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Llama 4 Scout

open weights
Meta · released 2025-04-05 · 4 offers

Output 91 tokens/sFirst token 0.7 sContext 10M tokens

Top 4 cheapest providers (Adjusted $/task)

The same list price can give a different adjusted $/task (caching, token efficiency) — click a price for its inputs.

Within the active global provider, residency and confidentiality filters.

#ProviderAdjusted $/task
1DeepInfravia OpenRouter — Price via OpenRouter: OpenRouter's price for DeepInfra's endpoint (https://openrouter.ai/meta-llama/llama-4-scout/providers), collected 2026-11-03.
2AWS Bedrockdirect — Direct price: AWS Bedrock's own price list (https://aws.amazon.com/bedrock/pricing/), collected 2026-11-03.
3Google Vertex AIdirect — Direct price: Google Vertex AI's own price list (https://cloud.google.com/vertex-ai/generative-ai/pricing), collected 2026-11-03.
4Googlevia OpenRouter — Price via OpenRouter: OpenRouter's price for Google's endpoint (https://openrouter.ai/meta-llama/llama-4-scout/providers), collected 2026-11-03.

Composite

3 of 7 inputs · 1 from the model family6 radar axes: DesignArena's two boards share one

4.0

AA Coding v4.xCoding Agent v1.4AA Intelligence (est.)AA AgenticEpoch ECISoftware ECIDesignArenaAA Coding v4.x: percentile 7AA Intelligence (est.): percentile 17AA Agentic: percentile 2Epoch ECI: percentile 7

AA Coding v4.x 8.2Coding Agent v1.4 —AA Intelligence (est.) 2.7AA Agentic 0.5Epoch ECI 129.6Software ECI —DesignArena —/—

Radar: percentile among all models measured on each input; a gap means not measured.

Benchmark sheet

29 of 319 registered benchmark versions · bars show the percentile among all models measured on each benchmark.

3 of 31 values are Meta's own claims (†), not independent measurements; a matching independent result replaces a claim as soon as one exists.

Composite attachments (used in the score, not counted as exact benchmarks):
  • Epoch ECI · attached
Compare this model →

Agentic

  • GDPval-AA v2660 Elo

    GDPval-AA v2 v2 → · Published board — Tests professional knowledge-work deliverables across occupations using AA's Stirrup harness.

  • Terminal-Bench Hard (AA)161.5%

    Terminal-Bench Hard (AA) pinned revision 74221fb → · Published board — Tests a pinned 44-task hard subset of terminal-based work using Terminus 2.

  • Terminal-Bench v2.1 (AA)93.7%

    Terminal-Bench v2.1 (AA) v2.1 → · Published board — Tests terminal-based work on the 89-task verified refresh using Terminus 2.

  • Terminal-Bench v4.0 (AA)180.0%

    Terminal-Bench v4.0 (AA) v4.0 → · Published board — Tests terminal-based work on the 66-task release using mini-SWE-agent v2.4.6.

  • AA Agentic Index20.50

    AA Agentic Index published 2026-09-18 → · Published board — Artificial Analysis publishes one Agentic Index per model, measured on its primary configuration and relayed by OpenRouter's Benchmarks API; the value is attached at family scope on the deterministic representative. Artificial Analysis publishes this board without a version number; we keep the date each result was retained.

Coding

  • SciCode (AA subproblems) v1.0.1221.3%

    SciCode (AA subproblems) v1.0.1 → · Published board — Tests scientific Python programming with scientist-annotated background information.

  • AA Coding Index v4.x · frozen 2026-10-2778.20

    AA Coding Index v4.x · frozen 2026-10-27 → · Published board — Artificial Analysis withdrew the Coding Index with Intelligence Index v5.0 (2026-10-28). These are the last v4.x values, frozen with their own date (2026-10-27).

Instruction-following

  • IFBench (AA single-turn)3739.5%

    IFBench (AA single-turn) published 2026-09-10 → · Published board — Tests precise single-turn instructions with deterministic rule checks.

  • LisanBench word chains, v0.2.0 (Lisan al Gaib)24126.7

    LisanBench word chains, v0.2.0 (Lisan al Gaib) v0.2.0 → · Published board — A model builds the longest chain of English words it can, each differing from the last by one letter, with no repeats and only dictionary words, from 50 starting words; any broken rule ends the chain.

Knowledge

Long-context

  • GDP.pdf (AA)10.0%

    GDP.pdf (AA) published 2026-09-10 → · Published board — Tests professional reasoning over long PDFs with AA document preparation and grading.

  • AA-LCR v1.13027.7%

    AA-LCR v1.1 v1.1 → · Published board — Tests reasoning across multiple long documents with corrected answer keys and grading.

Math

  • AIME 2025 (AA)1714.0%

    AIME 2025 (AA) v2025 → · Published board — Advanced mathematical problem solving on AIME I and II 2025.

Reasoning

  • ARC-AGI-1developer's claim0.5%† self-reported by the developer

    ARC-AGI-1 v1 → · Published board — The first-generation ARC-AGI benchmark measuring passive fluid intelligence, with leaderboard score plotted against cost-per-task.

  • ARC-AGI-2developer's claim0.0%† self-reported by the developer

    ARC-AGI-2 v2 → · Published board — The second-generation ARC-AGI benchmark of passive fluid intelligence, with leaderboard score plotted against cost-per-task.

Safety/Alignment

Science

Tool-use

  • AutomationBench-AA v1.0.610.2%

    AutomationBench-AA v1.0.6 → · Published board — Tests multi-app SaaS workflows through REST tools on a held-out AutomationBench split.

  • τ²-Bench Telecom (AA)1315.5%

    τ²-Bench Telecom (AA) published 2026-09-10 → · Published board — Tests dual-control telecom agents that coordinate tool use with a simulated user.

  • τ³-Banking (AA) v1.0.133.3%

    τ³-Banking (AA) v1.0.1 → · Published board — Tests banking support agents that retrieve policies and change account state through tools.

Uncensored

  • UGI Willingness795.20

    UGI Willingness published 2026-09-10 → · Published board — A private-question evaluation of how far a model follows challenging instructions before refusing or deviating.

  • UGI4331.0

    UGI published 2026-09-10 → · Published board — A private-question suite combining sensitive-topic knowledge with willingness to follow controversial instructions.

Vision

  • MMMU Pro (AA)1852.9%

    MMMU Pro (AA) published 2026-09-10 → · Published board — Tests multimodal understanding using challenging ten-option questions.

  • MMMUdeveloper's claim69.4%† self-reported by the developer

    MMMU published 2026-09-10 → · validation — A multimodal benchmark of 11.5K college-level questions across six disciplines and 30 subjects, with 30 heterogeneous image types.

Writing

  • EQ-Bench Creative Writing v314781 Elo

    EQ-Bench Creative Writing v3 v3 → · Published board — A LLM-judged creative writing benchmark with vocabulary and GPT-slop controls.

  • EQ-Bench Longform Creative Writing v1.11531.1

    EQ-Bench Longform Creative Writing v1.11 → · Published board — LLM-judged benchmark in which models plan and write a short story/novella over 8x 1000-word turns, scored 0-100 across 14 rubric dimensions.

  • UGI Writing2526.7

    UGI Writing published 2026-09-10 → · Published board — A maintainer-owned writing evaluation considering intelligence, style, repetition and length adherence, informed by human preferences.

Unusual results

1 threshold-crossing signal flagged · 27 eligible benchmark families

How flags are calculated

Heuristic screen, not statistical significance: benchmark families are correlated and source uncertainty is unknown. Peer evidence needs ≥ 20 independently measured matched configurations from ≥ 10 distinct model families. A flag needs a directed population z-score of magnitude ≥ 1.5 and a gap of ≥ 1.5 from the leave-one-benchmark-family-out mean z in the same direction, over ≥ 5 other benchmark families.

  • unusually weak SciCode (AA subproblems) v1.0.1
    Why
    Observed: 0.21296 fraction
    Peer mean 0.46304 · peer sd 0.10542 · n 183 · families 125
    Directed z -2.372 · baseline z -0.824 · gap -1.549 · profile n 26
    0.21296 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗
    Evidence
    Axis: SciCode (AA subproblems) · v1.0.1 · Published board
    Exact value: 0.212962962962963 fraction
    Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
    Observation id: aa:adf9a85e-abc3-4f28-937b-db6655cc5238:scicode
Missing coverage · 290 benchmark versions

No result does not mean a zero, or that the model was never tested. Collection failures and disputed versions retain their distinct status.

Token offers by platform · 4 offers (Adjusted $/task)

Click any underlined price to see how it is estimated and where each input comes from. How we calculate adjusted cost.

4 offers within the active global filters; “price n/a” means the provider serves the model but publishes no direct token price we could collect — no other channel's price is shown in its place.

OpenRouter (2)

Prices via OpenRouter — what OpenRouter charges for each provider's endpoint, not the provider's own direct price. Source: openrouter.ai/meta-llama/llama-4-scout/providers, collected 2026-11-03.

DeepInfra
Google

AWS Bedrock (1)

Direct prices from AWS Bedrock's own price list. Source: aws.amazon.com/bedrock/pricing/, collected 2026-11-03.

AWS Bedrock

Google Vertex AI (1)

Direct prices from Google Vertex AI's own price list. Source: cloud.google.com/vertex-ai/generative-ai/pricing, collected 2026-11-03.

Google Vertex AI
Llama 4 Scout — benchmarks & cost | Benchmark Heaven