Context Window

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Ministral 3 14b 2512 vs Qwen3 235B A22B Thinking 2507

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

Ministral 3 14b 2512

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Alibaba

Model

Qwen3 235B A22B Thinking 2507

Tool calling

Context window

262K

262,144 tokens · ~197K 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.

Ministral 3 14b 2512262K
Qwen3 235B A22B Thinking 2507262K

Same context window size for both models.

Token calculator →Memory savings →

Ministral 3 14b 2512 and Qwen3 235B A22B Thinking 2507 have identical context windows (262K tokens). Qwen3 235B A22B Thinking 2507 is 45% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Qwen3 235B A22B Thinking 2507. Input tokens are 45% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMinistral 3 14b 2512Qwen3 235B A22B Thinking 2507
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokens262,144 tokens (262K)262,144 tokens (262K)
Speed tierFastDeep
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AJul 2025

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.

ProviderMinistral 3 14b 2512 inMinistral 3 14b 2512 outQwen3 235B A22B Thinking 2507 inQwen3 235B A22B Thinking 2507 out
Deepinfra——$0.300/M$2.90/M
Fireworks——$0.220/M$0.880/M
Mistral$0.200/M$0.200/M——
Novita——$0.300/M$3.00/M
Openrouter——$0.110/M$0.600/M
Together Ai——$0.650/M$3.00/M

Frequently asked questions

Qwen3 235B A22B Thinking 2507 has a larger context window: 262K tokens vs 262K. 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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Use a smaller model.
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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