences, and humanities.
  • Crit: CritPt, research-level physics reasoning.
  • LCR: AA-LCR, extracting, reasoning about, and synthesizing information across long documents.
  • GPQA: GPQA Diamond, graduate-level scientific reasoning in biology, physics, and chemistry.
  • IF: IFBench, precise instruction following under verifiable output constraints.
  • MMMU: MMMU-Pro, multimodal understanding and visual reasoning across academic disciplines.
  • tau2: τ²-Bench Telecom, conversational tool use and coordination with a simulated user to resolve telecom support issues.
  • tauB: Banking tool-use benchmark, knowledge retrieval and multi-step customer-support workflows.
  • Analyst: AA-AnalystAgent, end-to-end quantitative analysis of real-world spreadsheets and documents.
  • ITB: ITBench SRE, identifying Kubernetes incident root causes from alerts, events, traces, and topology.
  • Apex: APEX-Agents, long-horizon, cross-application work in investment banking, consulting, and corporate law.
  • AIME: AIME 2025, competition-level mathematical problem solving.
  • LCB: LiveCodeBench, generating correct code for recent competitive programming problems.
  • Harvey: Harvey LAB-AA, producing legal deliverables from case documents, graded against task-specific criteria.
  • MLCR: Medical Long Context Reasoning overall, synthesizing long, fragmented medical records for healthcare and insurance case review.
  • Ent: EnterpriseOps-Gym-AA, stateful, multi-step business workflows using tools, graded on the resulting database state.

Artificial Analysis Intelligence Index v4.3.2

Table: the 673 models on the Artificial Analysis leaderboard as of 2026-09-25, including models with no published scores, plus 5 Claude Sonnet 5.5 rows accessed 2026-09-28, 5 GPT-6.1 Sol rows accessed 2026-09-29, and 5 Claude Haiku 5.5 rows accessed 2026-10-07. GPT-6.1 Sol’s five effort rows were accessed 2026-09-29 from the leaderboard and model page. The release date is not an evaluation run date; run dates are unpublished. AA’s methodology uses mini-swe-agent for TB4, all 66 tasks, pass@1 averaged over three repeats, a 500-step cap and no context compaction. Claude Haiku 5.5 rows were accessed 2026-10-07 from the leaderboard and the max, xhigh, high, medium, and low model pages. AA labels these configurations “with fallback”. All five pages publish non-estimated indices and twelve component measurements in their public structured data, although their text rendering says “Not publicly available”. Evaluation run dates are unpublished; the release date is not an evaluation run date. These are AA measurements, not Anthropic’s launch-table results. Missing ONH remains unknown; the published hallucination rate is a different metric.

DeepSWE v1.1

Source: DeepSWE leaderboard by Datacurve, 113 tasks, updated 2026-09-22, accessed 2026-09-25. Every model runs on mini-swe-agent; each row is the model’s best effort level. 25 of the 28 listed models have v1.1 results. Key:
  • DeepSWE: long-horizon software engineering on 113 original tasks written from scratch across 91 repositories and 5 languages, graded by hand-written behavioral verifiers.
  • Pass@1: percent of tasks solved in one attempt; CI is the published ± interval.
  • Cost: average USD per task. Out tok: average output tokens per task. Steps: average agent steps per task.

FrontierCode 1.1

Source: FrontierCode leaderboard by Cognition, current revision, accessed 2026-09-28. Each row is the model’s best-scoring reasoning effort, which is the same on both sets. GPT-6.1 Sol was added 2026-09-29 and accessed that day in the primary JSON. Its best score on both sets is medium effort under the codex harness. The Codex revision and run date are unpublished. Main averages 11,365 output tokens and 14.42 minutes per rollout. Claude Haiku 5.5 was added and accessed 2026-10-07 in the primary JSON. Its best score on both sets is max effort under the Claude Code harness; the run date is unpublished. No unfair-internet-use flag rate is recorded here. Mean cost per Main rollout is $1.33. Key:
  • FrontierCode: mergeability of agent-written pull requests on tasks built by open-source maintainers, graded by rubrics, unit tests and other verifiers for correctness, test quality, scope discipline, style and codebase conventions. Runs that consult solution-bearing sources such as the original pull request score zero.
  • Main, Extended: weighted rubric score in percent on the Main (100 tasks) and Extended (150 tasks) sets; a solution that fails a blocking criterion scores 0.
  • Main pass, Ext pass: percent of tasks passing every blocking criterion.
  • Flag: percent of Main runs flagged for unfair internet use. Cost: mean USD per Main rollout.

Published benchmark results

Scores published by model vendors and benchmark owners for recent frontier models, accessed 2026-09-25; Claude Sonnet 5.5 rows accessed 2026-09-28 and Claude Haiku 5.5 rows accessed 2026-10-07. Values are percent unless the setting names another unit. Each row names its source; the same benchmark can appear once per source because vendors run different harnesses, grading and effort levels. Internal vendor evaluations are excluded. Sources: OpenAI = GPT-6 Astra announcement; Anthropic = Claude Fable 5.1 announcement, Claude Sonnet 5.5 announcement and system card; Google = Gemini 3.8 Flash model card; ARC Prize = arcprize.org results; TB-Science leaderboard = terminal-bench-science.ai; Zapier = AutomationBench leaderboard. GPT-6.1 Sol rows were accessed 2026-09-29. OpenAI = Sol 6.1 announcement and system card; Artificial Analysis = Sol 6.1 model page and methodology. OpenAI’s external benchmark results are vendor-reported, not benchmark-owner leaderboard rows. They ran in its research environment or API; individual harness revisions and run dates are not stated. AA’s TBSci result is independent evidence from its own harness. No matching Sol 6.1 row was retrieved from the DeepSWE, TB-Science, Zapier, Surge GDP.pdf, OSWorld or ARC Prize owner leaderboards on that date. These gaps are unknown, not zero. No minimal effort is measured here, and no Fast or Ultrafast tier has a separate benchmark or latency measurement for any model. Atomic’s derived -fast and -ultrafast routes use explicit base-model metadata to reuse capability evidence, not a separately measured tier score or latency result. Claude Haiku 5.5: Anthropic = launch announcement, published and accessed 2026-10-07. These launch-table results are vendor-reported; effort, harness revisions, and evaluation run dates are not stated unless shown in the setting. GDPval-AA and AA-Briefcase are raw Elo, not the normalized AA columns above. No Haiku 5.5 result was published on the checked DeepSWE leaderboard on 2026-10-07; no row is added to that table. Artificial Analysis independently measures Haiku 5.5 Terminal-Bench-Science in its with-fallback configuration. Sources accessed 2026-10-07: effort-specific model pages for max, xhigh, high, medium, and low, the evaluation page, and AA’s general methodology. That methodology specifies version 0.1.0, 70 tasks, mini-swe-agent, pass@1 averaged over three repeats, and a 1,000-step cap. These are AA’s general settings, not a separately documented Haiku-specific harness revision. Evaluation run dates are unpublished; these are not Anthropic launch-table results. Key:
  • DSWE: DeepSWE v1.1, vendor-reported software-engineering results; distinct from Datacurve’s independent table above.
  • GDPpdf: GDP.pdf All-pass, satisfying every criterion on professional-document tasks.
  • GDPElo, BriefElo: GDPval-AA v2.1 and AA-Briefcase v1.1, raw Elo ratings.
  • OSW21: OSWorld 2.1, computer-use workflows; the Haiku result covers the offline subset.
  • FC11: FrontierCode 1.1 Main, weighted rubric score; distinct from all-blocking-criteria pass rate.
  • Chart: Chartography, chart understanding without tools.
  • ALE: Agents’ Last Exam, long-horizon professional tasks in real software, from financial modeling to engineering and media production.
  • OSW2, OSW2s: OSWorld 2.0, long-horizon computer-use workflows operating desktop applications through the screen; partial credit and strict all-pass scoring.
  • SSP: ScreenSpot-Pro, locating GUI elements in high-resolution professional application screenshots.
  • ABench: AutomationBench, cross-app business workflows in simulated SaaS tools, graded strictly on the resulting business state; distinct from AutomationBench-AA above.
  • CAD: BenchCAD, generating CAD code that reproduces a target part, scored by geometric overlap.
  • BComp: BrowseComp, web browsing to find hard-to-locate facts.
  • HLEt, HLEv: Humanity’s Last Exam with tools, and Google’s HLE-Verified; expert-level academic reasoning.
  • TBSci: Terminal-Bench-Science, agentic scientific research workflows in a terminal across 70 tasks.
  • FMT4: FrontierMath Tier 4, research-level mathematics problems.
  • XBench: ExploitBench, turning known vulnerabilities into exploit primitives and code execution.
  • XGym: ExploitGym, turning real software vulnerabilities into working exploits.
  • SRE: SRE-Bench, reverse engineering binaries without source code.
  • SECPro: SEC-Bench Pro, finding security bugs in critical software.
  • ARC1, ARC2, ARC3: ARC-AGI 1, 2 and 3, abstract reasoning on novel grid puzzles; version 3 is interactive.