GLM 5.3 vs DeepSeek V4 Pro
GLM 5.3 scores higher on 3 of the 4 benchmarks both models publish. The widest gap is on Humanity's Last Exam (with tools), where GLM 5.3 leads by 2.5 points. Both have open weights; GLM 5.3 is far lighter to host, needing about 520 GB at 4-bit against 1.1 TB for DeepSeek V4 Pro.
Shared benchmarks
Raw published scores; the higher score on each row is highlighted. LMArena Elo is a human-preference vote and is not counted as a win.
- GPQA Diamond91.790.1
GLM 5.3 (max); competitor comparison columnV4-Pro (Preview) Max, pass@1
- Humanity's Last Exam42.342.7
no tools, text-only subset (2,158 q); competitor comparison columnV4-Pro-0813, no tools
- Humanity's Last Exam (with tools)62.560.0
with toolsV4-Pro-0813, with tools
- Terminal-Bench88.287.9
Terminal-Bench 2.1Terminal-Bench 2.1, V4-Pro-0813, max effort, DeepSeek Harness
- LMArena Text Elo · not counted14811465
GLM-5.3 (Max), ±5, board dated 2026-10-27DeepSeek-V4-Pro (High, 20260813), ±7, board dated 2026-10-27
Should you pick GLM 5.3 or DeepSeek V4 Pro?
Pick GLM 5.3 if…
- You care most about Humanity's Last Exam (with tools), where it scores at least 2 points higher.
- People prefer its answers in blind chats: LMArena Elo 1481 against 1465.
- You want the lighter model to host: about 520 GB at 4-bit against 1.1 TB.
Pick DeepSeek V4 Pro if…
- Its published results are close enough that cost and hosting should decide.
Hardware and cost to run
GLM 5.3
Even at 4-bit it needs about 520 GB (4× H200 (564 GB)), well beyond a home workstation, so most people run it on rented multi-GPU servers. Full BF16 precision needs about 1.7 TB (Multiple nodes (over 1.1 TB)).
DeepSeek V4 Pro
Even at 4-bit it needs about 1.1 TB (8× H200 (1,128 GB)), well beyond a home workstation, so most people run it on rented multi-GPU servers. Full BF16 precision needs about 3.7 TB (Multiple nodes (over 1.1 TB)).
Category profile
Category index uses every test each model reports, not only the shared ones.
Specs side by side
| GLM 5.3 | DeepSeek V4 Pro | |
|---|---|---|
| Lab | Z.ai (Zhipu) | DeepSeek |
| Weights | Open | Open |
| Released | 2026-08-14 | 2026-08-13 |
| Parameters | 753B total, 40B active (MoE) | 1.6T total, 49B active (MoE) |
| Context | 1M | 1M |
| License | GLM-5.3 License (custom; arena.ai lists MIT) | MIT |
| 4-bit memory | 520 GB · 4× H200 (564 GB) | 1.1 TB · 8× H200 (1,128 GB) |
| API price | $1.40 in / $4.40 out per 1M tokens | $1.32 in / $3.96 out per 1M tokens |
| API speed | 88 tok/s | 94 tok/s |
Related comparisons
Sources
Scores are published results from model cards, lab reports, and public leaderboards, each linked to its source. Settings such as thinking mode or test version differ between sources and are shown next to each score. OpenLLM Buddy catalog models are uncensored fine-tunes; their scores are those of the official base model. Data collected 2026-10-30. How the index works