Standard coding benchmarks
Terminal-Bench 2.1, SWE-bench Pro, NL2Repo, DeepSWE, ProgramBench.
About this data
On standard coding suites GLM-5.2 is the strongest open-source model and improves sharply on GLM-5.1 (e.g. 81.0 vs 63.5 on Terminal-Bench 2.1). It lands within a few points of Claude Opus 4.8 on Terminal-Bench while staying ahead of Gemini 3.1 Pro, though closed models still lead on several suites.
Score (higher = better). GLM-5.2 closes much of the gap to the closed-source frontier.
View data & sources →Data table
| glm51 | glm52 | gpt55 | gemini | opus48 | series | benchmark | source_ref | value_basis |
|---|---|---|---|---|---|---|---|---|
| 63.5 | 81 | 84 | 74 | 85 | coding_std | Terminal-Bench 2.1 | zai-glm52 | Full Benchmark Table — Terminal-Bench 2.1 (Terminus-2) |
| 58.4 | 62.1 | 58.6 | 54.2 | 69.2 | coding_std | SWE-bench Pro | zai-glm52 | Full Benchmark Table — SWE-bench Pro |
| 42.7 | 48.9 | 50.7 | 33.4 | 69.7 | coding_std | NL2Repo | zai-glm52 | Full Benchmark Table — NL2Repo |
| 18 | 46.2 | 70 | 10 | 58 | coding_std | DeepSWE | zai-glm52 | Full Benchmark Table — DeepSWE |
| 50.9 | 63.7 | 70.8 | 39.5 | 71.9 | coding_std | ProgramBench | zai-glm52 | Full Benchmark Table — ProgramBench |
Methodology & sources
Last updated: Jul 17, 2026Methodology
Source-backed values for all six charts come from the Z.ai GLM-5.2 launch post (2026-06-16): long-horizon coding (FrontierSWE, PostTrainBench, SWE-Marathon), standard coding (Terminal-Bench 2.1, SWE-bench Pro, NL2Repo, DeepSWE, ProgramBench), reasoning (HLE, CritPt, AIME, HMMT, GPQA-Diamond, IMOAnswerBench), agentic (MCP-Atlas, Tool-Decathlon), the GLM-5.2 vs GLM-5.1 generational leap, and the MTP acceptance-length ablation. Every numeric point carries a sources[].ref and a value_basis naming the table row. Scores are reproduced verbatim from the Full Benchmark Table; cells the vendor published as "-" (not reported) are omitted, never estimated. CAVEAT: these are the model developer’s self-reported figures under their stated harnesses and prompts (see the post’s footnotes); some competitor HLE/CritPt cells are full-set scores. They are comparison-as-published, not an independent eval. Architecture context (not charted): 1M-token context (up from 200K), IndexShare cuts per-token indexer FLOPs ~2.9× at 1M, MIT-licensed open weights. Re-verified 2026-06-22.
Comparisons are informative, not definitive. See each source for definitions and limits.