Benchmark Almanac

Infinite Generations (Mistral internal)

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
June 2025
Kind
Overall chat quality
Level
Everyday
Scored
Checked automatically
Answers
Open-ended
Tasks
Kept private
Last in a launch post
June 2025, dropped before it saturated
Last model added
June 2025
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. 1 Mistral Small 3.2 1.29%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Mistral Small 3.2 1.29% Jun 2025 Lab 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
0% 20% 40% 60% 80% 100% 2025 Mistral Small 3.2 0% 20% 40% 60% 80% 100% 2025 Mistral Small 3.2

Source: Labs’ model cards

No launch post has listed it since June 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.

Mistral AI
DateLaunch postScoreAs the post writes it
Jun 20, 2025 Mistral Small 3.2 1.29 Infinite Generations (Internal; lower is better)

More in Instructions and chat

All 81 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. Gemma 4 31B IT 95.9%