Benchmark Almanac

Consulting and Recommendations Benchmark (StepFun)

Only in system cards

Models answer 500 advice queries taken from Reddit, Stack Exchange and forums. Model judges score usefulness, logic, instruction following and tone against an expert reference set at 88.

In-house StepFun evaluation. The tasks are not public.

Made by
StepFun
Released
February 2026
Kind
Overall chat quality
Level
Everyday
Scored
Graded by a model
Answers
Open-ended
Tasks
Kept private
Last in a system card
February 2026
Last model added
February 2026
1
Model scored in lab cards
1
System card reports it, from 1 lab

Step 3.5 Flash leads with 70.5%

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 Step 3.5 Flash 70.5%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Step 3.5 Flash 70.5% Feb 2026 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% 2026 Near the ceiling (85%) Step 3.5 Flash 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Step 3.5 Flash

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
Feb 11, 2026 Step 3.5 Flash technical report 70.5 Consulting and Recommendations Benchmark, in-house, Average (Table 20). Usefulness 73.3, Logic 62.1, Tone 72.4, Instruction-following 74.2 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%