The trick: Narrowed Superlative
Mistral says Large 4 is the best open-weight model for coding.
On the one independent coding index it cites, its own post says it is effectively tied with GLM-5.3, at 48%.
Mistral says Large 4 ranks as the best open-weight model for coding.
Before you read on. Your call?
TRUE, BUT
48%
Mistral's own post rests the lead on Terminal-Bench and Cognition's private FrontierCode 2.0, and calls its 48% on the Artificial Analysis Coding Agent Index effectively tied with GLM-5.3.
The twist
On the preview's broader intelligence index, Tom's Hardware reported at least five Chinese open models ahead.
There’s more to this story.
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The trick has a name
We call it Narrowed Superlative: first or fastest, inside a quietly narrowed category. You'll see it again. Learn to spot it →
Receipts
- Supports mistral.ai:
Mistral Large 4 ranks as the best open-weight model for coding and among the world's most capable open models overall.
- Supports mistral.ai:
taking the lead on FrontierCode and Terminal-Bench while matching the best open-weight models on the Artificial Analysis Coding Agent Index
- Context mistral.ai:
On the Artificial Analysis Coding Agent Index, Mistral Large 4 scores 48%, effectively tied with the leading open-weight model, GLM-5.3, while using 47% fewer tokens per task on average.
- Context mistral.ai:
On Cognition's private FrontierCode 2.0 benchmark, which assesses end-to-end code quality and whether a repository maintainer would merge the agent's change, Mistral Large 4 leads with a score of 19.7%.
- Supports mistral.ai:
On Terminal-Bench 4.0, Mistral Large 4 improved by 13 percentage points to 41% compared to its public preview, taking the lead among open-weight models
- Context mistral.ai:
a full technical report is coming soon
- Refutes tomshardware.com:
At least five Chinese open models scored higher on the index
- Context tomshardware.com:
Its best result is a cyber test, but it falls behind on the index.
- Context datastudios.org:
Mistral reports 48% on the Artificial Analysis Coding Agent Index, which it calls effectively tied with Zhipu's GLM-5.3 while using 47% fewer tokens per task on average.
- Supports mistral.ai:
Just three weeks of additional reinforcement learning have since propelled it to the forefront of open-weight coding
Open the Receipts Pack → What each source proves, every figure traced, and what would change our verdict.
