Language modelDeprecated

GPT-5 Pro

OpenAI

Released: October 6, 2025
Data date: September 8, 2026
Predecessor: o3-pro

Benchmark-Meta-Score: Unavailable
Fewer than eight test series
Price: $15 · $120
Input · Output / 1M tokens
Input: Text, Image
Output: Text

GPT-5 Pro spends more compute producing an answer than GPT-5. In ChatGPT, it took over o3-pro’s role.

Specifications and access

Specification	Value and source
Weights	ProprietarySource
Access	APISource
Context window	400,000 tokensSource
Knowledge cutoff	September 30, 2024Source
Notes	Pro-tier reasoning model, parameter count not disclosedSource
API model ID	gpt-5-proSource
Maximum output	272,000 tokensSource
Input	Text, ImageSource
Output	TextSource
Reasoning mode	highSource
API interfaces	Responses APISource
Tool calling	YesSource
Structured output	YesSource
Release	2025-08-07Source

Benchmark meta-analysis

Overall and area scores

These Gradually indexes place selected published measurements on a 0 to 100 scale. They are not accuracy percentages and do not guarantee results in your application.

Retrieval dates of the sources used 2026-08-26 - 2026-09-02

Overall index

Not enough independent results for a score yet

An overall score needs at least eight test series.

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Test series: 5
Test families: 5 / 65
Sources: 1
Share of the fixed test panel (informative): 8 %

Reasoning

Index 11.2 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Standard error: ± 7.2
95% interval: 2.5 to 31.8
Test series: 2
Test families: 2 / 7
Sources: 1
Share of the fixed test panel (informative): 21 %

Finance

Index 25.3 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Standard error: ± 6.0
95% interval: 15.1 to 38.2
Test series: 1
Test families: 1 / 7
Sources: 1
Share of the fixed test panel (informative): 14 %

Legal

Index 24.0 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Standard error: ± 6.0
95% interval: 13.9 to 37.2
Test series: 1
Test families: 1 / 4
Sources: 1
Share of the fixed test panel (informative): 25 %

Multimodal

Index 12.3 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Standard error: ± 5.0
95% interval: 5.1 to 24.7
Test series: 1
Test families: 1 / 4
Sources: 1
Share of the fixed test panel (informative): 25 %

Documented method · Included test families and measurements

Method version: benchmark-meta-score-v3

The index includes active measurements from reviewed primary sources when at least five models are compared in the same documented comparison slice across dataset, harness, agent, tools, and trial count. Each benchmark series and evaluator contributes one shared setup slice from the newest capture. When one source and series include several explicitly documented reasoning tiers, the highest documented tier (none through max) is selected, never the best result value. The explicit source label “Non-reasoning” is treated as none. Unknown or ambiguous tiers exclude the model from that series. Within each series, percentages and rates are logit-transformed, other units stay unchanged, and all values are then standardized. A one-factor model then estimates one shared strength per model and, per series, how sharply it separates the models. Area scores move away from that strength only as far as the model's own results in the area support. The overall score weights the core areas at 75% and finance, law and health at 25%. It requires at least eight test series from two sources and three areas. The 0 to 100 scale refers to a fixed reference group of 29 language models whose average sits at 50.

Published model-specific parameters can differ. This is not a controlled experiment; comparisons with such differences are marked provisional.

Unmeasured series are not entered as zero. Where a model has no own results in a core area, its overall strength stands in, and the note “estimated without own results in …” names the area. Fewer test series lead to a larger standard error.

The 95% interval follows from the standard error of 200 recalculations with randomly redrawn test families. It describes how precisely the recorded test series determine the value. It is no prediction for future tasks or for later recalculations with other participants.

The source count is not a count of independent experiments.

OECD handbook on composite indicators

All 5 test families and 5 of 5 raw measurements are listed here. The complete calculation for every model is available in the JSON export.

Test family: Humanity’s Last Exam
Reasoning · Mean rank percentile of the family: 16.7 / 100
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 50 %
- Scale AI
  Measurement:
  humanitys-last-exam
  Series weight within the test family:
  50 %
  Nominal contribution to the overall panel:
  0.77 %
  Raw value:
  31.64 %
  Metric:
  accuracy
  Version:
  final 2,500-question public set
  Participating models:
  7
  Rank percentile within the cohort:
  16.7 %
  Retrieved:
  2026-09-02
  Comparison cohort:
  hle-final-scale-public-temperature-0
  Documented comparison setup
  Publication cohort:
  hle-final-scale-public-temperature-0
  Dataset type:
  public
  Attempts:
  pass@1
  Setup and model parameters
  All 2,500 public questions, temperature 0, and an automatic answer extractor.. Attempts: pass@1
  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: 2026-10-15
Test family: MultiNRC
Reasoning · Mean rank percentile of the family: 100.0 / 100
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 100 %
- Scale AI
  Published configurations differ within this cohort.
  Measurement:
  multinrc
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.53 %
  Raw value:
  65.2 %
  Metric:
  accuracy
  Version:
  French, Spanish, and Chinese release
  Participating models:
  12
  Rank percentile within the cohort:
  100.0 %
  Retrieved:
  2026-08-26
  Comparison cohort:
  scale-multinrc-native-languages
  Documented comparison setup
  Publication cohort:
  scale-multinrc-native-languages
  Dataset type:
  public
  Attempts:
  pass@1
  Setup and model parameters
  Native, non-translated reasoning tasks with automatic evaluation.. Attempts: pass@1
  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: 2026-10-15
Test family: PRBench Finance
Finance · Mean rank percentile of the family: 80.0 / 100
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
- Scale AI
  Published configurations differ within this cohort.
  Measurement:
  prbench-finance
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.19 %
  Raw value:
  51.06 %
  Metric:
  weighted rubric score
  Version:
  full finance set
  Participating models:
  16
  Rank percentile within the cohort:
  80.0 %
  Retrieved:
  2026-08-26
  Comparison cohort:
  scale-prbench-finance-full
  Documented comparison setup
  Publication cohort:
  scale-prbench-finance-full
  Dataset type:
  public
  Attempts:
  pass@1
  Setup and model parameters
  600 finance tasks with task-specific rubrics and o4-mini as judge.. Attempts: pass@1
  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: 2026-10-15
Test family: PRBench Legal
Legal · Mean rank percentile of the family: 80.0 / 100
Family weight in the fixed test panel (informative): 2.08 % · Coverage of this test family: 100 %
- Scale AI
  Published configurations differ within this cohort.
  Measurement:
  prbench-legal
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.08 %
  Raw value:
  49.89 %
  Metric:
  weighted rubric score
  Version:
  full legal set
  Participating models:
  16
  Rank percentile within the cohort:
  80.0 %
  Retrieved:
  2026-08-26
  Comparison cohort:
  scale-prbench-legal-full
  Documented comparison setup
  Publication cohort:
  scale-prbench-legal-full
  Dataset type:
  public
  Attempts:
  pass@1
  Setup and model parameters
  500 legal tasks with task-specific rubrics and o4-mini as judge.. Attempts: pass@1
  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: 2026-10-15
Test family: VISTA
Multimodal · Mean rank percentile of the family: 84.6 / 100
Family weight in the fixed test panel (informative): 2.68 % · Coverage of this test family: 100 %
- Scale AI
  Published configurations differ within this cohort.
  Measurement:
  vista
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.68 %
  Raw value:
  52.39 %
  Metric:
  rubric score
  Version:
  758 private prompt-image pairs
  Participating models:
  14
  Rank percentile within the cohort:
  84.6 %
  Retrieved:
  2026-08-26
  Comparison cohort:
  scale-vista-private-overall
  Documented comparison setup
  Publication cohort:
  scale-vista-private-overall
  Dataset type:
  public
  Attempts:
  pass@1
  Setup and model parameters
  758 single-turn image-text tasks with structured yes-no rubrics.. Attempts: pass@1
  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: 2026-10-15

Benchmark sources at a glance

Each evaluator measures different capabilities under its own conditions. Test series, composite indices and their components stay separate. The detailed results below retain their scales, configurations and source dates.

Evaluator	Test series	Composite indices	Recorded active measurements
Scale AI Evals	5	0	5

Coverage is not a quality ranking. Overlapping tests and multiple settings do not count as independent experiments. Vendor claims, historical and disputed results are excluded from this overview and remain labelled in the detailed evidence.

Published benchmarks

Humanity's Last Exam

Overall

2,500 difficult, partly multimodal expert questions across more than 100 subjects. The benchmark measures closed-ended expert knowledge and reasoning, not open research or general intelligence.

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%

higher is betterAs of September 2, 2026Scale: 0% - 100%

Source: Scale AI

|

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/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: 2026-10-15

Source: Scale AI

MultiNRC

Overall

Native reasoning tasks in French, Spanish, and Chinese that were not translated from English templates. Three languages counter English-only evaluation, but they do not represent global language coverage.

1.GPT-5 Pro65.2%

2.Gemini 3.1 Pro Preview64.74%

3.GPT-5.4 Pro62.27%

4.GPT-5.458.29%

5.GPT-552.13%

7.Gemini 2.5 Pro45.12%

8.DeepSeek-R127.58%

9.GPT-4.121.23%

10.Llama 4 Maverick8.44%

higher is betterAs of August 26, 2026Scale: 0% - 100%

Source: Scale AI

|

gradually.ai

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: 2026-10-15

Source: Scale AI

PRBench Finance

Overall

600 realistic finance questions with weighted criteria, including a 300-question hard subset. An o4-mini judge scores the responses, so domain correctness and operational reliability remain separate questions.

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%

higher is betterAs of August 26, 2026Scale: 0% - 100%

Source: Scale AI

|

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/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: 2026-10-15

Source: Scale AI

PRBench Legal

Overall

500 realistic legal questions with weighted criteria across jurisdictions and practice areas. The benchmark measures professional response quality but cannot replace legal review or human accountability.

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%

higher is betterAs of August 26, 2026Scale: 0% - 100%

Source: Scale AI

|

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/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: 2026-10-15

Source: Scale AI

VISTA

Overall

758 image-text tasks combine OCR, spatial understanding, object recognition, and open-ended visual reasoning. The dataset is private and uses task-specific rubrics, making independent reproduction difficult.

1.Gemini 2.5 Pro54.63%

2.GPT-5.4 Pro53.89%

3.GPT-5 Pro52.39%

4.o3-pro51.63%

5.GPT-5.450.89%

6.GPT-5 mini50.39%

7.GPT-549.69%

8.GPT-4.145.34%

9.GPT-4.1 mini41.14%

10.Llama 4 Maverick38.33%

11.GPT-4.1 nano26.55%

higher is betterAs of August 26, 2026Scale: 0% - 100%

Source: Scale AI

|

gradually.ai

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: 2026-10-15

Source: Scale AI

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
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
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

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: 2026-10-15
Comparison methodology: All 2,500 public questions, temperature 0, and an automatic answer extractor.

Source Scale AI

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: 2026-10-15
Comparison methodology: Native, non-translated reasoning tasks with automatic evaluation.

Source Scale AI

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: 2026-10-15
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: 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: 2026-10-15
Comparison methodology: 500 legal tasks with task-specific rubrics and o4-mini as judge.

Source Scale AI

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: 2026-10-15
Comparison methodology: 758 single-turn image-text tasks with structured yes-no rubrics.

Source Scale AI

Predecessor and successor

Sources and data date

Type	Evidence and data date
Price	Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs. Source: Pricing · Context window data (retrieved September 8, 2026) · GPT-5 Pro API model documentation · Editorial description reviewed October 2, 2026
Input	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Context window data (retrieved September 8, 2026) · GPT-5 Pro API model documentation · Editorial description reviewed October 2, 2026
Output	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Context window data (retrieved September 8, 2026) · GPT-5 Pro API model documentation · Editorial description reviewed October 2, 2026
Research status	Research date September 8, 2026. 3 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not found in the inspected sources	parameters-billions Original wording OpenAI does not publish parameter counts.
Not found in the inspected sources	independent-benchmarks Original wording The exact release-page results are vendor-reported; no independent evaluator row was merged.
Unresolved	AA benchmark rows Original wording No exact Artificial Analysis model page located in the checked public sitemap.
Additional source	Model metadata
Additional source	Scale AI (retrieved September 2, 2026)
Additional source	Scale AI (retrieved August 26, 2026)
Additional source	Scale AI (retrieved August 26, 2026)
Additional source	Scale AI (retrieved August 26, 2026)
Additional source	Scale AI (retrieved August 26, 2026)
Additional source	OpenAI (retrieved September 8, 2026) · Introducing GPT-5 · Editorial description reviewed October 2, 2026
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Ollama: cumulative downloads for the popularity index
Additional source	Replicate: cumulative runs for the popularity index
Additional source	Google Trending Now: limited feed selection for Germany and the US
Additional source	Creative Commons Attribution 4.0