GLM vs Gemini 3.1 Pro is the most lopsided comparison on this site, and it is lopsided in the open-weight model's favour. Across 19 published evaluations GLM-5.2 wins 15, including every single coding and long-horizon row, while Gemini 3.1 Pro's wins are concentrated in a narrow band of knowledge-heavy reasoning benchmarks.
That shape is worth stating plainly rather than dressing up: these are two models optimised for different things. All figures are results as published by the model authors and reproduced here without modification; glmmodel.com does not run these evaluations. Win chips are arithmetic on the published numbers.
GLM-5.2 takes 15 of the 19 published rows against Gemini 3.1 Pro, and the coding block is a clean sweep — FrontierSWE Dominance 74.4 to 39.6, DeepSWE 46.2 to 10.0, ProgramBench 63.7 to 39.5, NL2Repo 48.9 to 33.4, SWE-bench Pro 62.1 to 54.2 and Terminal-Bench 2.1 81.0 to 74.0. Gemini 3.1 Pro's four wins are HLE (45.0 to 40.5), GPQA-Diamond (94.3 to 91.2), HMMT Nov. 2025 by four tenths and Tool-Decathlon by six tenths. If your work is software engineering, the published data points hard at the GLM model; if it is broad academic knowledge, Gemini keeps an edge.
As with the other closed-weight comparisons, the structural difference is ownership. GLM-5.2 publishes a 1,000,000-token window and MIT-licensed weights that run on the standard open inference stack. Gemini 3.1 Pro is a hosted proprietary model; its context ceiling is outside the published comparison set this site works from, so the table records that rather than estimating.
| Feature | GLM-5.2 | Gemini 3.1 Pro |
|---|---|---|
| Context window | 1,000,000 tokens | Not published |
| Openness | Open weights | Closed API |
| License | MIT license | Proprietary |
| Effort control | Explicit — Non-Thinking, High, Max | Implicit |
| Self-hosting | Yes — transformers, vLLM, SGLang, xLLM, ktransformers | No — hosted API only |
The coding and long-horizon block needs little commentary: nine rows, nine GLM-5.2 wins, several by more than twenty points. The reasoning and agentic block is genuinely mixed. Gemini leads on HLE and GPQA-Diamond, which reward broad academic knowledge, and edges HMMT Nov. 2025 and Tool-Decathlon by fractions of a point. GLM-5.2 takes AIME 2026, HMMT Feb. 2026, CritPt, IMOAnswerBench, HLE with tools and the MCP-Atlas agentic set. The split is coherent rather than arbitrary. Benchmarks that ask a model to recall and apply broad factual knowledge in one pass favour Gemini 3.1 Pro; benchmarks that ask a model to plan, execute, read tool output and revise across many turns favour GLM-5.2, and the longer the trajectory the wider the gap gets. Deciding which of those two shapes your own work resembles is more useful than totalling the wins.
| Benchmark | GLM-5.2 | Gemini 3.1 Pro | Winner |
|---|---|---|---|
| Coding & long-horizon | |||
| FrontierSWE Dominance | 74.4 | 39.6 | GLM-5.2 |
| PostTrainBench | 34.3 | 21.6 | GLM-5.2 |
| SWE-Marathon | 13.0 | 4.0 | GLM-5.2 |
| SWE-bench Pro | 62.1 | 54.2 | GLM-5.2 |
| NL2Repo | 48.9 | 33.4 | GLM-5.2 |
| ProgramBench | 63.7 | 39.5 | GLM-5.2 |
| DeepSWE | 46.2 | 10.0 | GLM-5.2 |
| Terminal-Bench 2.1 (Terminus-2) | 81.0 | 74.0 | GLM-5.2 |
| Terminal-Bench 2.1 (best harness) | 82.7 | 70.7 | GLM-5.2 |
| Reasoning & agentic | |||
| MCP-Atlas (public set) | 76.8 | 69.2 | GLM-5.2 |
| Tool-Decathlon | 48.2 | 48.8 | Gemini 3.1 Pro |
| HLE | 40.5 | 45.0 | Gemini 3.1 Pro |
| HLE w/ Tools | 54.7 | 51.4 | GLM-5.2 |
| AIME 2026 | 99.2 | 98.2 | GLM-5.2 |
| HMMT Nov. 2025 | 94.4 | 94.8 | Gemini 3.1 Pro |
| HMMT Feb. 2026 | 92.5 | 87.3 | GLM-5.2 |
| CritPt | 20.9 | 17.7 | GLM-5.2 |
| IMOAnswerBench | 91.0 | 81.0 | GLM-5.2 |
| GPQA-Diamond | 91.2 | 94.3 | Gemini 3.1 Pro |
* score on the full set.
All figures on this page are results as published by the model authors. glmmodel.com reports them; it does not run these evaluations.
See all 17 GLM benchmarks in detail →On this published data, software engineering is not a close call. Every coding and long-horizon row goes to the GLM model, usually by a wide margin, and it adds a 1,000,000-token context window and open weights on top. For agent workloads it also leads the MCP-Atlas public set 76.8 to 69.2.
Gemini 3.1 Pro's advantages here are real but narrow. It leads HLE at 45.0 to 40.5 and GPQA-Diamond at 94.3 to 91.2 — both benchmarks that reward broad, graduate-level factual knowledge rather than execution over a long trajectory. If that is what you are buying, it is the stronger model on this table.
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