Clinical Benchmarks
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GLM 5.2

Released 16 Jun 2026$1.4 input, $4.4 output per million tokens1M context753Bmit

3 results on 3 of 13 boards, latest measured Sep 2026. Compare with other models

Clinical Benchmarks Index28.9Rank 66 of 1492 of 10 boardsAverage × multiplier 50.0 × 0.577 = 28.9This 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

  1. MedScribe (Vals AI)
    model ID zai/glm-5.2; temperature=1; max_output_tokens=30000
    83.53
    Rank 43 of 105Leader Claude Opus 5.5 91.43
    Official leaderboard
    Measured Sep 2026
  2. MedCode (Vals AI)
    model ID zai/glm-5.2; temperature=1; max_output_tokens=30000
    40.77
    Rank 57 of 103Leader Claude Opus 5 63.57
    Official leaderboard
    Measured Sep 2026

EHR and workflow agents

  1. CHI-Bench
    Listed as openai-agents + glm-5.2
    All Domains pass@1; openai-agents harness
    18.7
    Rank 14 of 43 here, 45 models on the boardLeader erius + claude-opus-5 54.7
    Official leaderboard
    Measured Jul 2026

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)83.5383.5×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID zai/glm-5.2; temperature=1; max_output_tokens=30000
  • MedCode (Vals AI)40.7740.8×0.911model ID zai/glm-5.2; temperature=1; max_output_tokens=30000
Weighted average of 2 boards
50.0
Multiplier for 2 of 10 boards
× 0.577
Index
50.0 × 0.577 = 28.9

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 3 results
  1. 43MedScribe (Vals AI) glm-5.2 83.53
    Printed as 83.53%Official leaderboard, measured Sep 2026Configuration: model ID zai/glm-5.2; temperature=1; max_output_tokens=30000
    Vals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 43 of 105 (GLM 5.2), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["zai/glm-5.2"].
    43 | GLM 5.2 | 83.53%±2.00 | $1.4/$4.4 | 2m18s
    Every result from this document
  2. 57MedCode (Vals AI) glm-5.2 40.77
    Printed as 40.77%Official leaderboard, measured Sep 2026Configuration: model ID zai/glm-5.2; temperature=1; max_output_tokens=30000
    Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 57 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["zai/glm-5.2"].
    57 | GLM 5.2 | 40.77%±2.17 | $1.4/$4.4 | 95.27s
    Every result from this document
  3. 14CHI-Bench All Domains pass@1 18.7
    Printed as 18.7%Official leaderboard, measured Jul 2026Configuration: All Domains pass@1; openai-agents harness
    CHI-Bench leaderboard (actAVA) official leaderboard, actAVA, 12 Aug 2026. CHI-Bench v1.0.0, All Domains, rank 16, Agent/Model and Accuracy columns; PA/UM/CM follow; board last updated 2026-08-12; board Date column 2026-07-06; accessed 2026-09-30. Linked submission provenance: run 2026-07-02T04:22:04Z to 2026-07-02T04:48:05Z; submitted_at 2026-07-06T05:23:05Z.
    16 | openai-agents | glm-5.2 | Open-source | 18.7% | 20.0% | 32.0% | 4.0% | 2026-07-06
    Every result from this document

Other Zhipu models: GLM 4.7, GLM 5.1, GLM 5.3, GLM 5.3 Flash