Llama 4 Maverick
Featured Meta Llama 4 Generally Available Apr 2025
Meta's most capable open-weights model. 17B active / 400B total MoE with 1M context, strong MMLU scores that match GPT-4o, and native multimodal support.
Context
1M
tokens
Input
$0.15
per MTok
Output
$0.60
per MTok
About
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Modalities
Input
Text Vision Code
Output
Text Code
Code Examples
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-4-Maverick-17B-128E",
"messages": [
{ "role": "user", "content": "Explain quantum entanglement in one sentence." }
]
}' API Parameters
| Name | Type | Description |
|---|---|---|
frequency_penalty | number | Penalize tokens by their frequency so far. Positive values reduce repetition. |
logit_bias | object | Map of token-id to bias (-100…100) added to the logit before sampling. |
max_tokens deprecated | integer | Deprecated. Use max_completion_tokens. |
min_p | unknown | — |
presence_penalty | number | Penalize tokens that have appeared at all so far. Positive values encourage new topics. |
repetition_penalty | number | Penalize repeated tokens (>1.0 reduces repetition, <1.0 encourages it). |
response_format | one of | Constrain output to a JSON schema or an enum (structured outputs). |
seed | integer | Deterministic seed for sampling. Same seed + same prompt produces identical output. |
stop | array | Up to 4 sequences where the API will stop generating tokens. |
structured_outputs | boolean | Enable JSON-schema-constrained output. |
temperature | number | Sampling temperature; higher values produce more random output. 0 is deterministic. |
top_k | integer | Limit sampling to the top-k most likely tokens at each step. |
top_p | number | Nucleus sampling: consider only tokens whose cumulative probability ≥ top_p. |
Standard OpenAI-compatible parameters. Consult the provider docs for model-specific behaviour.
Benchmark Scores
| Benchmark | Score |
|---|---|
| MMLU | 85.5% |
| MMLU-Pro | 80.5% |
| HumanEval | 86% |
| MATH | 73.5% |
Strengths & Limitations
Best For
GPT-4o-level MMLU
1M token context
Open weights — self-hostable
Natively multimodal
Strong code generation
Limitations
Requires large GPU cluster for full deployment
Smaller HF community vs Scout at launch
Tags
Open WeightsMultimodalMoEReasoningVision