Deepstory Research

GLM-5.2 Benchmarks

How Z.ai’s GLM-5.2 stacks up against the frontier (Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro) and its predecessor GLM-5.1 — across long-horizon coding, standard coding, reasoning, and agentic benchmarks, plus the MTP speculative-decoding gain. The strongest open-source model at launch.

Curated snapshot Last updated: Aug 5, 2026 28 data points
Long-horizon coding benchmarks

Long-horizon coding benchmarks

Score (higher = better). GLM-5.2 trails Opus 4.8 narrowly and is the top open-source model.

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Standard coding benchmarks

Standard coding benchmarks

Score (higher = better). GLM-5.2 closes much of the gap to the closed-source frontier.

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Reasoning benchmarks

Reasoning benchmarks

Score (higher = better). GLM-5.2 leads on AIME 2026; competitive across the board.

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Agentic benchmarks

Agentic benchmarks

Score (higher = better). Tool use and multi-step agent tasks.

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Generational leap: GLM-5.2 vs GLM-5.1

Generational leap: GLM-5.2 vs GLM-5.1

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

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MTP speculative decoding — acceptance length

MTP speculative decoding — acceptance length

Ablation on coding scenarios. Each technique lifts acceptance length; +20% over baseline.

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Data table

GLM-5.2 Benchmarks — full data table
glm51 glm52 gpt55 gemini opus48 series benchmark source_ref value_basis step acc_len
30.5 74.4 72.6 39.6 75.1 coding_long FrontierSWE zai-glm52 Full Benchmark Table — FrontierSWE (dominance as of 2026-06-16)
20.1 34.3 28.4 21.6 37.2 coding_long PostTrainBench zai-glm52 Full Benchmark Table — PostTrainBench
1 13 12 4 26 coding_long SWE-Marathon zai-glm52 Full Benchmark Table — SWE-Marathon
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

23 more rows + CSV download

The full 28-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.

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Methodology & sources

Last updated: Aug 5, 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.

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

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