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DeepSeek V3.1 vs Mistral Devstral 2 123b

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Deepseek

Model

DeepSeek V3.1

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Mistral

Model

Mistral Devstral 2 123b

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

DeepSeek V3.1164K
Mistral Devstral 2 123b256K

Mistral Devstral 2 123b has about 1.6× the context window of the other in this pair.

Mistral Devstral 2 123b has 56% more context capacity (256K vs 163K tokens). DeepSeek V3.1 is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Devstral 2 123b. Its 256K context fits entire documents without chunking (vs 163K).

  • RAG / high-volume retrieval

    Use DeepSeek V3.1. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use DeepSeek V3.1. Its 163K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDeepSeek V3.1Mistral Devstral 2 123b
Context window163,840 tokens (163K)256,000 tokens (256K)
Max output tokens163,840 tokens (163K)8,192 tokens (8K)
Speed tierBalancedFast
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateAug 2025N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderDeepSeek V3.1 inDeepSeek V3.1 outMistral Devstral 2 123b inMistral Devstral 2 123b out
Aws Bedrock$0.400/M$2.00/M
Openrouter$0.200/M$0.800/M

Frequently asked questions

Mistral Devstral 2 123b has a larger context window: 256K tokens vs 163K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model