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DeepSeek vs Mistral: Token Pricing, Speed & Intelligence

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

DeepSeek

Chinese frontier lab — DeepSeek V3 and R1 at remarkably low prices

DeepSeek is a Chinese AI lab that has released highly capable open-weight models at prices far below Western competitors. DeepSeek V3 matches GPT-4 class performance at $0.27/1M input tokens, while DeepSeek R1 is a reasoning model competitive with o1 at a fraction of the cost. Both models are open-weight.

Cost-efficiencyReasoningCodingOpen-sourceSelf-hosting
Proprietary 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.

CodingEuropean complianceOpen-sourceCost-efficiencyChat
Proprietary modelsHosts open weights

Key metrics

Cheapest input ($/1M)

Cheapest output ($/1M)

Peak throughput

Best latency (TTFT)

Intelligence score

Context window

Live token pricing

Strengths & weaknesses

DeepSeek

DeepSeek V3 matches GPT-4 class at $0.27/1M input — 10× cheaper
R1 reasoning model competitive with o1 at a fraction of the cost
Both V3 and R1 are open-weight — can be self-hosted
Mixture-of-Experts architecture for efficient inference
Strong coding and math benchmarks
Data residency in China — may not meet compliance requirements
API reliability can lag Western providers during peak demand
Limited multimodal capability vs. Gemini or GPT-4o

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

DeepSeek

DeepSeek V3 delivers GPT-4 class intelligence at $0.27/1M input tokens — the most disruptive price-to-performance ratio in the LLM market.

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

DeepSeek FAQs

How much does DeepSeek cost?

DeepSeek V3 costs $0.27/1M input and $1.10/1M output tokens — roughly 10× cheaper than GPT-4o for comparable capability. DeepSeek R1 is $0.55/1M input and $2.19/1M output.

Is DeepSeek open-weight?

Yes. Both DeepSeek V3 and DeepSeek R1 are open-weight models available on Hugging Face. You can self-host them on your own GPU infrastructure, though they require significant compute (671B parameters for R1).

How does DeepSeek R1 compare to OpenAI o1?

DeepSeek R1 scores comparably to OpenAI o1 on math and coding benchmarks at a fraction of the cost. R1 is open-weight and can be self-hosted, while o1 is proprietary. R1 is available via multiple inference providers including Fireworks AI and Together AI.

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

DeepSeekChinese frontier lab — DeepSeek V3 and R1 at remarkably low prices

DeepSeek is a Chinese AI lab that has released highly capable open-weight models at prices far below Western competitors. DeepSeek V3 matches GPT-4 class performance at $0.27/1M input tokens, while DeepSeek R1 is a reasoning model competitive with o1 at a fraction of the cost. Both models are open-weight.

DeepSeek V3 delivers GPT-4 class intelligence at $0.27/1M input tokens — the most disruptive price-to-performance ratio in the LLM market.

MistralEuropean 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

DeepSeek

  • DeepSeek V3 matches GPT-4 class at $0.27/1M input — 10× cheaper
  • R1 reasoning model competitive with o1 at a fraction of the cost
  • Both V3 and R1 are open-weight — can be self-hosted

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

DeepSeek is a frontier lab, founded in 2023. 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 DeepSeek and Mistral are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: DeepSeek leads on deepseek v3 matches gpt-4 class at $0.27/1m input — 10× cheaper, 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.