GPT-5 pro
Not tested yet
Language modelPro only
GPT-5 pro
OpenAI
- Released
- August 7, 2025
- Data date
- September 8, 2026
- Intelligence
- Overall score
- Price
- Input · Output / 1M tokens
- Input
- Text, Image
- Output
- Text
No published category-wide ranking is available for this model.
Entries 1-7 of 7. Page 1 of 1.
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 modelsGPT-5 pro: Rank 6 of 7Data date: September 2, 2026
Scale: 0% - 100%
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/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Model snapshot: gpt-5-pro-2025-10-06. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 31.64% ± 1.82. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-09-02-2fa0a1a962e9. Source payload hash: 2fa0a1a962e9a3c5220390ba98c86b5502caf5700cda9ca4ebac05222503d874. Parser version: curated-v1. Imported at: 2026-09-04
MultiNRC
Overall
What this benchmark measures: Native reasoning tasks in French, Spanish, and Chinese that were not translated from English templates.
Context: Three languages counter English-only evaluation, but they do not represent global language coverage.
Higher is better10 modelsGPT-5 pro: Rank 1 of 10Data date: August 26, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- scale-multinrc-native-languages
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 10
- Methodology and context
- Native, non-translated reasoning tasks with automatic evaluation.. Source model ID: curated/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 65.2% ± 1.24. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-57a178cbb173. Source payload hash: 57a178cbb173288aaa3efcbad6e3216d222f4787159448eaccdff6629d74f8c2. Parser version: curated-v1. Imported at: 2026-09-04
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 modelsGPT-5 pro: Rank 4 of 16Data date: August 26, 2026
Scale: 0% - 100%
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/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 51.06% ± 0.59. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-466bd0b3fa6e. Source payload hash: 466bd0b3fa6e323b6844a677a04942e35399edcb3102716a3c2d0c7338565b09. Parser version: curated-v1. Imported at: 2026-09-04
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 modelsGPT-5 pro: Rank 4 of 16Data date: August 26, 2026
Scale: 0% - 100%
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/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 49.89% ± 0.36. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-08c20ad4ef1f. Source payload hash: 08c20ad4ef1fb8e59f1ebfe00288e0e00898bc4db7eca96aa27a92dee3c1664b. Parser version: curated-v1. Imported at: 2026-09-04
VISTA
Overall
What this benchmark measures: 758 image-text tasks combine OCR, spatial understanding, object recognition, and open-ended visual reasoning.
Context: The dataset is private and uses task-specific rubrics, making independent reproduction difficult.
Higher is better11 modelsGPT-5 pro: Rank 3 of 11Data date: August 26, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- scale-vista-private-overall
- Metric
- rubric score
- Scale
- 0-100
- Participants
- 11
- Methodology and context
- 758 single-turn image-text tasks with structured yes-no rubrics.. Source model ID: curated/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 52.39% ± 1.07. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-f22f53f59f01. Source payload hash: f22f53f59f01af696ad30df581c179307f548cefbd32d346d2fa431d9c25e8dd. Parser version: curated-v1. Imported at: 2026-09-04
Measurements without matching peer values
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
GPQA (Show measurement, test conditions, and source)
- Source value
- 88.4
- Score
- 88.4
- Metric
- percent
- Unit
- %
- Category
- Intelligence
- Direction
- Higher is better
- Comparison cohort
- research:gpt-5-pro:0
- Source type
- vendor-reported
- Evaluator
- OpenAI
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. GPT-5 pro; without tools
- Context
- Vendor-reported result; the release page does not expose a full GPT-5 pro benchmark table.
External expert preference (Show measurement, test conditions, and source)
- Source value
- 67.8
- Score
- 67.8
- Metric
- percent
- Unit
- %
- Category
- Human evaluation
- Direction
- Higher is better
- Comparison cohort
- research:gpt-5-pro:1
- Source type
- vendor-reported
- Evaluator
- OpenAI
- Status
- active
- Retrieved at
- 2026-09-08
- Methodology
- Vendor-reported result. GPT-5 pro vs GPT-5 thinking; over 1,000 economically valuable real-world reasoning prompts
- Context
- Vendor-reported preference rate, not a task accuracy score.
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
- 31.64
- Score
- 31.64
- 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
- 1.82
- 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/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Model snapshot: gpt-5-pro-2025-10-06. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 31.64% ± 1.82. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-09-02-2fa0a1a962e9. Source payload hash: 2fa0a1a962e9a3c5220390ba98c86b5502caf5700cda9ca4ebac05222503d874. Parser version: curated-v1. Imported at: 2026-09-04
- Comparison methodology
- All 2,500 public questions, temperature 0, and an automatic answer extractor.
MultiNRC: Overall (Show measurement, test conditions, and source)
- Source value
- 65.2
- Score
- 65.2
- Metric
- accuracy
- Unit
- %
- Benchmark version
- French, Spanish, and Chinese release
- Category
- reasoning
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- scale-multinrc-native-languages
- Confidence interval
- 1.24
- Source type
- official-leaderboard
- Evaluator
- Scale AI
- Status
- active
- Retrieved at
- 2026-08-26
- Methodology
- Native, non-translated reasoning tasks with automatic evaluation.. Source model ID: curated/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 65.2% ± 1.24. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-57a178cbb173. Source payload hash: 57a178cbb173288aaa3efcbad6e3216d222f4787159448eaccdff6629d74f8c2. Parser version: curated-v1. Imported at: 2026-09-04
- Comparison methodology
- Native, non-translated reasoning tasks with automatic evaluation.
PRBench Finance: Overall (Show measurement, test conditions, and source)
- Source value
- 51.06
- Score
- 51.06
- 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
- 0.59
- 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/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 51.06% ± 0.59. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-466bd0b3fa6e. Source payload hash: 466bd0b3fa6e323b6844a677a04942e35399edcb3102716a3c2d0c7338565b09. Parser version: curated-v1. Imported at: 2026-09-04
- Comparison methodology
- 600 finance tasks with task-specific rubrics and o4-mini as judge.
PRBench Legal: Overall (Show measurement, test conditions, and source)
- Source value
- 49.89
- Score
- 49.89
- 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
- 0.36
- 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/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 49.89% ± 0.36. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-08c20ad4ef1f. Source payload hash: 08c20ad4ef1fb8e59f1ebfe00288e0e00898bc4db7eca96aa27a92dee3c1664b. Parser version: curated-v1. Imported at: 2026-09-04
- Comparison methodology
- 500 legal tasks with task-specific rubrics and o4-mini as judge.
VISTA: Overall (Show measurement, test conditions, and source)
- Source value
- 52.39
- Score
- 52.39
- Metric
- rubric score
- Unit
- %
- Benchmark version
- 758 private prompt-image pairs
- Category
- multimodal
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- scale-vista-private-overall
- Confidence interval
- 1.07
- Source type
- official-leaderboard
- Evaluator
- Scale AI
- Status
- active
- Retrieved at
- 2026-08-26
- Methodology
- 758 single-turn image-text tasks with structured yes-no rubrics.. Source model ID: curated/gpt-5-pro. Source model name: GPT-5 pro. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 52.39% ± 1.07. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-f22f53f59f01. Source payload hash: f22f53f59f01af696ad30df581c179307f548cefbd32d346d2fa431d9c25e8dd. Parser version: curated-v1. Imported at: 2026-09-04
- Comparison methodology
- 758 single-turn image-text tasks with structured yes-no rubrics.
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.
Prompt and test conditions
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)Profile
Specifications and access
Published information about this model. Existing estimates are explicitly labeled.
Additional source observations
Dated documentation and model-card observations. Provider limits, native context and extended context can differ. Configuration notes retain the source wording.
Pricing
Published prices
Prices apply to the stated unit. Resolution, output length, and provider can change the cost.
Cost example
100 API requests
$7.5
100 requests with 1,000 input and 500 output tokens each calculate to $7.5. Input accounts for $1.5; output accounts for $6.
- Input price
- $15 / 1M tokensBase tier for each request
- Output price
- $120 / 1M tokensBase tier for each request
The calculation uses documented token prices and no cache discount. It excludes extra tools, tax, reasoning tokens, and further hidden output tokens.
Open sourceHead-to-head comparisons
Compare this model
Each matchup compares this model with exactly one other model from the same category.
There are no published direct comparisons for this model yet.
Evidence
Sources and data date
Every statement links to its underlying documentation or leaderboard.
- Price
Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs.
- Input
Documented modalities. Retrieved September 8, 2026.
- Output
Documented modalities. Retrieved September 8, 2026.
Research date September 8, 2026. 3 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Open evidence questions and research notes (original wording)
Not found in the inspected sources
parameters-billions. OpenAI does not publish parameter counts.
Not found in the inspected sources
independent-benchmarks. The exact release-page results are vendor-reported; no independent evaluator row was merged.
Unresolved
AA benchmark rows. No exact Artificial Analysis model page located in the checked public sitemap.
- Model metadataSource
- Context window data (retrieved September 8, 2026)Source
- Scale AI (retrieved September 2, 2026)Source
- Scale AI (retrieved August 26, 2026)Source
- Scale AI (retrieved August 26, 2026)Source
- Scale AI (retrieved August 26, 2026)Source
- Scale AI (retrieved August 26, 2026)Source
- OpenAI (retrieved September 8, 2026)Source