BlogLLM Benchmark Scores as Published, Side by Side
Z.ai Code Bench: Results as Published
Every result the catalog holds for Z.ai Code Bench: 4 models from 2 developers, read from 1 page. Each number is as printed by the company named beside it; nothing is ranked or rescored. Models are listed by developer, newest first.
Area Agentic CodingData version 2026103001, as of 2026-10-30
Z.ai Code Bench
| Model | Value | Printed by | Setup as printed |
|---|---|---|---|
| Claude Fable 5Anthropic, released 2026-06-09 | 39.5% | Z.ai, GLM-5.3 postchecked 2026-10-15 | Effort: Max |
| Claude Opus 4.8Anthropic, released 2026-05-28 | 29.5% | Z.ai, GLM-5.3 postchecked 2026-10-15 | Effort: High |
| GLM-5.3Z.ai, released 2026-08-18 | 31.4% | Z.ai, GLM-5.3 postchecked 2026-10-15 | Effort: High |
| 34.5% | Z.ai, GLM-5.3 postchecked 2026-10-15 | Effort: Max | |
| GLM-5.2Z.ai, released 2026-06-16 | 23.4% | Z.ai, GLM-5.3 postchecked 2026-10-15 | Effort: Max |
Every number here is read from the same catalog file as the benchmark table, data version 2026103001, and is the number the table shows for that model and test. Where one page printed a model under several setups, each value is listed. The catalog is public in armanobosyan/sugra-llm-benchmarks; a wrong number can be reported from the table, with Report an error under the value, or as an issue in that repository.