Context Window

Compare

Mistral 7b Instruct V0 1 vs Qwen3 235b A22b Fp8 Tput

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

Mistral 7b Instruct V0 1

Tool calling

Context window

16K

16,384 tokens · ~12K words

Model page
Alibaba

Model

Qwen3 235b A22b Fp8 Tput

Context window

40K

40,000 tokens · ~30K 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.

Mistral 7b Instruct V0 116K
Qwen3 235b A22b Fp8 Tput40K

Qwen3 235b A22b Fp8 Tput has about 2.4× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 235b A22b Fp8 Tput has 144% more context capacity (40K vs 16K tokens). Mistral 7b Instruct V0 1 is 25% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 235b A22b Fp8 Tput. Its 40K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Mistral 7b Instruct V0 1. Input tokens are 25% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMistral 7b Instruct V0 1Qwen3 235b A22b Fp8 Tput
Context window16,384 tokens (16K)40,000 tokens (40K)
Max output tokens16,384 tokens (16K)N/A
Speed tierFastBalanced
VisionNoNo
Function callingYesNo
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.

ProviderMistral 7b Instruct V0 1 inMistral 7b Instruct V0 1 outQwen3 235b A22b Fp8 Tput inQwen3 235b A22b Fp8 Tput out
Anyscale$0.150/M$0.150/M——
Cloudflare$1.92/M$1.92/M——
Together Ai——$0.200/M$0.600/M

Frequently asked questions

Qwen3 235b A22b Fp8 Tput has a larger context window: 40K tokens vs 16K. 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