Context Window

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Devstral Medium 2507 vs Granite Ttm 1024 96 R2

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 Medium 2507

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Ibm

Model

Granite Ttm 1024 96 R2

Context window

1K

512 tokens · ~384 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 Medium 2507128K
Granite Ttm 1024 96 R21K

Devstral Medium 2507 has about 250× the context window of the other in this pair.

Token calculator →Memory savings →

Devstral Medium 2507 has 24900% more context capacity (128K vs 0K tokens). Granite Ttm 1024 96 R2 is 5% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Devstral Medium 2507. Its 128K context fits entire documents without chunking (vs 0K).

  • RAG / high-volume retrieval

    Use Granite Ttm 1024 96 R2. Input tokens are 5% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Devstral Medium 2507. Its 128K 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.

SpecDevstral Medium 2507Granite Ttm 1024 96 R2
Context window128,000 tokens (128K)512 tokens (0K)
Max output tokens128,000 tokens (128K)512 tokens (0K)
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.

ProviderDevstral Medium 2507 inDevstral Medium 2507 outGranite Ttm 1024 96 R2 inGranite Ttm 1024 96 R2 out
Ibm Watsonx——$0.380/M$0.380/M
Mistral$0.400/M$2.00/M——

Frequently asked questions

Devstral Medium 2507 has a larger context window: 128K tokens vs 0K. 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.
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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