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Meta AI releases the Llama family as open-weights models — freely available for self-hosting and fine-tuning via 15+ API providers.
32
indexed models
34
total catalog
$0.000
cheapest model
1
free / open source
| Model | Input $/1M | Output $/1M | Context | |
|---|---|---|---|---|
llama-2-7b-chat-int8 Quantized (int8) generative text model with 7 billion parameters from Meta | — | — | 8k | |
llama-3.2-3b-instruct The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. | $0.051 | $0.335 | 80k | |
Meta: Muse Spark 1.3 Contributor Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information... | $0.100 | $0.200 |
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| 1.048576M |
Meta: Muse Spark 1.2 Contributor Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost. It’s meaningfully cheaper than Muse Spark... | $0.100 | $0.200 | 1.048576M |
llama-3-8b-instruct-awq Quantized (int4) generative text model with 8 billion parameters from Meta. | $0.123 | $0.266 | 8k |
llama-3.1-8b-instruct-awq Quantized (int4) generative text model with 8 billion parameters from Meta. | $0.123 | $0.266 | 8k |
llama-4-scout-17b-16e-instruct Meta's Llama 4 Scout is a 17 billion parameter model with 16 experts that is natively multimodal. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. | $0.270 | $0.850 | 131k |
llama-3-8b-instruct Generation over generation, Meta Llama 3 demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. | $0.282 | $0.827 | 8k |
llama-3.3-70b-instruct-fp8-fast Llama 3.3 70B quantized to fp8 precision, optimized to be faster. | $0.293 | $2.253 | 24k |
Meta: Muse Glimmer 30B Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon... | $0.300 | $1.200 | 131k |
m2m100-1.2b Multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation | $0.342 | $0.342 | — |
llama-guard-3-8b Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. | $0.484 | $0.030 | 131k |
llama-2-7b-chat-fp16 Full precision (fp16) generative text model with 7 billion parameters from Meta | $0.556 | $6.667 | 4k |
Meta: Muse Spark 1.2 Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks. It accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context window.... | $1.250 | $4.250 | 1.048576M |
Meta: Muse Spark Open Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers... | $1.250 | $4.250 | 1.048576M |
Meta: Muse Spark 1.3 Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through... | $1.250 | $4.250 | 1.048576M |
Meta: Muse Wonder Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with... | $4.000 | $20.000 | 1.048576M |
llama-3.2-11b-vision-instruct The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. | — | — | 128k |
llama-3.2-1b-instruct The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. | — | — | 60k |
Llama 3.2 3B Instruct Llama 3.2 3B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3 8B Instruct Llama 3 8B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3.1 70B Instruct Llama 3.1 70B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3.2 11B Instruct Llama 3.2 11B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3.2 90B Instruct Llama 3.2 90B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3.2 1B Instruct Llama 3.2 1B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3.3 70B Instruct Llama 3.3 70B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 4 Scout 17B Instruct Llama 4 Scout 17B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3 70B Instruct Llama 3 70B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 3.1 8B Instruct Llama 3.1 8B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
Llama 4 Maverick 17B Instruct Llama 4 Maverick 17B Instruct — available via AWS Bedrock (us-east-1). | — | — | — |
llama-4-maverick-instruct A 17 billion parameter model with 128 experts | — | — | — |
llama-4-scout-instruct A 17 billion parameter model with 16 experts | — | — | — |