MiMo V2.6 Pro vs GLM 5.3
MiMo V2.6 Pro and GLM 5.3 split the 2 benchmarks both publish, 1–1. The widest gap is on Terminal-Bench 4.0, where GLM 5.3 leads by 7.0 points. Both have open weights; GLM 5.3 is far lighter to host, needing about 520 GB at 4-bit against 704 GB for MiMo V2.6 Pro.
MiMo V2.6 ProXiaomi · OpenLLM Buddy catalog—composite
GLM 5.3Z.ai (Zhipu) · Open weights82.7compositeShared 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.
- Terminal-Bench89.988.2
Terminal Bench 2.1Terminal-Bench 2.1
- Terminal-Bench 4.034.941.9
Terminal Bench 4.0Terminal-Bench 4, Max, mini-swe-agent harness
- LMArena Text Elo · not counted14801481
LMArena text overall, 'MiMo-V2.6-Pro', ±9, as of 2026-10-27GLM-5.3 (Max), ±5, board dated 2026-10-27
Should you pick MiMo V2.6 Pro or GLM 5.3?
Pick MiMo V2.6 Pro if…
- Cost matters: $0.870 per million output tokens against $4.40.
- You want it running in minutes on a dedicated GPU through OpenLLM Buddy, in an uncensored build.
Pick GLM 5.3 if…
- You care most about Terminal-Bench 4.0, where it scores at least 2 points higher.
- You want the lighter model to host: about 520 GB at 4-bit against 704 GB.
- You need fast responses: about 88 tokens per second through APIs, against 47.
Hardware and cost to run
MiMo V2.6 Pro
Even at 4-bit it needs about 704 GB (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 2.3 TB (Multiple nodes (over 1.1 TB)).
On OpenLLM Buddy: MiMo-V2.6-Pro-RL-UNCENSORED runs on 8× H200, with an OpenAI-compatible API and no per-token fees. Prices start at $559 USD for 11 hours.
Deploy MiMo V2.6 ProGLM 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)).
Category profile
Category index uses every test each model reports, not only the shared ones.
Specs side by side
| MiMo V2.6 Pro | GLM 5.3 | |
|---|---|---|
| Lab | Xiaomi | Z.ai (Zhipu) |
| Weights | Open | Open |
| Released | 2026-09-21 | 2026-08-14 |
| Parameters | 1.0T total, 42B active (MoE) | 753B total, 40B active (MoE) |
| Context | 1M | 1M |
| License | MIT | GLM-5.3 License (custom; arena.ai lists MIT) |
| 4-bit memory | 704 GB · 8× H200 (1,128 GB) | 520 GB · 4× H200 (564 GB) |
| API price | $0.435 in / $0.870 out per 1M tokens | $1.40 in / $4.40 out per 1M tokens |
| API speed | 47 tok/s | 88 tok/s |
Related comparisons
Sources
| Model | Test | Score | Setting | Source |
|---|---|---|---|---|
| MiMo V2.6 Pro | Terminal-Bench | 89.9 | Terminal Bench 2.1 | MiMo-V2.6-Pro-RL model card (Hugging Face) |
| GLM 5.3 | Terminal-Bench | 88.2 | Terminal-Bench 2.1 | Hugging Face model card (GLM-5.3) |
| MiMo V2.6 Pro | Terminal-Bench 4.0 | 34.9 | Terminal Bench 4.0 | MiMo-V2.6-Pro-RL model card (Hugging Face) |
| GLM 5.3 | Terminal-Bench 4.0 | 41.9 | Terminal-Bench 4, Max, mini-swe-agent harness | Hugging Face model card (GLM-5.3), evaluation results |
| MiMo V2.6 Pro | LMArena Text Elo | 1480 | LMArena text overall, 'MiMo-V2.6-Pro', ±9, as of 2026-10-27 | Arena (LMArena) text leaderboard |
| GLM 5.3 | LMArena Text Elo | 1481 | GLM-5.3 (Max), ±5, board dated 2026-10-27 | LMArena (arena.ai) text leaderboard |
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