‹ PublicAI Index
The LLM benchmark aggregator.
Open Codestral Mamba
Mistral
Strongest in Fairness (#21 of 299, on 1 of its 2 boards), weakest in Harm refusal (#279 of 299). Above par in 2 of 6 scopes. Among the models it meets almost everywhere, it finishes behind Claude Sonnet 5 and Claude Opus 5.5 and ahead of Grok 4.3 and NexusRaven V2 13B.
§ 1 · Profile
What it is good at
Bars run from 50 — the average of the models each source lists — so right of the line is above par. The middle column is the gap to whoever leads that scope.
Safety47.2−14.3#250/3361/3
Fairness65.6−4.4#21/2991/2
Jailbreak resistance52−12.9#160/2721/1
Toxicity avoidance48.9−10.1#212/2721/1
Secure code32.4−34.1#243/2741/1
Harm refusal41.4−20.2#279/2991/2
§ 2 · Head to head
What it beats, and what beats it
The same models turn up scope after scope. Counted once: where both were placed, who finished higher. Bars run right for Open Codestral Mamba, left for the other.
§ 3 · Sources
Where the numbers come from
1 publication, 6 figures. Every one links to the page it was read from.
Enkrypt · Jailbreak risk 11.1%Enkrypt · Harmful content risk 68.3%Enkrypt · CBRN risk 18.7%Enkrypt · Toxicity risk 7.1%Enkrypt · Bias risk 53.5%Enkrypt · Insecure code risk 69.3%
Badge
[](https://publicai.io/model-index/m/open-codestral-mamba)