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Favicon for meta-llama

Meta: Llama 4 Maverick

meta-llama/llama-4-maverick

Model weights
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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 pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.

Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.

Modalities
In / Out Price
$0.1875 / $0.6525per 1M
Context
524K
Released
Apr 5, 2025
Knowledge Cutoff
Aug 2024
Compare
ProvidersPricingPerformanceUptimeAppsActivityFAQExplore

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Floor (cheapest).

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

Not currently available through EU in-region routing. In-region routing docs

Explore more models

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Latency / throughput
$0.3500$1.1500------
--
$0.1875$0.6525$0.050.58s19 tps
99.97%
$0.3500$1.0000$0.170.61s75 tps
100.00%
Throughput
75tok/s

P50, best across providers

Latency
0.58s

P50, best provider

AutoExacto benchmarks
GPQA DiamondTAU-BenchDigitalOcean66.7%37.3%
Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
99.92%

Availability over the last 3 days

Last 72 hours
Availability 99.92%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
99.90%
Without Routing
86.15%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

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Frequently asked questions

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 pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages.

Llama 4 Maverick costs $0.1875/M input tokens and $0.6525/M output tokens, with separate rates for Cache Read at $0.05/M tokens.

Llama 4 Maverick has a 524,288 token context window. It supports up to 16,384 completion tokens.

Yes. Llama 4 Maverick accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

Llama 4 Maverick accepts text and images as input and returns text.

Llama 4 Maverick is served by 3 providers on OpenRouter: DigitalOcean, Parasail, and Google Vertex. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.

Llama 4 Maverick was released on April 5, 2025. Its knowledge cutoff is August 31, 2024.