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Llama 4 Maverick

Added Sep 5, 2025Open weights

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Use in the app

Llama 4 Maverick, a 17 billion active parameter model with 128 experts, is the best multimodal model in its class, beating GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks, while achieving comparable results to the new DeepSeek v3 on reasoning and coding—at less than half the active parameters. Llama 4 Maverick offers a best-in-class performance to cost ratio with an experimental chat version scoring ELO of 1417 on LMArena.

  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.19
Output
$0.65
Cache read
$0.05

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
114 tokens
Longer than 11% of 465 models

Side by side

Llama 4 Maverick compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Llama 4 MaverickMetaThis modelMistral Medium 3.1MistralRemove
ProviderMetaMistral
Discovery tierOpen weightsBalanced
AddedSep 5, 2025Sep 5, 2025
Limits
Context window1.05MBest131.07K
Max output65.54KBest32.77K
Average output114 tokens60 tokens
Pricing, USD per 1M tokens
Input$0.19Best$0.40
Output$0.65Best$2.00
Cache read$0.05—
Performance
Request success (24h)99.6%100.0%Best
Benchmark indexes
Intelligence Index9.3Best9.2
Coding Index16.320.5Best
Math Index19.338.3Best
Capabilities
ToolsYesNo
VisionYesNo
Structured outputYesNo
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Llama 4 Maverick
  • Mistral Medium 3.1

Intelligence Index

  • Llama 4 Maverick9.3
  • Mistral Medium 3.19.2

Llama 4 Maverick: Better than 18% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 4 Maverick: 16.3
    • Mistral Medium 3.1: 20.5Top

  • Math Index

    Composite of the math evaluations

    • Llama 4 Maverick: 19.3
    • Mistral Medium 3.1: 38.3Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 4 Maverick: 0.2%
    • Mistral Medium 3.1: 4.1%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 4 Maverick: 0.0%
    • Mistral Medium 3.1: 0.2%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 4 Maverick: —
    • Mistral Medium 3.1: 530 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 4 Maverick: —
    • Mistral Medium 3.1: 385 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 4 Maverick: 0.8%
    • Mistral Medium 3.1: 1.2%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 4 Maverick: 50.0%Top
    • Mistral Medium 3.1: 42.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 4 Maverick: —
    • Mistral Medium 3.1: 2.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 4 Maverick: 4.9%Top
    • Mistral Medium 3.1: 4.7%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 4 Maverick: 67.1%Top
    • Mistral Medium 3.1: 58.8%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 4 Maverick: 80.9%Top
    • Mistral Medium 3.1: 68.3%

  • IFBench

    Following precise output instructions

    • Llama 4 Maverick: 43.0%Top
    • Mistral Medium 3.1: 39.8%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 4 Maverick: 0.0%Top
    • Mistral Medium 3.1: 0.0%Top

  • SciCode

    Scientific computing in Python

    • Llama 4 Maverick: 31.7%
    • Mistral Medium 3.1: 32.4%Top

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 4 Maverick: 39.7%
    • Mistral Medium 3.1: 40.6%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 4 Maverick: 19.3%
    • Mistral Medium 3.1: 38.3%Top

  • AIME

    Competition mathematics

    • Llama 4 Maverick: 39.0%
    • Mistral Medium 3.1: —

  • MATH-500

    Competition math problems

    • Llama 4 Maverick: 88.9%
    • Mistral Medium 3.1: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 4 Maverick: 6.8%
    • Mistral Medium 3.1: 10.6%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 4 Maverick: 17.8%
    • Mistral Medium 3.1: 40.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 4 Maverick: 50.0%Top
    • Mistral Medium 3.1: 42.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
6.7%
About $0.18 per 1M input tokens with caching
Request success (24h)
99.6%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 87.5%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-4-maverick",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="meta-llama/llama-4-maverick",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.