Context Window

Compare

Gpt 35 Turbo 16k 0613 vs Mistral Tiny

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 35 Turbo 16k 0613

Tool calling

Context window

16K

16,385 tokens · ~12K words

Model page
Mistral

Model

Mistral Tiny

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 35 Turbo 16k 061316K
Mistral Tiny32K

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

Token calculator →Memory savings →

Mistral Tiny has 95% more context capacity (32K vs 16K tokens). Mistral Tiny is 91% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

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

  • RAG / high-volume retrieval

    Use Mistral Tiny. Input tokens are 91% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Mistral Tiny. 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 35 Turbo 16k 0613Mistral Tiny
Context window16,385 tokens (16K)32,000 tokens (32K)
Max output tokens4,096 tokens (4K)8,191 tokens (8K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
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.

ProviderGpt 35 Turbo 16k 0613 inGpt 35 Turbo 16k 0613 outMistral Tiny inMistral Tiny out
Azure$3.00/M$4.00/M——
Mistral——$0.250/M$0.250/M

Frequently asked questions

Mistral Tiny 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.

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