Context Window

Compare

Databricks Qwen35 122b A10b vs Mistral Large 4

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.

Alibaba

Model

Databricks Qwen35 122b A10b

Tool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Mistral

Model

Mistral Large 4

Image inputTool calling

Context window

524K

524,288 tokens · ~393K 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.

Databricks Qwen35 122b A10b262K
Mistral Large 4524K

Mistral Large 4 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 4 has 100% more context capacity (524K vs 262K tokens). Databricks Qwen35 122b A10b is 67% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 4. Its 524K context fits entire documents without chunking (vs 262K).

  • RAG / high-volume retrieval

    Use Databricks Qwen35 122b A10b. Input tokens are 67% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDatabricks Qwen35 122b A10bMistral Large 4
Context window262,144 tokens (262K)524,288 tokens (524K)
Max output tokens25,000 tokens (25K)N/A
Speed tierBalancedDeep
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
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.

ProviderDatabricks Qwen35 122b A10b inDatabricks Qwen35 122b A10b outMistral Large 4 inMistral Large 4 out
Databricks$0.220/M$2.20/M——
Mistral——$0.680/M$2.09/M

Frequently asked questions

Mistral Large 4 has a larger context window: 524K 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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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
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Current turn

80% less to send — works with any model