Llama 4 Maverick

Llama 4 Maverick by Meta excels in the Coding category, where it ranks #171 with a composite score of 68/100. 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... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Llama 4 Maverick is priced at $0.20 per million input tokens and $0.70 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Llama 4 Maverick holds rank #171 out of 377 models tracked. Its quality rank is #171 and adoption rank is #171. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Llama 4 Maverick has been evaluated across 6 different signals. Its strongest areas include Capabilities (67/100), Benchmarks (70/100), Pricing (99/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 68/100 reflects a weighted combination of all tracked signals.

Llama 4 Maverick is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check Meta's website for current free tier availability and promotional offers.

Llama 4 Maverick supports a 1,049K token context window (1,048,576 tokens total). That translates to roughly 786,432 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Llama 4 Maverick supports image understanding (vision), function/tool calling, structured JSON output, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. Vision support means it can analyze images, screenshots, and diagrams alongside text. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Llama 4 Maverick is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Llama 4 Maverick was developed by Meta. It was released on April 5, 2025. You can access it through Meta's API or download the model weights directly. Check our provider page for all models from Meta and how they compare against each other.

Pick Llama 4 Maverick when you need a solid balance of cost and capability for everyday development tasks, content generation, and standard API integrations. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Llama 4 Maverick through Meta's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.