Meta vs Mistral: Token Pricing, Speed & Intelligence
Full comparison of Meta and Mistral — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Meta
Llama 4 & Muse Spark — the world's most widely deployed open-weight models
Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.
Mistral
European frontier AI — Mistral Large, Codestral, and open models
Mistral AI is a Paris-based lab that trains both proprietary and open-weight models. Mistral Large competes with GPT-4 class models at lower prices, while Codestral is purpose-built for code generation with a 262K context window. Several Mistral models are open-weight and available for self-hosting.
Key metrics
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Live token pricing
Strengths & weaknesses
Meta
Mistral
Key differentiators
The only frontier-class model family available as open weights — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume workloads.
The only frontier lab offering open-weight models alongside proprietary ones — giving teams the flexibility to self-host or use the API.
Frequently asked questions
Meta FAQs
What is the Llama 4 context window?
Llama 4 Scout and Maverick support 1,000,000-token (1M) context windows. Llama 4 Behemoth also targets 1M context. This makes Llama 4 competitive with Gemini 1.5 Pro for long-document and multi-document tasks.
How much does the Meta Llama API cost?
Llama 3.2 1B is $0.02/1M tokens in/out. Llama 3.2 3B is $0.03/$0.05. Llama 3.1 8B is $0.02/$0.05. Llama 3.2 90B Vision is $1.20/$1.20. Muse Spark 1.1 is $1.25/$4.25. Llama 4 Behemoth pricing is not yet publicly listed.
Can I self-host Llama models?
Yes — all Llama 3.x and Llama 4 Scout/Maverick weights are publicly available under the Llama Community License. You can run them on any GPU cloud provider. A single H100 at ~$2.50/hr can serve Llama 3.1 8B at very high throughput, making self-hosting cost-effective above ~10M tokens/day.
Mistral FAQs
How much does the Mistral API cost?
Mistral Large costs $2.00/1M input and $6.00/1M output tokens. Mistral Small is $0.10/$0.30 per 1M tokens — one of the cheapest capable models available. Codestral for code generation is priced separately.
Are Mistral models open-weight?
Some are. Mistral 7B, Mixtral 8x7B, and Mixtral 8x22B are open-weight and available on Hugging Face for self-hosting. Mistral Large and Codestral are proprietary and only available via the API.
What is Codestral?
Codestral is Mistral's code-specialised model with a 262K context window. It supports 80+ programming languages and is optimised for code completion, generation, and explanation tasks.
Provider resources
Meta — Llama 4 & Muse Spark — the world's most widely deployed open-weight models
Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.
The only frontier-class model family available as open weights — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume workloads.
Mistral — European frontier AI — Mistral Large, Codestral, and open models
Mistral AI is a Paris-based lab that trains both proprietary and open-weight models. Mistral Large competes with GPT-4 class models at lower prices, while Codestral is purpose-built for code generation with a 262K context window. Several Mistral models are open-weight and available for self-hosting.
The only frontier lab offering open-weight models alongside proprietary ones — giving teams the flexibility to self-host or use the API.
Key strengths compared
Meta
- ▸Open-weight models — self-host on any GPU cloud for lowest per-token cost at scale
- ▸Llama 4 Behemoth: 2T parameter frontier model with 1M context window
- ▸Widest third-party hosting ecosystem — available on AWS, Azure, GCP, Together AI, Groq, and 20+ others
Mistral
- ▸Several open-weight models available for self-hosting
- ▸Codestral purpose-built for code with 262K context
- ▸European data sovereignty — GDPR-native
Provider category context
Meta is a open source host, founded in 2023. Mistral is a frontier lab, founded in 2023. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.
How to choose between them
Choose Meta if you need open-weight models — self-host on any gpu cloud for lowest per-token cost at scale. Choose Mistral if you need several open-weight models available for self-hosting. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.