Context Window

Compare

Ft:gpt 3 5 vs Open Mixtral 8x7b

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

Ft:gpt 3 5

Context window

16K

16,385 tokens · ~12K words

Model page
Mistral

Model

Open Mixtral 8x7b

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.

Ft:gpt 3 516K
Open Mixtral 8x7b32K

Open Mixtral 8x7b has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Open Mixtral 8x7b has 95% more context capacity (32K vs 16K tokens). Open Mixtral 8x7b is 76% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Open Mixtral 8x7b. Its 32K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Open Mixtral 8x7b. Input tokens are 76% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Open Mixtral 8x7b. 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.

SpecFt:gpt 3 5Open Mixtral 8x7b
Context window16,385 tokens (16K)32,000 tokens (32K)
Max output tokens4,096 tokens (4K)8,191 tokens (8K)
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderFt:gpt 3 5 inFt:gpt 3 5 outOpen Mixtral 8x7b inOpen Mixtral 8x7b out
Mistral——$0.700/M$0.700/M
Openai$3.00/M$6.00/M——

Frequently asked questions

Open Mixtral 8x7b 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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