Context Window

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Codestral 2405 vs Qwen3 30b A3b Fp8

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
Alibaba

Model

Qwen3 30b A3b Fp8

Context window

41K

40,960 tokens · ~31K 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
Qwen3 30b A3b Fp841K

Qwen3 30b A3b Fp8 has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 30b A3b Fp8 has 28% more context capacity (40K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 30b A3b Fp8. Its 40K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Qwen3 30b A3b Fp8. Its 20K 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.

SpecCodestral 2405Qwen3 30b A3b Fp8
Context window32,000 tokens (32K)40,960 tokens (40K)
Max output tokens8,191 tokens (8K)20,000 tokens (20K)
Speed tierBalancedFast
VisionNoNo
Function callingNoNo
Extended thinkingNoYes
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 outQwen3 30b A3b Fp8 inQwen3 30b A3b Fp8 out
Google Vertex$0.200/M$0.600/M——
Mistral————
Novita——$0.090/M$0.450/M

Frequently asked questions

Qwen3 30b A3b Fp8 has a larger context window: 40K 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