Llama 4 Maverick
Llama 4 Maverick is a 17B active parameter mixture-of-experts text generation model from Meta with a 130,000 token context window.
Meta
DeepInfraMeta's mixture-of-experts instruct model
Llama 4 Maverick is a text generation model released by Meta in April 2025. It uses a mixture-of-experts (MoE) architecture with 17 billion active parameters across 128 experts, running in FP8 precision via the DeepInfra provider. The model is an instruction-tuned variant, meaning it has been fine-tuned to follow conversational and task-based prompts rather than serving as a raw base model.
Llama 4 Maverick is designed for chat and instruction-following tasks that benefit from a large context window of 130,000 tokens, allowing it to process lengthy documents or extended conversations in a single pass. Its MoE design activates only a subset of parameters per token, which enables a larger total parameter count without proportionally increasing inference compute. It is well suited for tasks such as summarization, question answering, coding assistance, and multi-turn dialogue.
What Llama 4 Maverick supports
Long Context Window
Processes up to 130,000 tokens in a single request, enabling analysis of long documents or extended multi-turn conversations without truncation.
Instruction Following
Fine-tuned on instruction and chat data to respond accurately to task-based prompts, including summarization, Q&A, and step-by-step instructions.
Mixture-of-Experts Architecture
Uses 128 experts with 17B active parameters per forward pass, allowing a large total model capacity while keeping per-token compute manageable.
Code Generation
Generates, explains, and debugs code across common programming languages as part of its general instruction-following capabilities.
Large Response Output
Supports a maximum response size of 60,000 tokens, making it suitable for generating long-form content such as reports or detailed technical explanations.
FP8 Quantized Inference
Runs in FP8 precision, reducing memory footprint and enabling faster inference while maintaining model quality for most generation tasks.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 80.9% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 67.1% |
| MATH-500 | Undergraduate and competition-level math problems | 88.9% |
| AIME 2024 | American math olympiad problems | 39.0% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 39.7% |
| HLE | Questions that challenge frontier models across many domains | 4.8% |
| SciCode | Scientific research coding and numerical methods | 33.1% |
Common questions about Llama 4 Maverick
What is the context window size for Llama 4 Maverick?
Llama 4 Maverick supports a context window of 130,000 tokens, meaning it can process up to that many tokens of combined input and conversation history in a single request.
What does the '17B-128E' in the model name mean?
It refers to the model's mixture-of-experts architecture: 17 billion active parameters are used per token, selected from a pool of 128 experts. The total parameter count is larger, but only 17B are active during any given forward pass.
Is Llama 4 Maverick a base model or an instruction-tuned model?
It is an instruction-tuned (instruct) model, fine-tuned to follow conversational prompts and task-based instructions rather than simply completing text like a base model would.
What is the maximum response length this model can generate?
The model supports a maximum response size of 60,000 tokens per output, which allows for long-form generation such as detailed reports or extended code files.
Who publishes Llama 4 Maverick and when was it released?
Llama 4 Maverick is published by Meta and was released in April 2025. On MindStudio it is served via the DeepInfra provider using the FP8 quantized version of the model.
What people think about Llama 4 Maverick
Community reception of Llama 4 Maverick has been mixed, with some users expressing disappointment in the model's real-world performance relative to expectations set by benchmark results. A thread with over 500 upvotes on r/LocalLLaMA specifically called out a gap between announced capabilities and observed outputs shortly after release.
Discussions across r/LocalLLaMA also include comparisons of Llama 4 against other open-weight models, with users evaluating it alongside competing releases in community-run grading exercises. Concerns around benchmark reliability and practical usability in local deployments appear frequently in these threads.
I'm incredibly disappointed with Llama-4
AI models that blackmailed when being tested in simulations
LLMs grading other LLMs 2
Alibaba's new Qwen3.5-397B-A17B is the #3 open weights model in the Artificial Analysis Intelligence Index
Parameters & options
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