Infinite Generations (Mistral internal)
Mistral Small 3.2 1.29% Dropped Models answer challenging long or repetitive prompts from Mistral's internal set. The score is the share of responses that never stop or loop, so lower is better.
Mistral reported it in the Mistral Small 3.2 card to show fewer runaway generations than Small 3.1. The prompt set and the detection rule are not published.
Made by Mistral AI Released Jun 2025 Overall chat quality Tasks kept private Last in a launch post Jun 2025 , dropped before it saturated
Last model added Jun 2025
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
Mistral Small 3.2 leads with 1.29%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Mistral Small 3.2 1.29%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Mistral Small 3.2 | 1.29% | Jun 2025 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Reported by the lab in its model card
No launch post has listed it since Jun 2025
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 20 Jun 2025 | Mistral Small 3.2 | 1.29 | Infinite Generations (Internal; lower is better) |
More in Instructions and chat
- IFEval In use Models answer about 500 prompts containing verifiable instructions such as word limits or required keywords. Code checks each instruction for strict and loose accuracy.
Gemma 4 31B IT 98.9% - IFBench In use 58 new out-of-domain verifiable output constraints. Scored by programmatic constraint checks (strict or loose).
Nemotron-Cascade 2 30B-A3B 82.9% - MultiChallenge In use Multi-turn conversations testing instruction retention, inference memory, versioned editing and self-coherence. Graded by LLM judge with rubrics. GPT-6.1 Sol 82%
- Multi-IF In use Models follow verifiable instructions over three-turn conversations in eight languages, extending IFEval. Scored by instruction accuracy at each turn. LFM3-1.7B 89.1%
- AdvancedIF In use Models respond to over 1,600 prompts with complex, multi-turn, and system-level instructions. A judge model checks each response against expert-written rubrics.
MAI-Thinking-1 85% - IFStruct In use Models generate JSON or YAML that must follow a requested schema and phrasing constraints, without constrained decoding. A response passes only if every structural constraint holds. LFM3-1.7B 93.7%