Context Window

Compare

GPT-4 Turbo 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.

Openai

Model

GPT-4 Turbo

Image inputTool calling

Context window

128K

128,000 tokens · ~96K 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.

GPT-4 Turbo128K
Mistral Large32K

GPT-4 Turbo has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-4 Turbo has 300% more context capacity (128K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-4 Turbo. Its 128K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Mistral Large. Its 8K max output lets you generate complete artifacts in one request.

Full specs

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

SpecGPT-4 TurboMistral Large
Context window128,000 tokens (128K)32,000 tokens (32K)
Max output tokens4,096 tokens (4K)8,191 tokens (8K)
Speed tierBalancedDeep
VisionYesNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APIYesNo
Release dateApr 2024Feb 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.

ProviderGPT-4 Turbo inGPT-4 Turbo 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————

Frequently asked questions

GPT-4 Turbo has a larger context window: 128K 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.

Powered by Mem0

Use a smaller model.
Get better results.

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