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GroqvsMistral

Groq vs Mistral: Token Pricing, Speed & Intelligence

Full comparison of Groq and Mistral — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.

Groq

LPU-powered inference — the fastest tokens per second available

Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.

SpeedReal-time chatVoice AICost-efficiencyBatch processing
Open-weight hostHosts open weights

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.

CodingEuropean complianceOpen-sourceCost-efficiencyChat
Proprietary modelsHosts open weights

Key metrics

Groq
Mistral
Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths & weaknesses

Groq

750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference
Sub-100ms time-to-first-token for real-time applications
Very competitive pricing on open-weight models
OpenAI-compatible API
Free tier available
Limited model selection vs. Together AI or Fireworks
No vision model support on most models
No fine-tuning capability

Mistral

Several open-weight models available for self-hosting
Codestral purpose-built for code with 262K context
European data sovereignty — GDPR-native
Competitive pricing vs. GPT-4 class models
Mistral Small is one of the cheapest capable models at $0.10/1M
Intelligence scores trail OpenAI and Anthropic at frontier tier
Smaller ecosystem than OpenAI
No vision support on smaller models

Key differentiators

Groq

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

Mistral

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

Groq FAQs

How fast is Groq inference?

Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B. This is 4–5× faster than typical GPU-based providers, making it ideal for real-time applications.

How much does Groq cost?

Llama 3.3 70B costs $0.59/1M input and $0.79/1M output tokens. Llama 3.1 8B is just $0.05/$0.08 per 1M tokens — among the cheapest options for a capable open-weight model.

What is a Groq LPU?

A Language Processing Unit (LPU) is Groq's custom silicon designed specifically for sequential token generation. Unlike GPUs which are optimised for parallel matrix operations, LPUs excel at the autoregressive decoding step that dominates LLM inference latency.

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

Groq

Provider pageWebsitePricingDocs

Mistral

Provider pageWebsitePricingDocs

Groq — LPU-powered inference — the fastest tokens per second available

Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

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

Groq

  • ▸750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference
  • ▸Sub-100ms time-to-first-token for real-time applications
  • ▸Very competitive pricing on open-weight models

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

Groq is a inference api, founded in 2016. Mistral is a frontier lab, founded in 2023. Groq as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. Mistral as a frontier lab trains and serves proprietary models with capabilities not available elsewhere.

How to choose between them

Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — fastest gpu-class inference. 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.