GLM vs DeepSeek-V4-Pro is the comparison that matters most if you have already decided to run open weights. Both models come from the open-weight world rather than a hosted API, so the choice is not about ownership or data locality — it is purely about which model does the work better, and where each one's strengths actually lie.
Across 16 published evaluations where both models have a score, GLM-5.2 wins 13, DeepSeek-V4-Pro wins two and one row ties exactly. As with every page here, the numbers are results as published by the model authors and the chips are arithmetic on those numbers; glmmodel.com does not run the evaluations.
GLM-5.2 leads this comparison decisively, taking 13 of 16 rows. The coding block is a sweep, and several margins are enormous: DeepSWE 46.2 to 8.0, FrontierSWE Dominance 74.4 to 29.0, ProgramBench 63.7 to 47.8 and Terminal-Bench 2.1 81.0 to 64.0. DeepSeek-V4-Pro's two wins are worth knowing about because they are not accidents — Tool-Decathlon at 52.8 to 48.2 is one of the few rows where any model beats GLM-5.2 on agentic tooling, and HMMT Feb. 2026 at 95.2 to 92.5 shows real competition-mathematics strength. HMMT Nov. 2025 ties at 94.4.
This is an open-versus-open comparison, so the usual openness argument largely cancels out and the table below reflects that honestly. What does not cancel out is the published context window and the explicit effort-level control, both of which are documented for the GLM model and not part of the published comparison set for DeepSeek-V4-Pro.
| Feature | GLM-5.2 | DeepSeek-V4-Pro |
|---|---|---|
| Context window | 1,000,000 tokens | Not published |
| Openness | Open weights | Open-weight family |
| License | MIT license | See the vendor's release terms |
| Effort control | Explicit — Non-Thinking, High, Max | Not published |
| Self-hosting | Yes — transformers, vLLM, SGLang, xLLM, ktransformers | Vendor-dependent |
In the coding and long-horizon block GLM-5.2 wins all six rows, and the gaps widen as tasks get longer — a nine-point lead on Terminal-Bench becomes a forty-five-point lead on FrontierSWE Dominance. The reasoning block is closer: GLM-5.2 takes HLE, HLE with tools, CritPt, AIME 2026, IMOAnswerBench and GPQA-Diamond, DeepSeek takes HMMT Feb. 2026 and Tool-Decathlon, and HMMT Nov. 2025 lands on exactly the same score for both. That identical HMMT Nov. 2025 result is a useful reminder of how these tables should be read. Two models scoring 94.4 on the same benchmark are not equivalent models; they are two models that happened to converge on one saturated evaluation. The rows that separate them are the ones far from ceiling, which on this page means the long-horizon coding block almost exclusively.
| Benchmark | GLM-5.2 | DeepSeek-V4-Pro | Winner |
|---|---|---|---|
| Coding & long-horizon | |||
| FrontierSWE Dominance | 74.4 | 29.0 | GLM-5.2 |
| SWE-bench Pro | 62.1 | 55.4 | GLM-5.2 |
| NL2Repo | 48.9 | 35.5 | GLM-5.2 |
| ProgramBench | 63.7 | 47.8 | GLM-5.2 |
| DeepSWE | 46.2 | 8.0 | GLM-5.2 |
| Terminal-Bench 2.1 (Terminus-2) | 81.0 | 64.0 | GLM-5.2 |
| Reasoning & agentic | |||
| MCP-Atlas (public set) | 76.8 | 73.6 | GLM-5.2 |
| Tool-Decathlon | 48.2 | 52.8 | DeepSeek-V4-Pro |
| HLE | 40.5 | 37.7 | GLM-5.2 |
| HLE w/ Tools | 54.7 | 48.2 | GLM-5.2 |
| CritPt | 20.9 | 12.9 | GLM-5.2 |
| AIME 2026 | 99.2 | 94.6 | GLM-5.2 |
| HMMT Nov. 2025 | 94.4 | 94.4 | Tie |
| HMMT Feb. 2026 | 92.5 | 95.2 | DeepSeek-V4-Pro |
| IMOAnswerBench | 91.0 | 89.8 | GLM-5.2 |
| GPQA-Diamond | 91.2 | 90.1 | GLM-5.2 |
* 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 →For agentic engineering on open weights, the published data does not leave much room for argument. GLM-5.2 wins every coding row and every long-horizon row where a DeepSeek score exists, and adds a published 1,000,000-token context window plus explicit effort levels on top of an MIT license.
DeepSeek-V4-Pro earns two rows here and they point at genuine strengths rather than noise. It is the only model in this comparison set that beats GLM-5.2 on Tool-Decathlon, and its competition-mathematics result on HMMT Feb. 2026 is the stronger of the pair. Both are narrow but real reasons to test it on your own workload.
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