Benchmark Almanac

Real-scenario prompts human evaluation (Z.ai)

Only in system cards

One human evaluator scores replies to 660 curated real user prompts in English, Chinese and other languages on a 0 to 10 scale.

In-house Z.ai evaluation. Scores are given per language with no combined score.

Made by Zhipu AI (Z.ai) Released Aug 2025 Overall chat quality Tasks kept private Last in a system card Aug 2025 Last model added Aug 2025
Level
Everyday Most adults, without training
Scored
Graded by people Human experts grade or compare the outputs.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
1
System card reports it, from 1 lab

No scores here yet

No lab, leaderboard or Epoch AI run has reported a score for Real-scenario prompts human evaluation (Z.ai) yet.

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 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
8 Aug 2025 GLM-4.5 technical report Human evaluation on real-scenario prompts, 0 to 10. Overall English 8.66, Chinese 8.37, other languages 8.49 (Tables 8 to 10), no combined score Capabilities

More in Instructions and chat

All 76 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%