GLM 5.1
Released 7 Apr 2026$1.4 input, $4.4 output per million tokens200K contextopenAlso written as zai/glm-5.1
7 results on 4 of 13 boards, latest measured Sep 2026. Compare with other models
Clinical Benchmarks Index36.2Rank 38 of 1493 of 10 boardsAverage × multiplier 51.2 × 0.707 = 36.2This site’s own calculation. Working How it is computed
Results
Each line sits on its own board's scale. Tall tick: the board leader.
Documentation and coding
- MedScribe (Vals AI)model ID zai/glm-5.1; temperature=1; top_p=0.95; max_output_tokens=3000072.27Rank 85 of 105Leader Claude Opus 5.5 91.43
- MedCode (Vals AI)model ID zai/glm-5.1; temperature=1; top_p=0.95; max_output_tokens=3000041.60Rank 49 of 103Leader Claude Opus 5 63.57
EHR and workflow agents
- 18.7Rank 14 of 43 here, 45 models on the boardLeader erius + claude-opus-5 54.7
- 18.7Rank 14 of 43 here, 45 models on the boardLeader erius + claude-opus-5 54.7
- 16.9Rank 18 of 43 here, 45 models on the boardLeader erius + claude-opus-5 54.7
- 11.1Rank 29 of 43 here, 45 models on the boardLeader erius + claude-opus-5 54.7
Safety
- First, Do NOHARM (v2)First Do NOHARM v2 overall; preview; open-weight model; run date unpublished57.9Rank 16 of 17 here, 19 models on the boardLeader LiSA 2.5 86.2
Index working
The Clinical Benchmarks Index is this site's own calculation: a weighted average of the board scores times a multiplier for the boards behind it. How it is computed
- MedScribe (Vals AI)72.2772.3×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID zai/glm-5.1; temperature=1; top_p=0.95; max_output_tokens=30000
- MedCode (Vals AI)41.6041.6×0.911model ID zai/glm-5.1; temperature=1; top_p=0.95; max_output_tokens=30000
- First, Do NOHARM (v2)57.957.9×0.507First Do NOHARM v2 overall; preview; open-weight model; run date unpublished
- Weighted average of 3 boards
- 51.2
- Multiplier for 3 of 10 boards
- × 0.707
- Index
- 51.2 × 0.707 = 36.2
Score: the result on the board’s own 0 to 100 scale. Dot: this model; tick: the board’s leader. Weight: how much the board counts in the weighted average; bold where the board is near its ceiling and counts for less. Each result links to its quote and page; the line under it is the setting it was measured with.
Sources
The quote, page and document for each of the 7 results
85MedScribe (Vals AI) glm-5.1 72.27
Printed as 72.27%Official leaderboard, measured Sep 2026Configuration: model ID zai/glm-5.1; temperature=1; top_p=0.95; max_output_tokens=30000Vals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 85 of 105 (GLM 5.1), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["zai/glm-5.1"].85 | GLM 5.1 | 72.27%±2.06 | $1/$3.2 | 95.70s
Every result from this document49MedCode (Vals AI) glm-5.1 41.60
Printed as 41.60%Official leaderboard, measured Sep 2026Configuration: model ID zai/glm-5.1; temperature=1; top_p=0.95; max_output_tokens=30000Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 49 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["zai/glm-5.1"].49 | GLM 5.1 | 41.60%±2.12 | $1/$3.2 | 77.58s
Every result from this document16First, Do NOHARM (v2) First Do NOHARM v2 overall 57.9
Printed as 57.9%Official leaderboard, measured Sep 2026Configuration: First Do NOHARM v2 overall; preview; open-weight model; run date unpublishedARISE MAST technical leaderboard official leaderboard, ARISE. First Do NOHARM v2, overall leaderboard; GLM 5.1 row; displayed 2026-09-30.First Do NOHARM v2 overall metric across 19 models 15GLM 5.1OSSZ.ai 57.9%
Every result from this document14CHI-Bench All Domains pass@1 18.7
Printed as 18.7%Official leaderboard, measured May 2026Configuration: All Domains pass@1; hermes harnessCHI-Bench leaderboard (actAVA) official leaderboard, actAVA, 12 Aug 2026. CHI-Bench v1.0.0, All Domains, rank 15, Agent/Model and Accuracy columns; PA/UM/CM follow; board last updated 2026-08-12; board Date column 2026-05-01; accessed 2026-09-30. Linked submission provenance: run 2026-05-03T07:01:48Z to 2026-05-04T05:47:30Z; submitted_at 2026-05-04T05:47:30Z.15 | hermes | glm-5.1 | Open-source | 18.7% | 10.7% | 34.7% | 10.7% | 2026-05-01
Every result from this document- Also printed in CHI-Bench submission manifest: hermes + openrouter/z-ai/glm-5.1, submission.model; provenance.started_at / finished_at; results.overall.pass_at_1; retrieved 2026-09-30
14CHI-Bench All Domains pass@1 18.7
Printed as 18.7%Official leaderboard, measured May 2026Configuration: All Domains pass@1; openai-agents harnessCHI-Bench leaderboard (actAVA) official leaderboard, actAVA, 12 Aug 2026. CHI-Bench v1.0.0, All Domains, rank 14, Agent/Model and Accuracy columns; PA/UM/CM follow; board last updated 2026-08-12; board Date column 2026-05-01; accessed 2026-09-30. Linked submission provenance: run 2026-05-03T08:41:54Z to 2026-05-04T07:03:18Z; submitted_at 2026-05-04T07:03:18Z.14 | openai-agents | glm-5.1 | Open-source | 18.7% | 18.7% | 33.3% | 4.0% | 2026-05-01
Every result from this document- Also printed in CHI-Bench submission manifest: openai-agents + z-ai/glm-5.1, submission.model; provenance.started_at / finished_at; results.overall.pass_at_1; retrieved 2026-09-30
18CHI-Bench All Domains pass@1 16.9
Printed as 16.9%Official leaderboard, measured May 2026Configuration: All Domains pass@1; openclaw harnessCHI-Bench leaderboard (actAVA) official leaderboard, actAVA, 12 Aug 2026. CHI-Bench v1.0.0, All Domains, rank 18, Agent/Model and Accuracy columns; PA/UM/CM follow; board last updated 2026-08-12; board Date column 2026-05-01; accessed 2026-09-30. Linked submission provenance: run 2026-05-02T09:10:57Z to 2026-05-04T02:49:29Z; submitted_at 2026-05-04T02:49:29Z.18 | openclaw | glm-5.1 | Open-source | 16.9% | 13.3% | 26.7% | 10.7% | 2026-05-01
Every result from this document- Also printed in CHI-Bench submission manifest: openclaw + openrouter/z-ai/glm-5.1, submission.model; provenance.started_at / finished_at; results.overall.pass_at_1; retrieved 2026-09-30
29CHI-Bench All Domains pass@1 11.1
Printed as 11.1%Official leaderboard, measured May 2026Configuration: All Domains pass@1; deepagents harnessCHI-Bench leaderboard (actAVA) official leaderboard, actAVA, 12 Aug 2026. CHI-Bench v1.0.0, All Domains, rank 30, Agent/Model and Accuracy columns; PA/UM/CM follow; board last updated 2026-08-12; board Date column 2026-05-01; accessed 2026-09-30. Linked submission provenance: run 2026-05-03T21:51:53Z to 2026-05-04T13:10:34Z; submitted_at 2026-05-04T13:10:34Z.30 | deepagents | glm-5.1 | Open-source | 11.1% | 17.3% | 10.7% | 5.3% | 2026-05-01
Every result from this document- Also printed in CHI-Bench submission manifest: deepagents + openrouter/z-ai/glm-5.1, submission.model; provenance.started_at / finished_at; results.overall.pass_at_1; retrieved 2026-09-30
Other Zhipu models: GLM 4.7, GLM 5.2, GLM 5.3, GLM 5.3 Flash