Context Window

Compare

Devstral Small 2507 vs Gpt 5 4 2026 03 05

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.

Mistral

Model

Devstral Small 2507

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Openai

Model

Gpt 5 4 2026 03 05

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K 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.

Devstral Small 2507128K
Gpt 5 4 2026 03 051.1M

Gpt 5 4 2026 03 05 has about 8.2× the context window of the other in this pair.

Token calculator →Memory savings →

Gpt 5 4 2026 03 05 has 720% more context capacity (1050K vs 128K tokens). Devstral Small 2507 is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gpt 5 4 2026 03 05. Its 1050K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Devstral Small 2507. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDevstral Small 2507Gpt 5 4 2026 03 05
Context window128,000 tokens (128K)1,050,000 tokens (1050K)
Max output tokens128,000 tokens (128K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
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.

ProviderDevstral Small 2507 inDevstral Small 2507 outGpt 5 4 2026 03 05 inGpt 5 4 2026 03 05 out
Azure——$2.50/M$15.00/M
Mistral$0.100/M$0.300/M——
Openai——$2.50/M$15.00/M

Frequently asked questions

Gpt 5 4 2026 03 05 has a larger context window: 1050K tokens vs 128K. 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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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.

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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