Llama 4 Maverick

Meta
Llama 4 Maverick brought a natively multimodal mixture-of-experts model to Meta’s open Llama line. Its weights are available under the Llama 4 Community License, so the checkpoint can be run independently.
Maverick combines image understanding with a context window of about one million tokens and a mixture-of-experts design. Llama 4 Scout is the more efficient sibling with a much larger context window. Both remain self-hostable open weights under the Llama 4 Community License, independently of any hosted route.
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 below 1 / 100
provisional
Estimated without own results in: Mathematics
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 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 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 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 below 1 / 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 16 test families and 12 of 16 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): 2.68 % · 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: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 2.08 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 50 %
Family weight in the fixed test panel (informative): 0 % · Coverage of this test family: 100 %
Family weight in the fixed test panel (informative): 2.68 % · 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 | 23 |
| Scale AI Evals | 6 | 0 | 6 |
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 4
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.
higher is betterAs of August 26, 2026Scale: 0% - 100%
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.
higher is betterAs of August 26, 2026Scale: 0% - 100%
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.
higher is betterAs of August 26, 2026Scale: 0% - 100%
Overall
1,490 tasks test adaptive explanations, feedback, and hints across six STEM subjects. A Claude 4 Sonnet judge scores weighted rubrics, so strong results do not prove safe tutoring of real learners.
higher is betterAs of August 26, 2026Scale: 0% - 100%
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%
Overall
1,204 visual tasks require image manipulation, Python, web search, or calculator use with at most 20 tool calls. The score belongs to the complete multimodal tool system and cannot isolate the model alone.
higher is betterAs of August 26, 2026Scale: 0% - 100%
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
Page 1 of 4
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.

A community classic for free-form SVG drawing.
Llama 4 Maverick

Reasoning (requested): not specified
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)45 / 100
| Specification | Value and source |
|---|---|
| Weights | Open weightsSource |
| Access | API, LocalSource |
| Parameters | 400BSource |
| Active parameters | 17BSource |
| Context window | 1,048,576 tokensSource |
| Knowledge cutoff | August 2024Source |
| Notes | 128 experts, 17B active per tokenSource |
| License | Llama 4 Community LicenseSource |
| Release | 2025-04-05Source |
| Parameters | 400 BSource |
| Active parameters | 17 BSource |
| Architecture | early-fusion sparse mixture-of-expertsSource |
| Input | Text, ImageSource |
| Output | Text, codeSource |
| Languages | 12Source |
| Training tokens | ~22TSource |
| Knowledge cutoff | 2024-08Source |
Previous generation in the directory
Meta's official Llama 4 launch blog states 'Llama 4 Maverick offers high quality at a lower price compared to Llama 3.3 70B' and that the Llama 4 models are 'more powerful than all previous generation Llama models'.
Meta AI, "The Llama 4 herd" (2026-09-19)