GLM vs GPT-5.5 is the closest of the frontier comparisons on this site, and the most genuinely split. Across 18 published evaluations the two models trade rows rather than one sweeping the table: GPT-5.5 dominates several standard coding benchmarks by wide margins, while the GLM model takes every long-horizon evaluation where both have a published score.
As everywhere on glmmodel.com, the numbers below are results as published by the model authors for each model, reproduced here rather than measured here. The win chips are arithmetic on those published figures. Different harnesses, context budgets and sampling settings will move any of these rows, which is exactly why the methodology notes matter as much as the scores.
GPT-5.5 takes 11 of the 18 published rows and GLM-5.2 takes 7, but the split is not random. GLM-5.2 wins all three long-horizon evaluations — FrontierSWE Dominance 74.4 to 72.6, PostTrainBench 34.3 to 28.4 and SWE-Marathon 13.0 to 12.0 — plus SWE-bench Pro, MCP-Atlas, HLE with tools and AIME 2026. GPT-5.5's wins are concentrated in short-horizon coding, where DeepSWE (70.0 to 46.2) and ProgramBench (70.8 to 63.7) are decisive, and in competition mathematics outside AIME. If your workload is hours long, the open-weight GLM model is the better bet on this data; if it is a bounded coding task, GPT-5.5 is.
The structural comparison is the familiar open-versus-closed one. GLM-5.2 publishes a 1,000,000-token context window and ships MIT-licensed weights you can serve yourself; GPT-5.5 is a proprietary hosted model whose context ceiling is not part of the published comparison set, so this table says so rather than guessing at it.
| Feature | GLM-5.2 | GPT-5.5 |
|---|---|---|
| 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 |
Reading the table by group makes the divide obvious. In the coding and long-horizon block, GLM-5.2 wins the four rows measured over multi-hour runs and loses the four measured over bounded tasks. In the reasoning and agentic block, GPT-5.5 takes most of the mathematics and the physics-flavoured CritPt row, while GLM-5.2 takes AIME 2026, HLE with tools and the MCP-Atlas agentic set. Very few of these margins exceed three points except on DeepSWE and ProgramBench. That matters when you are reading a comparison table for a decision rather than for a headline: a one-point difference on a single published run is not a reliable signal about your workload, whereas a twenty-point difference almost certainly is. Treat the narrow rows as a tie and the wide ones as evidence, and the picture resolves into something you can actually act on.
| Benchmark | GLM-5.2 | GPT-5.5 | Winner |
|---|---|---|---|
| Coding & long-horizon | |||
| FrontierSWE Dominance | 74.4 | 72.6 | GLM-5.2 |
| PostTrainBench | 34.3 | 28.4 | GLM-5.2 |
| SWE-Marathon | 13.0 | 12.0 | GLM-5.2 |
| SWE-bench Pro | 62.1 | 58.6 | GLM-5.2 |
| NL2Repo | 48.9 | 50.7 | GPT-5.5 |
| ProgramBench | 63.7 | 70.8 | GPT-5.5 |
| DeepSWE | 46.2 | 70.0 | GPT-5.5 |
| Terminal-Bench 2.1 (Terminus-2) | 81.0 | 84.0 | GPT-5.5 |
| Terminal-Bench 2.1 (best harness) | 82.7 | 83.4 | GPT-5.5 |
| Reasoning & agentic | |||
| MCP-Atlas (public set) | 76.8 | 75.3 | GLM-5.2 |
| Tool-Decathlon | 48.2 | 55.6 | GPT-5.5 |
| HLE | 40.5 | 41.4 | GPT-5.5 |
| HLE w/ Tools | 54.7 | 52.2 | GLM-5.2 |
| AIME 2026 | 99.2 | 98.3 | GLM-5.2 |
| HMMT Nov. 2025 | 94.4 | 96.5 | GPT-5.5 |
| HMMT Feb. 2026 | 92.5 | 96.7 | GPT-5.5 |
| CritPt | 20.9 | 27.1 | GPT-5.5 |
| GPQA-Diamond | 91.2 | 93.6 | GPT-5.5 |
* 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 →The GLM model is the stronger choice when the unit of work is a long trajectory rather than a single task. Every published long-horizon evaluation on this page goes its way, and it is the only one of the two you can run inside your own network with a 1,000,000-token window and explicit effort levels.
GPT-5.5 is comfortably ahead where tasks are bounded and well specified. Its DeepSWE result is more than twenty points clear, its ProgramBench and Terminal-Bench figures lead, and it takes most of the competition mathematics rows. If your pipeline is a queue of discrete, self-contained coding problems, this is the model the published data favours.
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