Google vs Mistral: Token Pricing, Speed & Intelligence
Full comparison of Google and Mistral — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Gemini 2.5 — the largest context window at the lowest frontier price
Google DeepMind's Gemini family offers some of the most competitive frontier pricing, with Gemini 2.5 Pro delivering top-tier intelligence at $1.25/1M input tokens. The 1M+ token context window is the largest available. Gemini 2.5 Flash is a standout efficient model for vision and multimodal tasks.
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
Mistral
Key differentiators
Frequently asked questions
Google FAQs
How much does the Google Gemini API cost?
Gemini 2.5 Pro costs $1.25/1M input tokens (up to 200K context) and $10/1M output. Gemini 2.5 Flash is $0.15/$0.60 per 1M tokens. Gemini 2.0 Flash is even cheaper at $0.10/$0.40 per 1M tokens.
What is the context window for Gemini models?
Gemini 2.5 Pro and Flash both support a 1,048,576-token (1M+) context window — the largest available from any major LLM provider. This makes them ideal for processing entire codebases, books, or long document collections.
Does Gemini support vision and multimodal inputs?
Yes. All Gemini 2.x models natively support images, audio, and video inputs alongside text. Gemini 2.5 Flash is particularly strong for vision tasks at a low cost.
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
Google — Gemini 2.5 — the largest context window at the lowest frontier price
Google DeepMind's Gemini family offers some of the most competitive frontier pricing, with Gemini 2.5 Pro delivering top-tier intelligence at $1.25/1M input tokens. The 1M+ token context window is the largest available. Gemini 2.5 Flash is a standout efficient model for vision and multimodal tasks.
Gemini 2.5 Pro delivers frontier-tier intelligence at $1.25/1M input tokens — the best price-to-performance ratio among all frontier models.
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
- ▸1M+ token context window — largest available
- ▸Best price-per-intelligence at frontier tier ($1.25/1M input)
- ▸Native multimodal: text, image, audio, video
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
Google is a frontier lab, founded in 1998. Mistral is a frontier lab, founded in 2023. Both are frontier lab providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Google and Mistral are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: Google leads on 1m+ token context window — largest available, while Mistral leads on several open-weight models available for self-hosting. For cost-sensitive workloads, compare the cheapest model tier from each provider in the pricing table above — the gap between efficient-tier models is often larger than between flagship models.