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Generational leap: GLM-5.2 vs GLM-5.1

The biggest jumps over the predecessor.

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About this data

The gap between GLM-5.2 and GLM-5.1 is largest exactly where long-horizon capability matters: SWE-Marathon (13.0 vs 1.0), DeepSWE (46.2 vs 18.0), FrontierSWE (74.4 vs 30.5) and CritPt (20.9 vs 4.6). These step-changes are the practical payoff of the 1M-context training and the new architecture.

Generational leap: GLM-5.2 vs GLM-5.1

Score (higher = better). The biggest jumps over the predecessor.

View data & sources →

Data table

Generational leap: GLM-5.2 vs GLM-5.1 — generational data table (GLM-5.2 Benchmarks)
glm51 glm52 series benchmark source_ref value_basis
1 13 generational SWE-Marathon zai-glm52 5.2 vs 5.1: 13.0 vs 1.0
4.6 20.9 generational CritPt zai-glm52 5.2 vs 5.1: 20.9 vs 4.6
18 46.2 generational DeepSWE zai-glm52 5.2 vs 5.1: 46.2 vs 18.0
30.5 74.4 generational FrontierSWE zai-glm52 5.2 vs 5.1: 74.4 vs 30.5
20.1 34.3 generational PostTrainBench zai-glm52 5.2 vs 5.1: 34.3 vs 20.1

2 more rows + CSV download

The full 7-row dataset, one-click CSV export, and the AI-ready context file are free with an account. Prefer to verify it yourself? The full methodology and sources are published below.

Methodology & sources

Last updated: Jul 17, 2026

Methodology

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.

Sources

Comparisons are informative, not definitive. See each source for definitions and limits.

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