Language modelDeprecated

GPT-4.1 nano

OpenAI

Released: April 14, 2025
Data date: September 8, 2026
Successor: GPT-5 nano

Benchmark-Meta-Score: 0 / 100
Provisional, not yet placed. Standard error ± 0
Speed: 160.4 tokens/s
Output throughput
Price: No current rate
Historical rates under Costs
Input: Text, Image
Output: Text

Although it was the 4.1 family’s smallest API tier, GPT-4.1 nano handled a roughly one-million-token context window. OpenAI designed it for fast classification and autocomplete.

It was followed by GPT-5 nano.

Specifications and access

Specification	Value and source
Weights	ProprietarySource
Context window	1,047,576 tokensSource
Knowledge cutoff	June 1, 2024Source
Notes	1M context, parameter count not disclosedSource
API model ID	gpt-4.1-nanoSource
Maximum output	32,768 tokensSource
Input	Text, ImageSource
Output	TextSource
Reasoning mode	noneSource
API interfaces	Responses API, Chat Completions APISource
Tool calling	YesSource
Structured output	YesSource
Release	2025-04-14Source

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 - 2026-10-19

Overall index

Index below 1 / 100

provisional

Estimated without own results in: Agents

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 14
Test families: 13 / 65
Sources: 3
Share of the fixed test panel (informative): 25 %

Coding

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 1
Test families: 1 / 15
Sources: 1
Share of the fixed test panel (informative): 7 %

Reasoning

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 1
Test families: 1 / 7
Sources: 1
Share of the fixed test panel (informative): 7 %

Mathematics

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 2
Test families: 2 / 5
Sources: 1
Share of the fixed test panel (informative): 40 %

Knowledge

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 1
Test families: 1 / 3
Sources: 1
Share of the fixed test panel (informative): 33 %

General

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 1
Test families: 1 / 5
Sources: 1
Share of the fixed test panel (informative): 20 %

Finance

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 3
Test families: 3 / 7
Sources: 1
Share of the fixed test panel (informative): 43 %

Legal

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 1
Test families: 1 / 4
Sources: 1
Share of the fixed test panel (informative): 25 %

Health

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 1
Test families: 1 / 3
Sources: 1
Share of the fixed test panel (informative): 33 %

Multimodal

Index below 1 / 100

provisional

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

Standard error: ± 0.0
95% interval: 0.0 to 0.0
Test series: 3
Test families: 2 / 4
Sources: 3
Share of the fixed test panel (informative): 50 %

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 13 test families and 12 of 14 raw measurements are listed here. The complete calculation for every model is available in the JSON export.

Test family: IFBench
General · Mean rank percentile of the family: 10.9 / 100
Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-ifbench
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.14 %
  Raw value:
  32.0408163265306 %
  Metric:
  IFBench prompt-level loose accuracy (pass@1)
  Version:
  AA test set: 294 questions
  Participating models:
  70
  Rank percentile within the cohort:
  10.9 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:tokens=0:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-ifbench","AA test set: 294 questions","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Tool policy:
  Single-turn answer evaluated with the official IFBench source code
  Attempts:
  pass@1, 5 repeats per question
  Setup and model parameters
  Exact AA configuration: GPT-4.1 nano (release gpt-4-1-nano, row gpt-4-1-nano, UUID 72c358fd-7d45-4d68-89aa-699743710924, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning
  Exact AA configuration: GPT-4.1 nano (release gpt-4-1-nano, row gpt-4-1-nano, UUID 72c358fd-7d45-4d68-89aa-699743710924, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/72c358fd-7d45-4d68-89aa-699743710924/gpt-4-1-nano/gpt-4-1-nano. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: GPT-4.1 nano. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Score display: 32.0%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: GPQA Diamond
Reasoning · Mean rank percentile of the family: 9.2 / 100
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 50 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-gpqa
  Series weight within the test family:
  50 %
  Nominal contribution to the overall panel:
  0.77 %
  Raw value:
  50.758 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  77
  Rank percentile within the cohort:
  9.2 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-gpqa:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-gpqa:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 4.192. Standard error: 2.457. Cost per task in USD: 0.000488. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-10-15-951eb5224097. Source payload hash: 951eb5224097733a9d09d06c14908ae2980e2e640e539f2c284877c5092d7077. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: MMMU-Pro
Multimodal · Mean rank percentile of the family: 0.9 / 100
Family weight in the fixed test panel (informative): 2.68 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-mmmu-pro
  Series weight within the test family:
  50 %
  Nominal contribution to the overall panel:
  1.34 %
  Raw value:
  40.1156069364162 %
  Metric:
  pass@1 accuracy
  Version:
  AA cohort: 1,730 questions
  Participating models:
  57
  Rank percentile within the cohort:
  1.8 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-mmmu-pro:AA cohort: 1,730 questions:standalone:non-reasoning:tokens=0:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-mmmu-pro","AA cohort: 1,730 questions","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Tool policy:
  Ten-option multiple choice; regex answer extraction
  Attempts:
  pass@1, one run per question
  Setup and model parameters
  Exact AA configuration: GPT-4.1 nano (release gpt-4-1-nano, row gpt-4-1-nano, UUID 72c358fd-7d45-4d68-89aa-699743710924, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. The AA run covers 1,730 questions with ten answer options. Answers are extracted by regex and scored pass@1.. Tool policy: Ten-option multiple choice; regex answer extraction. Attempts: pass@1, one run per question. Reasoning: non-reasoning
  Exact AA configuration: GPT-4.1 nano (release gpt-4-1-nano, row gpt-4-1-nano, UUID 72c358fd-7d45-4d68-89aa-699743710924, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. The AA run covers 1,730 questions with ten answer options. Answers are extracted by regex and scored pass@1.. Source model ID: artificial-analysis/72c358fd-7d45-4d68-89aa-699743710924/gpt-4-1-nano/gpt-4-1-nano. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: GPT-4.1 nano. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Ten-option multiple choice; regex answer extraction. Attempts: pass@1, one run per question. Reasoning: non-reasoning. Score display: 40.1%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-mmmu
  Series weight within the test family:
  50 %
  Nominal contribution to the overall panel:
  1.34 %
  Raw value:
  55.055 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  48
  Rank percentile within the cohort:
  0.0 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-mmmu:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-mmmu:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 7.251. Standard error: 1.196. Cost per task in USD: 0.000566. Source ID: vals-ai. Evidence capture ID: vals-mmmu-2026-10-15-29395186eddd. Source payload hash: 29395186eddd2c70601228c584a14b3c1d8f9dba4b68256561b46b0dd03c635a. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: CorpFin v2
Finance · Mean rank percentile of the family: 0.0 / 100
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-corp-fin-v2
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.19 %
  Raw value:
  42.075 %
  Metric:
  accuracy
  Version:
  2
  Participating models:
  75
  Rank percentile within the cohort:
  0.0 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-corp-fin-v2:2:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-corp-fin-v2:2:overall
  Dataset type:
  private
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 4.954. Standard error: 0.968. Cost per task in USD: 0.006454. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: LiveCodeBench v6
Coding · Mean rank percentile of the family: 7.8 / 100
Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-lcb
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  0.71 %
  Raw value:
  42.718 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  78
  Rank percentile within the cohort:
  7.8 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-lcb:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-lcb:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 11.801. Standard error: 1.152. Cost per task in USD: 0.000298. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-10-15-1a7c7dabc9d9. Source payload hash: 1a7c7dabc9d95a0182886b1edc42623987cac2095a0d407a5e334f37acdfa2f5. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: LegalBench
Legal · Mean rank percentile of the family: 2.6 / 100
Family weight in the fixed test panel (informative): 2.08 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-legal-bench
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.08 %
  Raw value:
  61.056 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  79
  Rank percentile within the cohort:
  2.6 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-legal-bench:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-legal-bench:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 0.357. Standard error: 0.595. Source ID: vals-ai. Evidence capture ID: vals-legal_bench-2026-10-15-c8486ba756ba. Source payload hash: c8486ba756baa5cd3955d035edff67440e8db23e69d62ad1a9e18d15b8c4bc52. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: MATH 500
Mathematics · Mean rank percentile of the family: 22.7 / 100
Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-math500
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.14 %
  Raw value:
  80.2 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  23
  Rank percentile within the cohort:
  22.7 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-math500:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-math500:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 0. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Reasoning effort: high. Temperature: 0. Maximum output tokens: 32768. Latency in seconds: 3.372. Standard error: 1.782. Cost per task in USD: 0.000314. Source ID: vals-ai. Evidence capture ID: vals-math500-2026-10-15-435ec4da1112. Source payload hash: 435ec4da1112362c0a6bb4afeab53bf7b0faf2000e1ddbe1a878735dca1fe35b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: MedQA
Health · Mean rank percentile of the family: 7.7 / 100
Family weight in the fixed test panel (informative): 2.78 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-medqa
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.78 %
  Raw value:
  68.225 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  40
  Rank percentile within the cohort:
  7.7 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-medqa:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-medqa:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 1.416. Standard error: 0.425. Cost per task in USD: 0.000154. Source ID: vals-ai. Evidence capture ID: vals-medqa-2026-10-15-d67905978d93. Source payload hash: d67905978d932f64698ebe9e230d9519e5bae1c9d7176dda8379dc3874b6f90f. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: MGSM
Mathematics · Mean rank percentile of the family: 0.0 / 100
Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-mgsm
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.14 %
  Raw value:
  69.273 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  30
  Rank percentile within the cohort:
  0.0 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-mgsm:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-mgsm:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 1.458. Standard error: 2.799. Cost per task in USD: 0.000129. Source ID: vals-ai. Evidence capture ID: vals-mgsm-2026-10-15-1b87f953e52c. Source payload hash: 1b87f953e52c00bf4bdd90c3add26165f644eeb7a74a8382d2a0324ff0cc1040. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: MMLU-Pro
Knowledge · Mean rank percentile of the family: 2.6 / 100
Family weight in the fixed test panel (informative): 3.57 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-mmlu-pro
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  3.57 %
  Raw value:
  63.479 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  78
  Rank percentile within the cohort:
  2.6 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-mmlu-pro:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-mmlu-pro:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 2.403. Standard error: 0.462. Cost per task in USD: 0.000337. Source ID: vals-ai. Evidence capture ID: vals-mmlu_pro-2026-10-15-dab025507f46. Source payload hash: dab025507f46c716dd8d145e90ec0a737d9c56219853f41e4b1cc3c5e8c222b4. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: MortgageTax
Finance · Mean rank percentile of the family: 7.7 / 100
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-mortgage-tax
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.19 %
  Raw value:
  52.822 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  53
  Rank percentile within the cohort:
  7.7 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-mortgage-tax:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-mortgage-tax:1:overall
  Dataset type:
  private
  Setup and model parameters
  Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768
  Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 3.858. Standard error: 0.995. Cost per task in USD: 0.001003. Source ID: vals-ai. Evidence capture ID: vals-mortgage_tax-2026-10-15-e5c844561f97. Source payload hash: e5c844561f97d119d66d70269e726ecfb68be79eae39acf14ead3e95422e648c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: TaxEval v2
Finance · Mean rank percentile of the family: 3.9 / 100
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
Test family: VISTA
Multimodal · Mean rank percentile of the family: 0.0 / 100
Family weight in the fixed test panel (informative): 2.68 % · Coverage of this test family: 100 %

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
Artificial Analysis	3	1	12
Vals AI	12	0	62
Scale AI Evals	1	0	1

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.

6 measurements from these sources still lack a verified test-family identity and do not increase the test-series count.

Published benchmarks

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-4.1-nano. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 26.55% ± 0.35. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-a4d5ac758f60. Source payload hash: a4d5ac758f60c33e7a59f3f05e6e0ddfe41226fbcd27fb88bced23199c68c38f. Parser version: curated-v1. Imported at: 2026-10-15

Source: Scale AI

Finance

CorpFin v2

Sub-task	GPT-4.1 nano (This model)	GPT-4.1	GPT-4.1 mini
Exact Pages	48.95%	62.82% (best value in this row)	59.44%
Max Fitting Context	38.46%	60.84% (best value in this row)	55.01%
Overall	42.08%	63.05% (best value in this row)	57.93%
Shared Max Context	38.81%	65.5% (best value in this row)	59.32%

higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows

Methodology and context

Metric: accuracy

Reasoning

GPQA Diamond

Sub-task	GPT-4.1 nano (This model)	GPT-4.1 mini	GPT-4.1
Few-Shot CoT	51.01%	64.65% (best value in this row)	64.14%
Zero-Shot CoT	50.51%	71.21% (best value in this row)	66.67%
Overall	50.76%	67.93% (best value in this row)	65.4%

higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows

Methodology and context

Metric: accuracy

Coding

LiveCodeBench v6

Sub-task	GPT-4.1 nano (This model)	GPT-4.1 mini	GPT-4.1
Easy	85.4%	95.34% (best value in this row)	94.72%
Hard	8.29%	21.43% (best value in this row)	13.14%
Medium	34.47%	57.7% (best value in this row)	56.14%
Overall	42.72%	58.16% (best value in this row)	54.67%

higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows

Methodology and context

Metric: accuracy

Recorded measurement details

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

Source value: 26.55
Score: 26.55
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: 0.35
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-4.1-nano. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Evaluator: Scale AI. Dataset type: public. Attempts: pass@1. Score display: 26.55% ± 0.35. Source ID: scale-ai. Evidence capture ID: curated-scale-ai-2026-08-26-a4d5ac758f60. Source payload hash: a4d5ac758f60c33e7a59f3f05e6e0ddfe41226fbcd27fb88bced23199c68c38f. 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

CorpFin v2: Exact Pages (Show measurement, test conditions, and source)

Source value: 48.951
Score: 48.951
Metric: accuracy
Unit: %
Benchmark version: 2
Category: finance
Task: exact_pages
Task label: Exact Pages
Direction: Higher is better
Comparison cohort: vals-corp-fin-v2:2:exact_pages
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 3.869. Standard error: 1.707. Cost per task in USD: 0.000183. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

CorpFin v2: Max Fitting Context (Show measurement, test conditions, and source)

Source value: 38.462
Score: 38.462
Metric: accuracy
Unit: %
Benchmark version: 2
Category: finance
Task: max_fitting_context
Task label: Max Fitting Context
Direction: Higher is better
Comparison cohort: vals-corp-fin-v2:2:max_fitting_context
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 5.948. Standard error: 1.661. Cost per task in USD: 0.012861. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

CorpFin v2: Overall (Show measurement, test conditions, and source)

Source value: 42.075
Score: 42.075
Metric: accuracy
Unit: %
Benchmark version: 2
Category: finance
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-corp-fin-v2:2:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 4.954. Standard error: 0.968. Cost per task in USD: 0.006454. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

CorpFin v2: Shared Max Context (Show measurement, test conditions, and source)

Source value: 38.811
Score: 38.811
Metric: accuracy
Unit: %
Benchmark version: 2
Category: finance
Task: shared_max_context
Task label: Shared Max Context
Direction: Higher is better
Comparison cohort: vals-corp-fin-v2:2:shared_max_context
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 5.045. Standard error: 1.664. Cost per task in USD: 0.006318. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

GPQA Diamond: Few-Shot CoT (Show measurement, test conditions, and source)

Source value: 51.01
Score: 51.01
Metric: accuracy
Unit: %
Benchmark version: 1
Category: reasoning
Task: diamond_few_shot_cot
Task label: Few-Shot CoT
Direction: Higher is better
Comparison cohort: vals-gpqa:1:diamond_few_shot_cot
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 4.255. Standard error: 3.438. Cost per task in USD: 0.000576. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-10-15-951eb5224097. Source payload hash: 951eb5224097733a9d09d06c14908ae2980e2e640e539f2c284877c5092d7077. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

GPQA Diamond: Zero-Shot CoT (Show measurement, test conditions, and source)

Source value: 50.505
Score: 50.505
Metric: accuracy
Unit: %
Benchmark version: 1
Category: reasoning
Task: diamond_zero_shot_cot
Task label: Zero-Shot CoT
Direction: Higher is better
Comparison cohort: vals-gpqa:1:diamond_zero_shot_cot
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 4.128. Standard error: 3.512. Cost per task in USD: 0.0004. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-10-15-951eb5224097. Source payload hash: 951eb5224097733a9d09d06c14908ae2980e2e640e539f2c284877c5092d7077. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

GPQA Diamond: Overall (Show measurement, test conditions, and source)

Source value: 50.758
Score: 50.758
Metric: accuracy
Unit: %
Benchmark version: 1
Category: reasoning
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-gpqa:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 4.192. Standard error: 2.457. Cost per task in USD: 0.000488. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-10-15-951eb5224097. Source payload hash: 951eb5224097733a9d09d06c14908ae2980e2e640e539f2c284877c5092d7077. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

LiveCodeBench v6: Easy (Show measurement, test conditions, and source)

Source value: 85.404
Score: 85.404
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: easy
Task label: Easy
Direction: Higher is better
Comparison cohort: vals-lcb:1:easy
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 2.331. Standard error: 1.968. Cost per task in USD: 0.000107. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-10-15-1a7c7dabc9d9. Source payload hash: 1a7c7dabc9d95a0182886b1edc42623987cac2095a0d407a5e334f37acdfa2f5. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

LiveCodeBench v6: Hard (Show measurement, test conditions, and source)

Source value: 8.286
Score: 8.286
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: hard
Task label: Hard
Direction: Higher is better
Comparison cohort: vals-lcb:1:hard
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 22.324. Standard error: 1.473. Cost per task in USD: 0.000588. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-10-15-1a7c7dabc9d9. Source payload hash: 1a7c7dabc9d95a0182886b1edc42623987cac2095a0d407a5e334f37acdfa2f5. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

LiveCodeBench v6: Medium (Show measurement, test conditions, and source)

Source value: 34.465
Score: 34.465
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: medium
Task label: Medium
Direction: Higher is better
Comparison cohort: vals-lcb:1:medium
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 10.747. Standard error: 2.428. Cost per task in USD: 0.000199. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-10-15-1a7c7dabc9d9. Source payload hash: 1a7c7dabc9d95a0182886b1edc42623987cac2095a0d407a5e334f37acdfa2f5. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

LiveCodeBench v6: Overall (Show measurement, test conditions, and source)

Source value: 42.718
Score: 42.718
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-lcb:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: openai/gpt-4.1-nano-2025-04-14. Source model name: GPT-4.1 nano. Provider reported by source: OpenAI. Model snapshot: gpt-4.1-nano-2025-04-14. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768. Latency in seconds: 11.801. Standard error: 1.152. Cost per task in USD: 0.000298. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-10-15-1a7c7dabc9d9. Source payload hash: 1a7c7dabc9d95a0182886b1edc42623987cac2095a0d407a5e334f37acdfa2f5. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Maximum output tokens: 32768

Page 1 of 9

Predecessor and successor

Sources and data date

Type	Evidence and data date
Speed	OpenAI. 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. Source: Artificial Analysis · Artificial Analysis (retrieved September 8, 2026)Speed measurement methodology
Input	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Model metadata (retrieved September 8, 2026) · GPT-4.1 nano API model documentation · Editorial description reviewed October 4, 2026
Output	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Model metadata (retrieved September 8, 2026) · GPT-4.1 nano API model documentation · Editorial description reviewed October 4, 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 API and release pages do not publish parameter counts.
Not found in the inspected sources	independent-benchmarks Original wording Only exact OpenAI appendix columns were used in this artifact.
Additional source	Context window data
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Scale AI (retrieved August 26, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	OpenAI (retrieved September 8, 2026) · Introducing GPT-4.1 in the API · Editorial description reviewed October 4, 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
Additional source	Introducing GPT-5 for developers (2026-09-19)