Instruction Following Benchmark

WritingBench

WritingBench - benchmark evaluating fine-grained writing capabilities with verifiable constraints.

Category

Instruction Following

Score type

percent

Scale

100%

higher is better

Models tested

2

What is WritingBench?

WritingBench evaluates open-ended writing tasks: speeches, marketing copy, academic drafts, creative text. Rubrics measure whether the piece actually fulfils its brief — a step beyond 'sounds good' towards measurable writing quality.

What does it test?

WritingBench measures how precisely a model obeys the instructions it was given. In detail, it checks:

  • fulfilling the specific writing brief (genre, audience, goal)
  • verifiable constraints inside the text
  • quality judged by rubric criteria
  • stylistic breadth across very different text formats

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Frequently asked questions

What is the WritingBench benchmark?

WritingBench evaluates open-ended writing tasks: speeches, marketing copy, academic drafts, creative text. Rubrics measure whether the piece actually fulfils its brief — a step beyond 'sounds good' towards measurable writing quality.

What does WritingBench test?

WritingBench mainly measures how precisely a model obeys the instructions it was given. Concretely, it checks: fulfilling the specific writing brief (genre, audience, goal); verifiable constraints inside the text; quality judged by rubric criteria.

How is the WritingBench score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get WritingBench data and scores?

The official source is https://arxiv.org/abs/2503.05244. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/writingbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 2 models tested

Reset
# Model Score Date Source Verified
1 Qwen3-Next-80B-A3B-Thinking
Qwen
84.6%
10.09.2025 Report Verified
2 Qwen3-VL-30B-A3B
Qwen
82.6%
04.10.2025 Report Verified

What now?

You've seen how models score on WritingBench — here's what to do next.