Benchmark Almanac

LengthCtrl (Qwen)

Only in system cards

Models do creative writing tasks with length requirements. The score depends on the gap between output length and target length.

In-house Qwen evaluation. The data is not public.

Released
May 2025
Kind
Following precise instructions
Level
Everyday
Scored
Checked automatically
Answers
Open-ended
Tasks
Kept private
Strongest model tested
Qwen3-32B #134 on the capability index
Last in a system card
May 2025
Last model added
May 2025
1
Model scored in lab cards
1
System card reports it, from 1 lab

Qwen3-32B leads with 73.5%

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 Qwen3-32B 73.5%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Qwen3-32B 73.5% May 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 Near the ceiling (85%) Qwen3-32B 0% 20% 40% 60% 80% 100% 2025 Near the ceiling (85%) Qwen3-32B

Source: Labs’ model cards

Labs report it only in system cards

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
May 14, 2025 Qwen3-32B technical report 73.5 LengthCtrl* (in-house), Stage 4 General RL, thinking mode. Non-thinking 87.3 (Table 22) Capabilities

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%