Context Window

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Codestral 2405 vs Deepseek R1 Distill Llama 8b

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.

Mistral

Model

Codestral 2405

Context window

32K

32,000 tokens · ~24K words

Model page
Meta

Model

Deepseek R1 Distill Llama 8b

Context window

131K

131,072 tokens · ~98K 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.

Codestral 240532K
Deepseek R1 Distill Llama 8b131K

Deepseek R1 Distill Llama 8b has about 4.1× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek R1 Distill Llama 8b has 309% more context capacity (131K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek R1 Distill Llama 8b. Its 131K context fits entire documents without chunking (vs 32K).

Full specs

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

SpecCodestral 2405Deepseek R1 Distill Llama 8b
Context window32,000 tokens (32K)131,072 tokens (131K)
Max output tokens8,191 tokens (8K)N/A
Speed tierBalancedFast
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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.

ProviderCodestral 2405 inCodestral 2405 outDeepseek R1 Distill Llama 8b inDeepseek R1 Distill Llama 8b out
Fireworks——$0.200/M$0.200/M
Google Vertex$0.200/M$0.600/M——
Mistral————
Nscale——$0.025/M$0.025/M

Frequently asked questions

Deepseek R1 Distill Llama 8b has a larger context window: 131K tokens vs 32K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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.

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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