Meta · Open weights
Llama 4 Maverick
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.
| Llama 4 MaverickMetaThis model | Muse Glimmer 30BMetaRemove | |
|---|---|---|
| Provider | Meta | Meta |
| Discovery tier | Open weights | Open weights |
| Added | Sep 5, 2025 | Aug 11, 2026 |
| Limits | ||
| Context window | 1.05MBest | 131.07K |
| Max output | 65.54K | — |
| Average output | 114 tokens | 682 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.19Best | $0.35 |
| Output | $0.65Best | $1.50 |
| Cache read | $0.05 | $0.04Best |
| Performance | ||
| Throughput | — | 70.8 tok/s |
| Time to first token | — | 1.1 s |
| Request success (24h) | 99.6% | 100.0%Best |
| Benchmark indexes | ||
| Intelligence Index | 9.3 | 17.5Best |
| Coding Index | 16.3 | 49.0Best |
| Math Index | 19.3 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | — | minimal, low, medium, high, xhigh |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Llama 4 Maverick
- Muse Glimmer 30B
Intelligence Index
- Llama 4 Maverick9.3
- Muse Glimmer 30B17.5
Llama 4 Maverick: Better than 18% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Llama 4 Maverick: 16.3
- Muse Glimmer 30B: 49.0Top
Math Index
Composite of the math evaluations
- Llama 4 Maverick: 19.3
- Muse Glimmer 30B: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Llama 4 Maverick: 0.2%
- Muse Glimmer 30B: 6.8%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Llama 4 Maverick: 0.0%
- Muse Glimmer 30B: 0.8%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Llama 4 Maverick: —
- Muse Glimmer 30B: 477 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Llama 4 Maverick: —
- Muse Glimmer 30B: 783 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Llama 4 Maverick: 0.8%
- Muse Glimmer 30B: 10.0%Top
AA-LCRv1.1
Reasoning across long documents
- Llama 4 Maverick: 50.0%
- Muse Glimmer 30B: 83.3%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Llama 4 Maverick: 4.9%
- Muse Glimmer 30B: 22.0%Top
GPQA Diamond
Graduate-level science questions
- Llama 4 Maverick: 67.1%
- Muse Glimmer 30B: 83.5%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Llama 4 Maverick: 80.9%
- Muse Glimmer 30B: —
IFBench
Following precise output instructions
- Llama 4 Maverick: 43.0%
- Muse Glimmer 30B: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Llama 4 Maverick: 0.0%
- Muse Glimmer 30B: 0.5%Top
SciCode
Scientific computing in Python
- Llama 4 Maverick: 31.7%
- Muse Glimmer 30B: 44.9%Top
LiveCodeBench
Recent competitive programming problems
- Llama 4 Maverick: 39.7%
- Muse Glimmer 30B: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Llama 4 Maverick: 19.3%
- Muse Glimmer 30B: —
AIME
Competition mathematics
- Llama 4 Maverick: 39.0%
- Muse Glimmer 30B: —
MATH-500
Competition math problems
- Llama 4 Maverick: 88.9%
- Muse Glimmer 30B: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Llama 4 Maverick: 6.8%
- Muse Glimmer 30B: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Llama 4 Maverick: 17.8%
- Muse Glimmer 30B: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Llama 4 Maverick: 50.0%
- Muse Glimmer 30B: 83.3%Top
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.
- 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.
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
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