Language modelActive

Claude Opus 4.6

Anthropic

Released: February 2026
Data date: September 8, 2026

Intelligence: Not ranked
Overall score
Speed: 39.2 tokens/s
Output throughput
Price: $5 · $25
Input · Output / 1M tokens
Input: Text, Image
Output: Text

No published category-wide ranking is available for this model.

Entries 1-12 of 32. Page 1 of 3.

Published benchmarks

Each chart compares models in the same documented test and highlights the current model.

Humanity's Last Exam

Overall

What this benchmark measures: 2,500 difficult, partly multimodal expert questions across more than 100 subjects.

Context: The benchmark measures closed-ended expert knowledge and reasoning, not open research or general intelligence.

Higher is better7 modelsClaude Opus 4.6: Rank 5 of 7Data date: September 2, 2026

1.Gemini 3.1 Pro Preview46.44%

2.GPT-5.4 Pro44.32%

3.GPT-5.436.24%

4.Claude Opus 4.736.2%

5.Claude Opus 4.634.44%

6.GPT-5 pro31.64%

7.GPT-525.32%

Scale: 0% - 100%

Source: Scale AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hle-final-scale-public-temperature-0
Metric: accuracy
Scale: 0-100
Participants: 7
Methodology and context: All 2,500 public questions, temperature 0, and an automatic answer extractor.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Model snapshot: claude-opus-4-6-thinking-max. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 34.44% ± 1.86. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-09-02-9e661b4b9e47. Source payload hash: 9e661b4b9e47fc78acc41dd3d83ae7363672deeb05d38a32fd0dcc88c7502935. Parser version: curated-v1. Imported at: 2026-09-04

Source: Scale AI

Agent tasks

2 measurements

METR Time Horizon

50% time horizon

What this benchmark measures: Human task duration at which a model-agent system reaches a 50% or 80% predicted success probability.

Context: Minutes refer to human task duration, not model runtime. METR considers estimates above 16 hours unreliable.

Higher is better3 modelsClaude Opus 4.6: Rank 1 of 3Data date: August 26, 2026

1.Claude Opus 4.6718.81 minutes

2.GPT-5.4341.74 minutes

3.Claude Opus 4.5292.99 minutes

Scale: 0 minutes - 718.81 minutes

Source: METR

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: metr-th1-1-p50
Metric: 50% time horizon
Scale: No upper bound is documented. The chart spans the recorded values and zero.
Participants: 3
Methodology and context: ReAct agent with six independent runs per task. Time is estimated human completion duration.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Model snapshot: claude-opus-4-6-2026-02-05. Evaluator: METR. Dataset type: public. Leaderboard date: 2026-05-08. Harness: ReAct. Agent: ReAct. Attempts: 6 independent runs per task. Score display: 12 h (95% confidence interval 5.3 h to 60.6 h). Source ID: metr. Evidence capture ID: curated-metr-2026-08-26-1ca8b69f5a82. Source payload hash: 1ca8b69f5a826f53e175e4b094b9f4b51e14a39ba2c084eab283897836d49d20. Parser version: curated-v1. Imported at: 2026-09-04

Source: METR

METR Time Horizon

80% time horizon

What this benchmark measures: Human task duration at which a model-agent system reaches a 50% or 80% predicted success probability.

Context: Minutes refer to human task duration, not model runtime. METR considers estimates above 16 hours unreliable.

Higher is better3 modelsClaude Opus 4.6: Rank 1 of 3Data date: August 26, 2026

1.Claude Opus 4.669.87 minutes

2.GPT-5.453.88 minutes

3.Claude Opus 4.549.43 minutes

Scale: 0 minutes - 69.87 minutes

Source: METR

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: metr-th1-1-p80
Metric: 80% time horizon
Scale: No upper bound is documented. The chart spans the recorded values and zero.
Participants: 3
Methodology and context: ReAct agent with six independent runs per task. Time is estimated human completion duration.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Model snapshot: claude-opus-4-6-2026-02-05. Evaluator: METR. Dataset type: public. Leaderboard date: 2026-05-08. Harness: ReAct. Agent: ReAct. Attempts: 6 independent runs per task. Score display: 1.2 h (95% confidence interval 27 min to 2.8 h). Source ID: metr. Evidence capture ID: curated-metr-2026-08-26-318bdb5530ca. Source payload hash: 318bdb5530cad90be3765939e5139aa3ed2f5a6b9e463cf3398f516e4710e864. Parser version: curated-v1. Imported at: 2026-09-04

Source: METR

MultiChallenge

Overall

What this benchmark measures: Multi-turn conversations test memory, instruction retention, versioned editing, and self-coherence.

Context: Tasks were selected from shared failures of six older frontier models and may disadvantage those systems.

Higher is better8 modelsClaude Opus 4.6: Rank 7 of 8Data date: August 26, 2026

1.Gemini 3.1 Pro Preview71.37%

2.GPT-5.4 Pro69.23%

3.GPT-563.19%

4.o3-pro62.4%

5.Claude Opus 4.558.97%

6.o356.62%

7.Claude Opus 4.656.02%

8.Claude Sonnet 4.555.32%

Scale: 0% - 100%

Source: Scale AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: scale-multichallenge-overall
Metric: rubric pass rate
Scale: 0-100
Participants: 8
Methodology and context: Multi-turn conversations with binary task-specific rubrics.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: none. Score display: 56.02% ± 1.08. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-c7bc8d4b82b8. Source payload hash: c7bc8d4b82b8a93b33209cb8b5c8da53dc01bac7aa0cd5ca76c4ae365619525d. Parser version: curated-v1. Imported at: 2026-09-04

Source: Scale AI

PRBench Finance

Overall

What this benchmark measures: 600 realistic finance questions with weighted criteria, including a 300-question hard subset.

Context: An o4-mini judge scores the responses, so domain correctness and operational reliability remain separate questions.

Higher is better16 modelsClaude Opus 4.6: Rank 2 of 16Data date: August 26, 2026

1.Claude Fable 553.86%

2.Claude Opus 4.653.28%

3.GPT-551.32%

4.GPT-5 pro51.06%

5.GPT-5.6 Sol50.45%

6.o3-pro49.08%

7.o347.69%

8.Claude Opus 4.546.16%

9.GPT-5.445.63%

10.Claude Sonnet 4.543.79%

11.Gemini 3.1 Pro Preview41.87%

12.o4-mini39.22%

13.Gemini 2.5 Pro38.92%

14.Gemini 2.5 Flash38.41%

15.GPT-4.134.32%

16.Llama 4 Maverick22.36%

Scale: 0% - 100%

Source: Scale AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: scale-prbench-finance-full
Metric: weighted rubric score
Scale: 0-100
Participants: 16
Methodology and context: 600 finance tasks with task-specific rubrics and o4-mini as judge.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: none. Score display: 53.28% ± 0.18. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-1196d0ba7a4c. Source payload hash: 1196d0ba7a4c8d626776c467c1d9d53e493e848fbce8f12f327c3864f83ac2c4. Parser version: curated-v1. Imported at: 2026-09-04

Source: Scale AI

PRBench Legal

Overall

What this benchmark measures: 500 realistic legal questions with weighted criteria across jurisdictions and practice areas.

Context: The benchmark measures professional response quality but cannot replace legal review or human accountability.

Higher is better16 modelsClaude Opus 4.6: Rank 2 of 16Data date: August 26, 2026

1.Claude Fable 552.56%

2.Claude Opus 4.652.27%

3.GPT-5.6 Sol50.5%

4.GPT-5 pro49.89%

5.o3-pro49.67%

6.GPT-548.96%

7.o348.57%

8.GPT-5.444.35%

9.Claude Opus 4.544.21%

10.Gemini 3.1 Pro Preview44.02%

11.Gemini 2.5 Pro41.43%

12.Gemini 2.5 Flash41.02%

13.Claude Sonnet 4.540.84%

14.o4-mini38.11%

15.GPT-4.136.48%

16.Llama 4 Maverick24.84%

Scale: 0% - 100%

Source: Scale AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: scale-prbench-legal-full
Metric: weighted rubric score
Scale: 0-100
Participants: 16
Methodology and context: 500 legal tasks with task-specific rubrics and o4-mini as judge.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: none. Score display: 52.27% ± 0.66. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-39dd22178287. Source payload hash: 39dd221782879e08cf3582225a60f6e91432e1e8d4968e2fb60b53c917fafe6a. Parser version: curated-v1. Imported at: 2026-09-04

Source: Scale AI

VisualToolBench

Overall

What this benchmark measures: 1,204 visual tasks require image manipulation, Python, web search, or calculator use with at most 20 tool calls.

Context: The score belongs to the complete multimodal tool system and cannot isolate the model alone.

Higher is better10 modelsClaude Opus 4.6: Rank 3 of 10Data date: August 26, 2026

1.GPT-5.429.17%

2.Gemini 3.1 Pro Preview28.97%

3.Claude Opus 4.627.52%

4.o313.74%

5.Gemini 2.5 Pro11.75%

6.o4-mini11.12%

7.GPT-4.15.52%

8.Gemini 2.5 Flash4.69%

9.Llama 4 Scout1.58%

10.Llama 4 Maverick1.41%

Scale: 0% - 100%

Source: Scale AI

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: scale-visual-toolbench-apr
Metric: average pass rate
Scale: 0-100
Participants: 10
Methodology and context: 1,204 tasks, six tools, and at most 20 tool calls per task.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: thinking. Score display: 27.52% ± 1.45. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-eb72202a59a1. Source payload hash: eb72202a59a1301462dd728b19363526d0f1ed2c2b58120de39b0027aafc74ac. Parser version: curated-v1. Imported at: 2026-09-04

Source: Scale AI

Cybersecurity

5 measurements

SecCodeBench-V2

Repair without hints

What this benchmark measures: 98 executable project cases across 22 CWE categories test functionality before security.

Context: The weighted score combines generation, repair, native prompts, and security-aware prompts.

Higher is better4 modelsClaude Opus 4.6: Rank 2 of 4Data date: August 9, 2026

1.Claude Opus 4.573.67%

2.Claude Opus 4.673.1%

3.Qwen 3.7 Max72.5%

4.GPT-5.461.41%

Scale: 0% - 100%

Source: SecCodeBench

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: seccodebench-v2-2-0:official:fix
Metric: native repair pass@1
Scale: 0-100
Participants: 4
Methodology and context: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-cbeb9cb3730c. Source payload hash: cbeb9cb3730ca29af3d8b7a5e2fca139765542955a6bbf61409536cafac54c77. Parser version: curated-v1. Imported at: 2026-09-04

Source: SecCodeBench

SecCodeBench-V2

Repair with security hints

What this benchmark measures: 98 executable project cases across 22 CWE categories test functionality before security.

Context: The weighted score combines generation, repair, native prompts, and security-aware prompts.

Higher is better4 modelsClaude Opus 4.6: Rank 3 of 4Data date: August 9, 2026

1.Qwen 3.7 Max86.77%

2.Claude Opus 4.582.18%

3.Claude Opus 4.681.73%

4.GPT-5.480.04%

Scale: 0% - 100%

Source: SecCodeBench

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: seccodebench-v2-2-0:official:fixHints
Metric: security-aware repair pass@1
Scale: 0-100
Participants: 4
Methodology and context: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-ce147aec18e9. Source payload hash: ce147aec18e9354c4817af561e4326fc26c9aa5fb2fae3b3abce107c5ae614a5. Parser version: curated-v1. Imported at: 2026-09-04

Source: SecCodeBench

SecCodeBench-V2

Generation without hints

What this benchmark measures: 98 executable project cases across 22 CWE categories test functionality before security.

Context: The weighted score combines generation, repair, native prompts, and security-aware prompts.

Higher is better4 modelsClaude Opus 4.6: Rank 3 of 4Data date: August 9, 2026

1.Claude Opus 4.558.02%

2.Qwen 3.7 Max55.12%

3.Claude Opus 4.651.05%

4.GPT-5.450.97%

Scale: 0% - 100%

Source: SecCodeBench

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: seccodebench-v2-2-0:official:generation
Metric: native generation pass@1
Scale: 0-100
Participants: 4
Methodology and context: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-ea0efe96af3b. Source payload hash: ea0efe96af3b9a3ff40cb16e820c61e6be6d652be27ce4f466af6ee5d583d804. Parser version: curated-v1. Imported at: 2026-09-04

Source: SecCodeBench

SecCodeBench-V2

Generation with security hints

What this benchmark measures: 98 executable project cases across 22 CWE categories test functionality before security.

Context: The weighted score combines generation, repair, native prompts, and security-aware prompts.

Higher is better4 modelsClaude Opus 4.6: Rank 3 of 4Data date: August 9, 2026

1.Claude Opus 4.572.1%

2.Qwen 3.7 Max70.85%

3.Claude Opus 4.670.65%

4.GPT-5.467.82%

Scale: 0% - 100%

Source: SecCodeBench

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: seccodebench-v2-2-0:official:generationHints
Metric: security-aware generation pass@1
Scale: 0-100
Participants: 4
Methodology and context: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-5a782a1bbabe. Source payload hash: 5a782a1bbabe9946ce4fb4771835246fcedde9c1186f456a2a364c1860c8531a. Parser version: curated-v1. Imported at: 2026-09-04

Source: SecCodeBench

SecCodeBench-V2

Weighted total

What this benchmark measures: 98 executable project cases across 22 CWE categories test functionality before security.

Context: The weighted score combines generation, repair, native prompts, and security-aware prompts.

Higher is better4 modelsClaude Opus 4.6: Rank 3 of 4Data date: August 9, 2026

1.Claude Opus 4.568.1%

2.Qwen 3.7 Max66.81%

3.Claude Opus 4.664.9%

4.GPT-5.459.74%

Scale: 0% - 100%

Source: SecCodeBench

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: seccodebench-v2-2-0:official:total
Metric: weighted pass@1
Scale: 0-100
Participants: 4
Methodology and context: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-1a93d69f218e. Source payload hash: 1a93d69f218e7cddea5bc50ca7db1867e78031ce74a62a35f964a572e0a55048. Parser version: curated-v1. Imported at: 2026-09-04

Source: SecCodeBench

Recorded measurement details

Every observation retains its source value and published test conditions.

Humanity's Last Exam: Overall (Show measurement, test conditions, and source)

Source value: 34.44
Score: 34.44
Metric: accuracy
Unit: %
Benchmark version: final 2,500-question public set
Category: knowledge
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: hle-final-scale-public-temperature-0
Confidence interval: 1.86
Source type: official-leaderboard
Evaluator: Scale AI
Status: active
Retrieved at: 2026-09-02
Methodology: All 2,500 public questions, temperature 0, and an automatic answer extractor.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Model snapshot: claude-opus-4-6-thinking-max. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 34.44% ± 1.86. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-09-02-9e661b4b9e47. Source payload hash: 9e661b4b9e47fc78acc41dd3d83ae7363672deeb05d38a32fd0dcc88c7502935. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: All 2,500 public questions, temperature 0, and an automatic answer extractor.

Source Scale AI

METR Time Horizon: 50% time horizon (Show measurement, test conditions, and source)

Source value: 718.80683
Score: 718.80683
Metric: 50% time horizon
Unit: minutes
Benchmark version: TH 1.1
Category: agentic
Task: p50
Task label: 50% time horizon
Direction: Higher is better
Comparison cohort: metr-th1-1-p50
Source type: independent-evaluator
Evaluator: METR
Status: active
Retrieved at: 2026-08-26
Methodology: ReAct agent with six independent runs per task. Time is estimated human completion duration.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Model snapshot: claude-opus-4-6-2026-02-05. Evaluator: METR. Dataset type: public. Leaderboard date: 2026-05-08. Harness: ReAct. Agent: ReAct. Attempts: 6 independent runs per task. Score display: 12 h (95% confidence interval 5.3 h to 60.6 h). Source ID: metr. Evidence capture ID: curated-metr-2026-08-26-1ca8b69f5a82. Source payload hash: 1ca8b69f5a826f53e175e4b094b9f4b51e14a39ba2c084eab283897836d49d20. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: ReAct agent with six independent runs per task. Time is estimated human completion duration.

Source METR

METR Time Horizon: 80% time horizon (Show measurement, test conditions, and source)

Source value: 69.874587
Score: 69.874587
Metric: 80% time horizon
Unit: minutes
Benchmark version: TH 1.1
Category: agentic
Task: p80
Task label: 80% time horizon
Direction: Higher is better
Comparison cohort: metr-th1-1-p80
Source type: independent-evaluator
Evaluator: METR
Status: active
Retrieved at: 2026-08-26
Methodology: ReAct agent with six independent runs per task. Time is estimated human completion duration.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Model snapshot: claude-opus-4-6-2026-02-05. Evaluator: METR. Dataset type: public. Leaderboard date: 2026-05-08. Harness: ReAct. Agent: ReAct. Attempts: 6 independent runs per task. Score display: 1.2 h (95% confidence interval 27 min to 2.8 h). Source ID: metr. Evidence capture ID: curated-metr-2026-08-26-318bdb5530ca. Source payload hash: 318bdb5530cad90be3765939e5139aa3ed2f5a6b9e463cf3398f516e4710e864. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: ReAct agent with six independent runs per task. Time is estimated human completion duration.

Source METR

MultiChallenge: Overall (Show measurement, test conditions, and source)

Source value: 56.02
Score: 56.02
Metric: rubric pass rate
Unit: %
Benchmark version: four multi-turn challenge types
Category: productivity
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: scale-multichallenge-overall
Confidence interval: 1.08
Source type: official-leaderboard
Evaluator: Scale AI
Status: active
Retrieved at: 2026-08-26
Methodology: Multi-turn conversations with binary task-specific rubrics.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: none. Score display: 56.02% ± 1.08. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-c7bc8d4b82b8. Source payload hash: c7bc8d4b82b8a93b33209cb8b5c8da53dc01bac7aa0cd5ca76c4ae365619525d. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: Multi-turn conversations with binary task-specific rubrics.

Source Scale AI

PRBench Finance: Overall (Show measurement, test conditions, and source)

Source value: 53.28
Score: 53.28
Metric: weighted rubric score
Unit: %
Benchmark version: full finance set
Category: finance
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: scale-prbench-finance-full
Confidence interval: 0.18
Source type: official-leaderboard
Evaluator: Scale AI
Status: active
Retrieved at: 2026-08-26
Methodology: 600 finance tasks with task-specific rubrics and o4-mini as judge.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: none. Score display: 53.28% ± 0.18. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-1196d0ba7a4c. Source payload hash: 1196d0ba7a4c8d626776c467c1d9d53e493e848fbce8f12f327c3864f83ac2c4. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 600 finance tasks with task-specific rubrics and o4-mini as judge.

Source Scale AI

PRBench Legal: Overall (Show measurement, test conditions, and source)

Source value: 52.27
Score: 52.27
Metric: weighted rubric score
Unit: %
Benchmark version: full legal set
Category: legal
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: scale-prbench-legal-full
Confidence interval: 0.66
Source type: official-leaderboard
Evaluator: Scale AI
Status: active
Retrieved at: 2026-08-26
Methodology: 500 legal tasks with task-specific rubrics and o4-mini as judge.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: none. Score display: 52.27% ± 0.66. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-39dd22178287. Source payload hash: 39dd221782879e08cf3582225a60f6e91432e1e8d4968e2fb60b53c917fafe6a. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 500 legal tasks with task-specific rubrics and o4-mini as judge.

Source Scale AI

VisualToolBench: Overall (Show measurement, test conditions, and source)

Source value: 27.52
Score: 27.52
Metric: average pass rate
Unit: %
Benchmark version: 1,204-task release
Category: multimodal
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: scale-visual-toolbench-apr
Confidence interval: 1.45
Source type: official-leaderboard
Evaluator: Scale AI
Status: active
Retrieved at: 2026-08-26
Methodology: 1,204 tasks, six tools, and at most 20 tool calls per task.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Reasoning effort: thinking. Score display: 27.52% ± 1.45. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-eb72202a59a1. Source payload hash: eb72202a59a1301462dd728b19363526d0f1ed2c2b58120de39b0027aafc74ac. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 1,204 tasks, six tools, and at most 20 tool calls per task.

Source Scale AI

SecCodeBench-V2: Repair without hints (Show measurement, test conditions, and source)

Source value: 73.1
Score: 73.1
Metric: native repair pass@1
Unit: %
Benchmark version: 2.2.0
Category: cybersecurity
Task: fix
Task label: Repair without hints
Direction: Higher is better
Comparison cohort: seccodebench-v2-2-0:official:fix
Source type: official-leaderboard
Evaluator: SecCodeBench
Status: active
Retrieved at: 2026-08-09
Methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-cbeb9cb3730c. Source payload hash: cbeb9cb3730ca29af3d8b7a5e2fca139765542955a6bbf61409536cafac54c77. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.

Source SecCodeBench

SecCodeBench-V2: Repair with security hints (Show measurement, test conditions, and source)

Source value: 81.73
Score: 81.73
Metric: security-aware repair pass@1
Unit: %
Benchmark version: 2.2.0
Category: cybersecurity
Task: fixHints
Task label: Repair with security hints
Direction: Higher is better
Comparison cohort: seccodebench-v2-2-0:official:fixHints
Source type: official-leaderboard
Evaluator: SecCodeBench
Status: active
Retrieved at: 2026-08-09
Methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-ce147aec18e9. Source payload hash: ce147aec18e9354c4817af561e4326fc26c9aa5fb2fae3b3abce107c5ae614a5. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.

Source SecCodeBench

SecCodeBench-V2: Generation without hints (Show measurement, test conditions, and source)

Source value: 51.05
Score: 51.05
Metric: native generation pass@1
Unit: %
Benchmark version: 2.2.0
Category: cybersecurity
Task: generation
Task label: Generation without hints
Direction: Higher is better
Comparison cohort: seccodebench-v2-2-0:official:generation
Source type: official-leaderboard
Evaluator: SecCodeBench
Status: active
Retrieved at: 2026-08-09
Methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-ea0efe96af3b. Source payload hash: ea0efe96af3b9a3ff40cb16e820c61e6be6d652be27ce4f466af6ee5d583d804. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.

Source SecCodeBench

SecCodeBench-V2: Generation with security hints (Show measurement, test conditions, and source)

Source value: 70.65
Score: 70.65
Metric: security-aware generation pass@1
Unit: %
Benchmark version: 2.2.0
Category: cybersecurity
Task: generationHints
Task label: Generation with security hints
Direction: Higher is better
Comparison cohort: seccodebench-v2-2-0:official:generationHints
Source type: official-leaderboard
Evaluator: SecCodeBench
Status: active
Retrieved at: 2026-08-09
Methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-5a782a1bbabe. Source payload hash: 5a782a1bbabe9946ce4fb4771835246fcedde9c1186f456a2a364c1860c8531a. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.

Source SecCodeBench

SecCodeBench-V2: Weighted total (Show measurement, test conditions, and source)

Source value: 64.9
Score: 64.9
Metric: weighted pass@1
Unit: %
Benchmark version: 2.2.0
Category: cybersecurity
Task: total
Task label: Weighted total
Direction: Higher is better
Comparison cohort: seccodebench-v2-2-0:official:total
Source type: official-leaderboard
Evaluator: SecCodeBench
Status: active
Retrieved at: 2026-08-09
Methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.. Source model ID: curated/claude-opus-4.6. Source model name: Claude Opus 4.6. Provider reported by source: Anthropic. Evaluator: SecCodeBench. Dataset type: public. Leaderboard date: 2026-02-16. Source ID: seccodebench. Evidence capture ID: curated-seccodebench-2026-08-09-1a93d69f218e. Source payload hash: 1a93d69f218e7cddea5bc50ca7db1867e78031ce74a62a35f964a572e0a55048. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: 98 project cases. Functional tests run before dynamic exploit checks and security scoring.

Source SecCodeBench

Our model tests

Community tests you can inspect

Compare how the models respond to the same prompt. Each test shows the first attempt, with no subsequent fixes to the generated code. These results do not contribute to an overall score.

Pelican on a bicycle

A community classic for free-form SVG drawing.

Claude Opus 4.6

Not tested yet

Prompt and test conditions

Generate an SVG of a pelican riding a bicycle

Token limit including reasoning: 8,192 tokens

Task origin (Simon Willison)

Claude Opus 4.6

Published benchmarks

Humanity's Last Exam

Agent tasks

METR Time Horizon

METR Time Horizon

MultiChallenge

PRBench Finance

PRBench Legal

VisualToolBench

Cybersecurity

SecCodeBench-V2

SecCodeBench-V2

SecCodeBench-V2

SecCodeBench-V2

SecCodeBench-V2

Recorded measurement details

Community tests you can inspect

Pelican on a bicycle

Specifications and access

Additional source observations

Published prices

100 API requests

Compare this model

Sources and data date