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 Spark OpenMetaRemove | |
|---|---|---|
| Provider | Meta | Meta |
| Discovery tier | Open weights | Frontier |
| Added | Sep 5, 2025 | Oct 15, 2026 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 65.54K | — |
| Average output | 114 tokens | 1.3K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.19Best | $1.25 |
| Output | $0.65Best | $4.25 |
| Cache read | $0.05Best | $0.15 |
| Performance | ||
| Request success (24h) | 99.6% | — |
| Benchmark indexes | ||
| Intelligence Index | 9.3 | 38.9Best |
| Coding Index | 16.3 | — |
| Math Index | 19.3 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | — | 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 Spark Open
Intelligence Index
- Llama 4 Maverick9.3
- Muse Spark Open38.9
Llama 4 Maverick: Better than 18% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Llama 4 Maverick: 16.3
- Muse Spark Open: —
Math Index
Composite of the math evaluations
- Llama 4 Maverick: 19.3
- Muse Spark Open: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Llama 4 Maverick: 0.2%
- Muse Spark Open: 49.7%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Llama 4 Maverick: 0.0%
- Muse Spark Open: 20.9%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Llama 4 Maverick: —
- Muse Spark Open: 1,279 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Llama 4 Maverick: —
- Muse Spark Open: 1,401 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Llama 4 Maverick: 0.8%
- Muse Spark Open: 18.6%Top
AA-LCRv1.1
Reasoning across long documents
- Llama 4 Maverick: 50.0%
- Muse Spark Open: 78.3%Top
MLCR-AA
Long-context reasoning over medical records
- Llama 4 Maverick: —
- Muse Spark Open: 7.8%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Llama 4 Maverick: 4.9%
- Muse Spark Open: 43.9%Top
GPQA Diamond
Graduate-level science questions
- Llama 4 Maverick: 67.1%
- Muse Spark Open: 89.0%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Llama 4 Maverick: 80.9%
- Muse Spark Open: —
IFBench
Following precise output instructions
- Llama 4 Maverick: 43.0%
- Muse Spark Open: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Llama 4 Maverick: 0.0%
- Muse Spark Open: 8.1%Top
SciCode
Scientific computing in Python
- Llama 4 Maverick: 31.7%
- Muse Spark Open: 58.4%Top
LiveCodeBench
Recent competitive programming problems
- Llama 4 Maverick: 39.7%
- Muse Spark Open: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Llama 4 Maverick: 19.3%
- Muse Spark Open: —
AIME
Competition mathematics
- Llama 4 Maverick: 39.0%
- Muse Spark Open: —
MATH-500
Competition math problems
- Llama 4 Maverick: 88.9%
- Muse Spark Open: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Llama 4 Maverick: 6.8%
- Muse Spark Open: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Llama 4 Maverick: 17.8%
- Muse Spark Open: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Llama 4 Maverick: 50.0%
- Muse Spark Open: 78.3%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Llama 4 Maverick
- Muse Spark Open
| Leaderboard | Llama 4 Maverick | Muse Spark Open |
|---|---|---|
| Website | Not in top 20 | #15 · 1306 |
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
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
Related models
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