Context Window

Compare

GPT-3.5 Turbo 16k vs Mistral Large 2402

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-3.5 Turbo 16k

Tool calling

Context window

16K

16,385 tokens · ~12K words

Model page
Mistral

Model

Mistral Large 2402

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-3.5 Turbo 16k16K
Mistral Large 240232K

Mistral Large 2402 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 2402 has 95% more context capacity (32K vs 16K tokens). GPT-3.5 Turbo 16k is 25% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 2402. Its 32K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use GPT-3.5 Turbo 16k. Input tokens are 25% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGPT-3.5 Turbo 16kMistral Large 2402
Context window16,385 tokens (16K)32,000 tokens (32K)
Max output tokens4,096 tokens (4K)N/A
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateAug 2023N/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.

ProviderGPT-3.5 Turbo 16k inGPT-3.5 Turbo 16k outMistral Large 2402 inMistral Large 2402 out
Azure——$8.00/M$24.00/M
Mistral——$4.00/M$12.00/M
Openai$3.00/M$4.00/M——
Openrouter$3.00/M$4.00/M——

Frequently asked questions

Mistral Large 2402 has a larger context window: 32K tokens vs 16K. 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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Example: a multi-turn chat session

Without Mem0~128K tokens sent
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