Context Window

Compare

Mistral Large 4 vs Qwen3 Coder Plus

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 Large 4

Image inputTool calling

Context window

524K

524,288 tokens · ~393K words

Model page
Alibaba

Model

Qwen3 Coder Plus

Tool calling

Context window

998K

997,952 tokens · ~748K 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 Large 4524K
Qwen3 Coder Plus998K

Qwen3 Coder Plus has about 1.9× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 Coder Plus has 90% more context capacity (997K vs 524K tokens). Qwen3 Coder Plus is 4% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 Coder Plus. Its 997K context fits entire documents without chunking (vs 524K).

  • RAG / high-volume retrieval

    Use Qwen3 Coder Plus. Input tokens are 4% 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 Large 4Qwen3 Coder Plus
Context window524,288 tokens (524K)997,952 tokens (997K)
Max output tokensN/A65,536 tokens (65K)
Speed tierDeepBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/ASep 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.

ProviderMistral Large 4 inMistral Large 4 outQwen3 Coder Plus inQwen3 Coder Plus out
Alibaba Cloud————
Mistral$0.680/M$2.09/M——
Openrouter——$0.650/M$3.25/M

Frequently asked questions

Qwen3 Coder Plus has a larger context window: 997K tokens vs 524K. 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.

Calculate your savingsLearn about Mem0

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