DeepSeek V4.1 Flash vs MiMo V2.6 Pro
DeepSeek V4.1 Flash and MiMo V2.6 Pro split the 2 benchmarks both publish, 1–1. The widest gap is on Terminal-Bench 4.0, where MiMo V2.6 Pro leads by 3.7 points. Both have open weights; DeepSeek V4.1 Flash is far lighter to host, needing about 381 GB at 4-bit against 704 GB for MiMo V2.6 Pro.
DeepSeek V4.1 FlashDeepSeek · OpenLLM Buddy catalog79.8composite
MiMo V2.6 ProXiaomi · OpenLLM Buddy catalog—compositeShared 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-Bench90.689.9
Terminal-Bench 2.1, Pass@1, max effortTerminal Bench 2.1
- Terminal-Bench 4.031.234.9
Terminal-Bench 4.0, Pass@1, max effortTerminal Bench 4.0
- LMArena Text Elo · not counted14761480
LMArena text overall, 'DeepSeek-V4.1-Flash-20260910 (Max)', ±6, as of 2026-10-27LMArena text overall, 'MiMo-V2.6-Pro', ±9, as of 2026-10-27
Should you pick DeepSeek V4.1 Flash or MiMo V2.6 Pro?
Pick DeepSeek V4.1 Flash if…
- You want the lighter model to host: about 381 GB at 4-bit against 704 GB.
- You need fast responses: about 217 tokens per second through APIs, against 47.
- You want it running in minutes on a dedicated GPU through OpenLLM Buddy, in an uncensored build.
Pick MiMo V2.6 Pro if…
- You care most about Terminal-Bench 4.0, where it scores at least 2 points higher.
- You want it running in minutes on a dedicated GPU through OpenLLM Buddy, in an uncensored build.
Hardware and cost to run
DeepSeek V4.1 Flash
Even at 4-bit it needs about 381 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 1.3 TB (Multiple nodes (over 1.1 TB)).
On OpenLLM Buddy: DeepSeek-V4.1-Flash-UNCENSORED-FP8 runs on 4× H200 141GB as an FP8 build, with an OpenAI-compatible API and no per-token fees. Prices start at $280 USD for 11 hours.
Deploy DeepSeek V4.1 FlashMiMo 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 ProCategory profile
Category index uses every test each model reports, not only the shared ones.
Specs side by side
| DeepSeek V4.1 Flash | MiMo V2.6 Pro | |
|---|---|---|
| Lab | DeepSeek | Xiaomi |
| Weights | Open | Open |
| Released | 2026-09-10 | 2026-09-21 |
| Parameters | 552B total, 16B active (MoE) | 1.0T total, 42B active (MoE) |
| Context | 1M | 1M |
| License | MIT | MIT |
| 4-bit memory | 381 GB · 4× H200 (564 GB) | 704 GB · 8× H200 (1,128 GB) |
| API price | $0.300 in / $1.20 out per 1M tokens | $0.435 in / $0.870 out per 1M tokens |
| API speed | 217 tok/s | 47 tok/s |
Related comparisons
Sources
| Model | Test | Score | Setting | Source |
|---|---|---|---|---|
| DeepSeek V4.1 Flash | Terminal-Bench | 90.6 | Terminal-Bench 2.1, Pass@1, max effort | DeepSeek-V4.1-Flash model card (Hugging Face) |
| MiMo V2.6 Pro | Terminal-Bench | 89.9 | Terminal Bench 2.1 | MiMo-V2.6-Pro-RL model card (Hugging Face) |
| DeepSeek V4.1 Flash | Terminal-Bench 4.0 | 31.2 | Terminal-Bench 4.0, Pass@1, max effort | DeepSeek-V4.1-Flash model card (Hugging Face) |
| MiMo V2.6 Pro | Terminal-Bench 4.0 | 34.9 | Terminal Bench 4.0 | MiMo-V2.6-Pro-RL model card (Hugging Face) |
| DeepSeek V4.1 Flash | LMArena Text Elo | 1476 | LMArena text overall, 'DeepSeek-V4.1-Flash-20260910 (Max)', ±6, as of 2026-10-27 | Arena (LMArena) text leaderboard |
| 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 |
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