Context Window

Compare

Grok 4.3 vs Mistral Large

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.

Xai

Model

Grok 4.3

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Mistral

Model

Mistral Large

Tool calling

Context window

32K

32,000 tokens · ~24K 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.

Grok 4.31M
Mistral Large32K

Grok 4.3 has about 31.3× the context window of the other in this pair.

Token calculator →Memory savings →

Grok 4.3 has 3025% more context capacity (1000K vs 32K tokens). Grok 4.3 is 58% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 4.3. Its 1000K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Grok 4.3. Input tokens are 58% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGrok 4.3Mistral Large
Context window1,000,000 tokens (1000K)32,000 tokens (32K)
Max output tokensN/A8,191 tokens (8K)
Speed tierBalancedDeep
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateApr 2026Feb 2024

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.

ProviderGrok 4.3 inGrok 4.3 outMistral Large inMistral Large out
Azure——$4.00/M$12.00/M
Ibm Watsonx——$3.00/M$10.00/M
Openrouter——$8.00/M$24.00/M
Snowflake————
Xai$1.25/M$2.50/M——

Frequently asked questions

Grok 4.3 has a larger context window: 1000K 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