Benchmark Almanac

LLMBoxing

Only in system cards

People see one question with two anonymous model answers and pick the better one. The site counts preferences between model pairs.

Independent human preference site cited in the Mistral 7B paper. The domain now serves an unrelated gambling page, so only archived copies of the leaderboard remain.

Made by llmboxing.com Released Oct 2023 Overall chat quality Last in a system card Oct 2023 Last model added Oct 2023
Level
Everyday Most adults, without training
Scored
Ranked by votes People pick the better of two answers, as in an arena, and the votes are turned into ratings.
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 LLMBoxing 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
10 Oct 2023 Mistral 7B v0.1 technical report llmboxing.com human evaluation: Mistral 7B preferred 5020 times vs 4143 for Llama 2 13B, as of Oct 6, 2023 (Section 4) 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%