Llama 4 Maverick
Llama 4 Maverick is an API model from Meta AI. It’s positioned for vision + text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Below avg
Speed
Medium
116 tok/s output
Cost
Low
$0.35 in / $0.85 out
Context
1M
Up to 1,000,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Multimodal
Combines text, vision, and audio in one model.
Vision
Understands images, scenes, and visual context.
Context Window
1M tokens
≈ entire codebase
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M This model 本模型
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
9.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Maverick variant — 17B active / 128 experts; multimodal frontier-tier.
- Meta's first MoE-native Llama and first natively multimodal open-weights generation
- Maverick (17B active / 128 experts) beats GPT-4o and Gemini 2.0 Flash on most benchmarks at release
- Scout (17B active / 16 experts) fits in a single H100 with a claimed 10M-token context window
Best use cases
- Self-hosted multimodal applications where API models can't go
- Long-context retrieval and document QA (especially Scout's 10M window)
- Fine-tuning on private data while staying inside Meta's open license
Tools to try
Not ideal for
- Frontier-leaderboard reasoning — Claude 4, GPT-5, DeepSeek R1 score higher
- Edge / single-consumer-GPU deployments (even Scout needs an H100)
Model Evolution
Llama is Meta AI's language model family.