Language modelActive

GPT-5

OpenAI

Released: August 7, 2025
Data date: September 8, 2026

Intelligence: Not ranked
Overall score
Speed: 105.2 tokens/s
Output throughput
Price: $1.25 · $10
Input · Output / 1M tokens
Input: Text, Image
Output: Text

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

Entries 1-12 of 149. Page 1 of 13.

Published benchmarks

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

Knowledge

5 measurements

FACTS Benchmark Suite

Document grounding

What this benchmark measures: 3,513 public examples test factuality with documents, images, parametric knowledge, and web search.

Context: The suite score averages four different capabilities. The component scores matter more for decisions.

Higher is better4 modelsGPT-5: Rank 2 of 4Data date: August 9, 2026

1.Gemini 2.5 Pro74.2%

2.GPT-569.6%

3.Claude Opus 4.562.1%

4.GPT-4.145.6%

Scale: 0% - 100%

Source: Google DeepMind

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: facts-suite-v2:official:grounding
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-e3c06a111c89. Source payload hash: e3c06a111c89025d960a0ff8915359c292a403d8d532eb38414ef7fa8844111c. Parser version: curated-v1. Imported at: 2026-09-04

Source: Google DeepMind

FACTS Benchmark Suite

Multimodal factuality

What this benchmark measures: 3,513 public examples test factuality with documents, images, parametric knowledge, and web search.

Context: The suite score averages four different capabilities. The component scores matter more for decisions.

Higher is better4 modelsGPT-5: Rank 2 of 4Data date: August 9, 2026

1.Gemini 2.5 Pro46.9%

2.GPT-544.1%

3.GPT-4.140.1%

4.Claude Opus 4.539.2%

Scale: 0% - 100%

Source: Google DeepMind

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: facts-suite-v2:official:multimodal
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-8bdaaf6bd0f7. Source payload hash: 8bdaaf6bd0f7b4c78d4b8590ae3b79dd006230b7b96e8a7012f1c498f52f7632. Parser version: curated-v1. Imported at: 2026-09-04

Source: Google DeepMind

FACTS Benchmark Suite

FACTS overall score

What this benchmark measures: 3,513 public examples test factuality with documents, images, parametric knowledge, and web search.

Context: The suite score averages four different capabilities. The component scores matter more for decisions.

Higher is better4 modelsGPT-5: Rank 2 of 4Data date: August 9, 2026

1.Gemini 2.5 Pro62.1%

2.GPT-561.8%

3.Claude Opus 4.551.3%

4.GPT-4.150.5%

Scale: 0% - 100%

Source: Google DeepMind

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: facts-suite-v2:official:overall
Metric: FACTS score
Scale: 0-100
Participants: 4
Methodology and context: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-a7084e38f392. Source payload hash: a7084e38f392ecbca770296346f79b4fc382d667f0425a3b16a736837116f83e. Parser version: curated-v1. Imported at: 2026-09-04

Source: Google DeepMind

FACTS Benchmark Suite

Parametric knowledge

What this benchmark measures: 3,513 public examples test factuality with documents, images, parametric knowledge, and web search.

Context: The suite score averages four different capabilities. The component scores matter more for decisions.

Higher is better4 modelsGPT-5: Rank 2 of 4Data date: August 9, 2026

1.Gemini 2.5 Pro63.2%

2.GPT-555.8%

3.GPT-4.151.5%

4.Claude Opus 4.530.6%

Scale: 0% - 100%

Source: Google DeepMind

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: facts-suite-v2:official:parametric
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-660579aeda2e. Source payload hash: 660579aeda2ec67dd0f27d69abe37808c7f28454186818581a3c3eea4b8bc463. Parser version: curated-v1. Imported at: 2026-09-04

Source: Google DeepMind

FACTS Benchmark Suite

Factuality with search

What this benchmark measures: 3,513 public examples test factuality with documents, images, parametric knowledge, and web search.

Context: The suite score averages four different capabilities. The component scores matter more for decisions.

Higher is better4 modelsGPT-5: Rank 1 of 4Data date: August 9, 2026

1.GPT-577.7%

2.Claude Opus 4.573.2%

3.GPT-4.164.6%

4.Gemini 2.5 Pro63.9%

Scale: 0% - 100%

Source: Google DeepMind

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: facts-suite-v2:official:search
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-1afb059d0a2f. Source payload hash: 1afb059d0a2f06d37090f7953ae08ce697a9d76b044632c76dd6722ec51da362. Parser version: curated-v1. Imported at: 2026-09-04

Source: Google DeepMind

Agent tasks

3 measurements

AssistantBench

Accuracy

What this benchmark measures: 33 automatically verifiable HAL tasks drawn from 214 realistic, time-consuming web research tasks.

Context: The published HAL subset is smaller than the full dataset. Reported costs do not account for caching benefits.

Higher is better4 modelsGPT-5: Rank 2 of 4Data date: August 5, 2026

1.o338.81%

2.GPT-535.23%

3.GPT-4.117.39%

4.DeepSeek-V32.03%

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-assistantbench-browser-use-overall
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: One shared Browser-Use scaffold solves 33 verified HAL tasks. Total cost is divided by 33.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: Browser-Use. Agent: Browser-Use. Reasoning effort: medium. Cost per task in USD: 1.2633333333333332. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-b037c99662cf. Source payload hash: b037c99662cf1205a80a9c7650f605db0cac75c65b50792d387a36d55e9bb080. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

Online Mind2Web

Accuracy

What this benchmark measures: 300 verified tasks across 136 dynamic live websites.

Context: SeeAct and Browser-Use are different agent scaffolds and therefore remain separate comparison cohorts.

Higher is better5 modelsGPT-5: Rank 3 of 5Data date: August 5, 2026

1.GPT-4.136.33%

2.DeepSeek-V332.33%

3.GPT-532%

4.o329%

5.DeepSeek-R125.33%

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-online-mind2web-browser-use-overall
Metric: accuracy
Scale: 0-100
Participants: 5
Methodology and context: The Browser-Use scaffold handles the same 300 tasks across 136 live websites. Total cost is divided by 300.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: Browser-Use. Agent: Browser-Use. Reasoning effort: medium. Cost per task in USD: 2.4543666666666666. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-4d01dc241a10. Source payload hash: 4d01dc241a10f780fd54bdb1144f6b99c78fe0f2edfa26564339faa029064ff1. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

Online Mind2Web

Accuracy

What this benchmark measures: 300 verified tasks across 136 dynamic live websites.

Context: SeeAct and Browser-Use are different agent scaffolds and therefore remain separate comparison cohorts.

Higher is better3 modelsGPT-5: Rank 1 of 3Data date: August 5, 2026

1.GPT-542.33%

2.o339%

3.GPT-4.130.33%

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-online-mind2web-seeact-overall
Metric: accuracy
Scale: 0-100
Participants: 3
Methodology and context: The SeeAct scaffold handles the same 300 tasks across 136 live websites. Total cost is divided by 300.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SeeAct. Agent: SeeAct. Reasoning effort: medium. Cost per task in USD: 0.5702333333333333. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-77eb4b2ad2af. Source payload hash: 77eb4b2ad2af8f3d88a4cfc0781ae6548b8564a04a8e335f67956d934ddc24f3. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

Coding

4 measurements

SciCode

Accuracy

What this benchmark measures: 65 scientific coding problems with 338 subproblems across 16 research fields.

Context: Zero Shot, Tool Calling, and HAL Generalist are not directly comparable scaffolds.

Higher is better4 modelsGPT-5: Rank 2 of 4Data date: August 5, 2026

1.o39.23%

2.GPT-56.15%

3.Claude Sonnet 4.53.08%

4.GPT-4.11.54%

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-scicode-scicode-tool-calling-overall
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: SciCode Tool Calling on 65 main scientific problems. Total cost is divided by 65.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SciCode Tool Calling. Agent: SciCode Tool Calling. Reasoning effort: medium. Cost per task in USD: 2.9772307692307693. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-43b66a7409bd. Source payload hash: 43b66a7409bdc2f55d1a67906b7ba4833adfd9f219de18276136cea9a8709df2. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

ScienceAgentBench

Accuracy

What this benchmark measures: 102 expert-validated tasks for data-driven scientific work.

Context: The scaffold, tools, and code execution are part of the result. HAL reports both 42 and 44 underlying publications in different places.

Higher is better5 modelsGPT-5: Rank 2 of 5Data date: August 6, 2026

1.o333.33%

2.GPT-530.39%

3.GPT-4.124.51%

4.DeepSeek-R123.53%

5.DeepSeek-V315.69%

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-scienceagentbench-sab-self-debug-overall
Metric: accuracy
Scale: 0-100
Participants: 5
Methodology and context: SAB Self-Debug on 102 scientific tasks. Total cost is divided by 102 and excludes caching benefits according to HAL.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SAB Self-Debug. Agent: SAB Self-Debug. Reasoning effort: medium. Cost per task in USD: 0.17901960784313728. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-06-02996f2b8ff9. Source payload hash: 02996f2b8ff940ff8edd458c82f56a7c369a23fa26315f8cce4ffaccf29b850a. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

SWE-bench Verified Mini

Accuracy

What this benchmark measures: A random, fully human-validated subset of 50 GitHub issues from 12 Python repositories.

Context: The small subset is cheaper but statistically less stable than the full 500 tasks. SWE-Agent and HAL Generalist remain separate.

Note (GPT-5): HAL reports min-max ranges for repeated runs. The central value is the reported mean, not a 95% confidence interval.

Higher is better4 modelsGPT-5: Rank 1 of 4Data date: August 5, 2026

1.GPT-546%

1.o346%

Note: HAL reports min-max ranges for repeated runs. The central value is the reported mean, not a 95% confidence interval.

3.GPT-4.144%

Note: HAL reports min-max ranges for repeated runs. The central value is the reported mean, not a 95% confidence interval.

4.DeepSeek-V324%

Note: HAL reports min-max ranges for repeated runs. The central value is the reported mean, not a 95% confidence interval.

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-swebench-verified-mini-swe-agent-overall
Metric: accuracy
Scale: 0-100
Participants: 4
Methodology and context: SWE-Agent on the same random 50-task subset. Total cost is divided by 50.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SWE-Agent. Agent: SWE-Agent. Reasoning effort: medium. Cost per task in USD: 3.2586. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-018a4ac8da36. Source payload hash: 018a4ac8da36b104fce59003162f8ef071d976f750d71a5dd2b5603c8b81f348. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

USACO

Accuracy

What this benchmark measures: 307 algorithmic programming tasks from Bronze through Platinum with exhaustive tests.

Context: The benchmark measures competitive programming, not repository navigation, maintainability, or product experience.

Higher is better5 modelsGPT-5: Rank 1 of 5Data date: August 6, 2026

1.GPT-569.06%

2.o346.25%

3.GPT-4.144.95%

4.DeepSeek-V339.09%

5.DeepSeek-R138.11%

Scale: 0% - 100%

Source: Princeton HAL

CC BY 4.0

gradually.ai

Methodology and context

Comparison cohort: hal-usaco-usaco-episodic-semantic-overall
Metric: accuracy
Scale: 0-100
Participants: 5
Methodology and context: USACO Episodic + Semantic on the same 307-problem split. Total cost is divided by 307 and excludes caching benefits.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: USACO Episodic + Semantic. Agent: USACO Episodic + Semantic. Reasoning effort: medium. Cost per task in USD: 0.37990228013029315. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-06-6c8514acdb83. Source payload hash: 6c8514acdb833c98ee9a4fd7e0206a3a7139d4334452da7c4f025adf20559f89. Parser version: curated-v1. Imported at: 2026-09-04

Source: Princeton HAL

Recorded measurement details

Every observation retains its source value and published test conditions.

FACTS Benchmark Suite: Document grounding (Show measurement, test conditions, and source)

Source value: 69.6
Score: 69.6
Metric: accuracy
Unit: %
Benchmark version: FACTS Benchmark Suite v2
Category: knowledge
Task: grounding
Task label: Document grounding
Direction: Higher is better
Comparison cohort: facts-suite-v2:official:grounding
Confidence interval: 2.1
Source type: official-leaderboard
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-08-09
Methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-e3c06a111c89. Source payload hash: e3c06a111c89025d960a0ff8915359c292a403d8d532eb38414ef7fa8844111c. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.

Source Google DeepMind

FACTS Benchmark Suite: Multimodal factuality (Show measurement, test conditions, and source)

Source value: 44.1
Score: 44.1
Metric: accuracy
Unit: %
Benchmark version: FACTS Benchmark Suite v2
Category: knowledge
Task: multimodal
Task label: Multimodal factuality
Direction: Higher is better
Comparison cohort: facts-suite-v2:official:multimodal
Confidence interval: 3.5
Source type: official-leaderboard
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-08-09
Methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-8bdaaf6bd0f7. Source payload hash: 8bdaaf6bd0f7b4c78d4b8590ae3b79dd006230b7b96e8a7012f1c498f52f7632. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.

Source Google DeepMind

FACTS Benchmark Suite: FACTS overall score (Show measurement, test conditions, and source)

Source value: 61.8
Score: 61.8
Metric: FACTS score
Unit: %
Benchmark version: FACTS Benchmark Suite v2
Category: knowledge
Task: overall
Task label: FACTS overall score
Direction: Higher is better
Comparison cohort: facts-suite-v2:official:overall
Source type: official-leaderboard
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-08-09
Methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-a7084e38f392. Source payload hash: a7084e38f392ecbca770296346f79b4fc382d667f0425a3b16a736837116f83e. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.

Source Google DeepMind

FACTS Benchmark Suite: Parametric knowledge (Show measurement, test conditions, and source)

Source value: 55.8
Score: 55.8
Metric: accuracy
Unit: %
Benchmark version: FACTS Benchmark Suite v2
Category: knowledge
Task: parametric
Task label: Parametric knowledge
Direction: Higher is better
Comparison cohort: facts-suite-v2:official:parametric
Confidence interval: 2.1
Source type: official-leaderboard
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-08-09
Methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-660579aeda2e. Source payload hash: 660579aeda2ec67dd0f27d69abe37808c7f28454186818581a3c3eea4b8bc463. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.

Source Google DeepMind

FACTS Benchmark Suite: Factuality with search (Show measurement, test conditions, and source)

Source value: 77.7
Score: 77.7
Metric: accuracy
Unit: %
Benchmark version: FACTS Benchmark Suite v2
Category: knowledge
Task: search
Task label: Factuality with search
Direction: Higher is better
Comparison cohort: facts-suite-v2:official:search
Confidence interval: 1.6
Source type: official-leaderboard
Evaluator: Google DeepMind
Status: active
Retrieved at: 2026-08-09
Methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Google DeepMind. Dataset type: public. Leaderboard date: 2025-12-11. Source ID: google-deepmind. Evidence capture ID: curated-google-deepmind-2026-08-09-1afb059d0a2f. Source payload hash: 1afb059d0a2f06d37090f7953ae08ce697a9d76b044632c76dd6722ec51da362. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: Public and private splits. Document, image, parametric knowledge, and standardized web search remain separate.

Source Google DeepMind

AssistantBench: Accuracy (Show measurement, test conditions, and source)

Source value: 35.23
Score: 35.23
Metric: accuracy
Unit: %
Benchmark version: HAL 33-task public set
Category: agentic
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-assistantbench-browser-use-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-05
Methodology: One shared Browser-Use scaffold solves 33 verified HAL tasks. Total cost is divided by 33.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: Browser-Use. Agent: Browser-Use. Reasoning effort: medium. Cost per task in USD: 1.2633333333333332. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-b037c99662cf. Source payload hash: b037c99662cf1205a80a9c7650f605db0cac75c65b50792d387a36d55e9bb080. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: One shared Browser-Use scaffold solves 33 verified HAL tasks. Total cost is divided by 33.

Source Princeton HAL

Online Mind2Web: Accuracy (Show measurement, test conditions, and source)

Source value: 32
Score: 32
Metric: accuracy
Unit: %
Benchmark version: 300-task live set
Category: agentic
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-online-mind2web-browser-use-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-05
Methodology: The Browser-Use scaffold handles the same 300 tasks across 136 live websites. Total cost is divided by 300.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: Browser-Use. Agent: Browser-Use. Reasoning effort: medium. Cost per task in USD: 2.4543666666666666. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-4d01dc241a10. Source payload hash: 4d01dc241a10f780fd54bdb1144f6b99c78fe0f2edfa26564339faa029064ff1. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: The Browser-Use scaffold handles the same 300 tasks across 136 live websites. Total cost is divided by 300.

Source Princeton HAL

Online Mind2Web: Accuracy (Show measurement, test conditions, and source)

Source value: 42.33
Score: 42.33
Metric: accuracy
Unit: %
Benchmark version: 300-task live set
Category: agentic
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-online-mind2web-seeact-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-05
Methodology: The SeeAct scaffold handles the same 300 tasks across 136 live websites. Total cost is divided by 300.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SeeAct. Agent: SeeAct. Reasoning effort: medium. Cost per task in USD: 0.5702333333333333. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-77eb4b2ad2af. Source payload hash: 77eb4b2ad2af8f3d88a4cfc0781ae6548b8564a04a8e335f67956d934ddc24f3. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: The SeeAct scaffold handles the same 300 tasks across 136 live websites. Total cost is divided by 300.

Source Princeton HAL

SciCode: Accuracy (Show measurement, test conditions, and source)

Source value: 6.15
Score: 6.15
Metric: accuracy
Unit: %
Benchmark version: 65-problem main set
Category: coding
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-scicode-scicode-tool-calling-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-05
Methodology: SciCode Tool Calling on 65 main scientific problems. Total cost is divided by 65.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SciCode Tool Calling. Agent: SciCode Tool Calling. Reasoning effort: medium. Cost per task in USD: 2.9772307692307693. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-43b66a7409bd. Source payload hash: 43b66a7409bdc2f55d1a67906b7ba4833adfd9f219de18276136cea9a8709df2. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: SciCode Tool Calling on 65 main scientific problems. Total cost is divided by 65.

Source Princeton HAL

ScienceAgentBench: Accuracy (Show measurement, test conditions, and source)

Source value: 30.39
Score: 30.39
Metric: accuracy
Unit: %
Benchmark version: verified 102-task set
Category: coding
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-scienceagentbench-sab-self-debug-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-06
Methodology: SAB Self-Debug on 102 scientific tasks. Total cost is divided by 102 and excludes caching benefits according to HAL.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SAB Self-Debug. Agent: SAB Self-Debug. Reasoning effort: medium. Cost per task in USD: 0.17901960784313728. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-06-02996f2b8ff9. Source payload hash: 02996f2b8ff940ff8edd458c82f56a7c369a23fa26315f8cce4ffaccf29b850a. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: SAB Self-Debug on 102 scientific tasks. Total cost is divided by 102 and excludes caching benefits according to HAL.

Source Princeton HAL

SWE-bench Verified Mini: Accuracy (Show measurement, test conditions, and source)

Source value: 46
Score: 46
Metric: accuracy
Unit: %
Benchmark version: 50-task random verified subset
Category: coding
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-swebench-verified-mini-swe-agent-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-05
Methodology: SWE-Agent on the same random 50-task subset. Total cost is divided by 50.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: SWE-Agent. Agent: SWE-Agent. Reasoning effort: medium. Cost per task in USD: 3.2586. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-05-018a4ac8da36. Source payload hash: 018a4ac8da36b104fce59003162f8ef071d976f750d71a5dd2b5603c8b81f348. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: SWE-Agent on the same random 50-task subset. Total cost is divided by 50.
Context: HAL reports min-max ranges for repeated runs. The central value is the reported mean, not a 95% confidence interval.

Source Princeton HAL

USACO: Accuracy (Show measurement, test conditions, and source)

Source value: 69.06
Score: 69.06
Metric: accuracy
Unit: %
Benchmark version: HAL 307-problem set
Category: coding
Task: overall
Task label: Accuracy
Direction: Higher is better
Comparison cohort: hal-usaco-usaco-episodic-semantic-overall
Source type: independent-evaluator
Evaluator: Princeton HAL
Status: active
Retrieved at: 2026-08-06
Methodology: USACO Episodic + Semantic on the same 307-problem split. Total cost is divided by 307 and excludes caching benefits.. Source model ID: curated/gpt-5. Source model name: GPT-5. Provider reported by source: OpenAI. Evaluator: Princeton HAL. Dataset type: public. Harness: USACO Episodic + Semantic. Agent: USACO Episodic + Semantic. Reasoning effort: medium. Cost per task in USD: 0.37990228013029315. Source ID: princeton-hal. Evidence capture ID: curated-princeton-hal-2026-08-06-6c8514acdb83. Source payload hash: 6c8514acdb833c98ee9a4fd7e0206a3a7139d4334452da7c4f025adf20559f89. Parser version: curated-v1. Imported at: 2026-09-04
Comparison methodology: USACO Episodic + Semantic on the same 307-problem split. Total cost is divided by 307 and excludes caching benefits.

Source Princeton HAL

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.

GPT-5

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)

Evidence

Sources and data date

Every statement links to its underlying documentation or leaderboard.

Speed: OpenAI, High effort. Output speed after the first streaming chunk, not total response time. Classified as deprecated by Artificial Analysis. Artificial Analysis maintains only the default 10K-input workload there. Retrieved September 8, 2026.
Artificial Analysis Methodology
Price: Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs.
Pricing
Input: Documented modalities. Retrieved September 8, 2026.
Model documentation
Output: Documented modalities. Retrieved September 8, 2026.
Model documentation

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 API and release pages do not publish total or active parameter counts.

Not found in the inspected sources

independent-benchmarks. This pass used the exact OpenAI model-column tables; no separate independent evaluator record was added.

Model metadata (retrieved September 8, 2026)Source
Aider (retrieved September 2, 2026)Source
Google DeepMind (retrieved August 9, 2026)Source
Princeton HAL (retrieved August 5, 2026)Source
Princeton HAL (retrieved August 5, 2026)Source
Princeton HAL (retrieved August 5, 2026)Source
Princeton HAL (retrieved August 6, 2026)Source
Princeton HAL (retrieved August 5, 2026)Source
Princeton HAL (retrieved August 6, 2026)Source
Scale AI (retrieved September 2, 2026)Source
MCP-Universe (retrieved August 9, 2026)Source
METR (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
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
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved August 11, 2026; September 4, 2026)Source
Vals AI (retrieved September 4, 2026)Source
OpenAI (retrieved September 8, 2026)Source
Artificial Analysis (retrieved September 8, 2026)Source
Speed measurement methodologySource

GPT-5

Published benchmarks

Knowledge

FACTS Benchmark Suite

FACTS Benchmark Suite

FACTS Benchmark Suite

FACTS Benchmark Suite

FACTS Benchmark Suite

Agent tasks

AssistantBench

Online Mind2Web

Online Mind2Web

Coding

SciCode

ScienceAgentBench

SWE-bench Verified Mini

USACO

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