GPT-5 mini
Unavailable in this round
OpenAI
GPT-5 mini brought the GPT-5 generation’s additional reasoning to a smaller, lower-cost tier. Compared with GPT-4.1 mini, the emphasis shifted from direct answers to tasks using extra reasoning.
GPT-5 nano covers the family’s still smaller API tier. GPT-5.4 mini later improved the Mini line’s coding and tool use.
The LLM comparison hub's selected-panel index averages rank percentiles from 65 documented comparison cohorts. Benchmark meta-scores are shown in the benchmark section.
Benchmark meta-analysis
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
Index 1.4 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 1.5 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 1.5 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 6.3 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 6.0 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 1.7 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 6.2 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 1.3 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 2.4 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
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 indicatorsAll 26 test families and 12 of 28 raw measurements are listed here. The complete calculation for every model is available in the JSON export.
Family weight in the fixed test panel (informative): 0.89 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 0.89 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 0.89 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 3.57 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 50 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 50 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 2.68 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 50 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 67 %
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.08 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.78 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.78 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.78 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 3.57 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 0 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.68 % · Coverage of this test family: 100 %
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 | 10 | 1 | 29 |
| Vals AI | 19 | 0 | 80 |
| 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.
7 measurements from these sources still lack a verified test-family identity and do not increase the test-series count.
Page 1 of 13
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.
higher is betterAs of August 26, 2026Scale: 0% - 100%
| Sub-task | GPT-5 mini (This model) | GPT-5.4 nano |
|---|---|---|
| Exact Pages | 58.86% | 60.37% (best value in this row) |
| Max Fitting Context | 60.49% | 62.12% (best value in this row) |
| Overall | 60.18% | 61.19% (best value in this row) |
| Shared Max Context | 61.19% (best value in this row) | 61.07% |
higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows
| Sub-task | GPT-5 mini (This model) | GPT-5 | GPT-5 nano |
|---|---|---|---|
| Few-Shot CoT | 78.28% | 84.85% (best value in this row) | 64.14% |
| Zero-Shot CoT | 82.32% | 86.36% (best value in this row) | 62.63% |
| Overall | 80.3% | 85.61% (best value in this row) | 63.38% |
higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows
| Sub-task | GPT-5 mini (This model) | GPT-5 nano | GPT-5 |
|---|---|---|---|
| Easy | 99.07% (best value in this row) | 99.07% (best value in this row) | 98.76% |
| Hard | 71.71% (best value in this row) | 22.29% | 66.29% |
| Medium | 89.03% | 89.3% | 92.69% (best value in this row) |
| Overall | 86.61% (best value in this row) | 70.22% | 85.91% |
higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows
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One SVG prompt and fixed output settings. Captures are shown only with 0 reported reasoning tokens or documented lack of reasoning controls. Identical test conditions do not make the models' internal compute identical.
Unavailable in this round
A community classic for free-form SVG drawing.
GPT-5 mini

Reasoning (requested): high
Visually reviewed. This individual test does not establish overall model quality.
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)29.1 / 100
| Specification | Value and source |
|---|---|
| Weights | ProprietarySource |
| Access | APISource |
| Context window | 400,000 tokensSource |
| Knowledge cutoff | May 31, 2024Source |
| Notes | Smaller, cheaper GPT-5 variant, parameter count not disclosedSource |
| API model ID | gpt-5-miniSource |
| Maximum output | 128,000 tokensSource |
| Input | Text, ImageSource |
| Output | TextSource |
| Reasoning mode | minimal, low, medium, highSource |
| API interfaces | Responses API, Chat Completions APISource |
| Tool calling | YesSource |
| Structured output | YesSource |
| Release | 2025-08-07Source |
OpenAI positions GPT-5 mini as the model to move to from GPT-4.1 mini for more complex, well-defined tasks; unlike the nano tier, no single quoted 'successor' sentence was found, so this is recorded at medium confidence.
GPT-5 mini API documentation (2026-09-19)OpenAI reports GPT-5.4 mini significantly improving over GPT-5 mini on coding, reasoning, multimodal understanding and tool use while running about twice as fast.
Introducing GPT-5.4 mini and nano (2026-09-19)