Head to head · Open-weight models you run yourself · October 2026 research run
MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B
MiMo-V2.6 Distill Qwen 9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.
Ahead on
- Reach, 80 against 70
- Risk, 65 against 55
Watch for
Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's
Best for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence
Ahead on
- Documentation and upkeep, 48 against 40
Watch for
The pixel decoder ships as a pickle file
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach MiMo-V2.6 Distill Qwen 9B +10
MiMo-V2.6 Distill Qwen 9B 80
Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.
WeVisOne-VL-2B 70
Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.
Risk MiMo-V2.6 Distill Qwen 9B +10
MiMo-V2.6 Distill Qwen 9B 65
File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.
WeVisOne-VL-2B 55
File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
Documentation and upkeep WeVisOne-VL-2B +8
MiMo-V2.6 Distill Qwen 9B 40
Model card 5 of 20, intended use and parameters only. Training data 5, a domain mix with token counts but no sources or cut-off. Last change 20, README updates on 28 October 2026. Responsiveness 0, Xiaomi answered none of the 14 discussions. Version policy 5, a first release. File health 5, no LICENSE file, a config that lists an MTP layer the weights lack, and template and tool-call reports waiting without a reply.
WeVisOne-VL-2B 48
Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.
Score by category
| Category | Weight this run | MiMo-V2.6 Distill Qwen 9B | WeVisOne-VL-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 70 | MiMo-V2.6 Distill Qwen 9B +10 |
| Risk | 20%20 | 65 | 55 | MiMo-V2.6 Distill Qwen 9B +10 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 40 | 48 | WeVisOne-VL-2B +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | MiMo-V2.6 Distill Qwen 9B | WeVisOne-VL-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Xiaomi (MiMo team) | Tencent (WeChat Vision) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-21 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.3/5 (4) | none |
How the scores have moved
WeVisOne-VL-2B
No score change since it was listed.
Verdicts
MiMo-V2.6 Distill Qwen 9B
A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.
WeVisOne-VL-2B
A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.
Before you call either
MiMo-V2.6 Distill Qwen 9B
- Quickest start is
ollama run mimo-v2.6:9b, Ollama's 6.6 GB q4_K_M build with text and image input - Serve it as the card does,
sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo, and add--tool-call-parser mimofor parsed tool calls - SGLang's mimo reasoning parser expects thinking to be asked for, so pass
chat_template_kwargswithenable_thinkingtrue, as the card does, and readreasoning_content - Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read
- The generation config samples at temperature 0.6, top_k 20 and top_p 0.95
- Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them
- The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer
- It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens
- If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks
WeVisOne-VL-2B
- Use Tencent's package:
WeVisOneVL("tencent/WeVisOne-VL-2B", backend="vllm")thenmodel.predict(image, task=..., query=...) - Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
- A None answer means the target isn't in the image. Treat it as a result, not an error
- mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
- Send one image a turn. Video grids are rejected by the decoder
- The card says it isn't for identifying individuals or for high-impact decisions on its own
Questions
Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or WeVisOne-VL-2B?
MiMo-V2.6 Distill Qwen 9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on documentation and upkeep.
Other comparisons with MiMo-V2.6 Distill Qwen 9B or WeVisOne-VL-2B
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- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-4B-Instruct
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- MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B
- MiniCPM5-2B vs WeVisOne-VL-2B
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- MiniMax-M3.1-Flash vs WeVisOne-VL-2B
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- Muse Glimmer 30B vs WeVisOne-VL-2B
- Nanbeige4.2-3B vs WeVisOne-VL-2B
- Nanbeige4.3-33B vs WeVisOne-VL-2B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B
- Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B
- Nemotron 3 Nano 4B vs WeVisOne-VL-2B
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- Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-2B
- North Mini Code 1.0 vs WeVisOne-VL-2B
- occamy-1.0 vs WeVisOne-VL-2B
- Olmo 3 7B Think vs WeVisOne-VL-2B
- Ornith-1.5-122B vs WeVisOne-VL-2B
- Ornith-1.5-35B-A3B vs WeVisOne-VL-2B
- Ornith-1.5-397B vs WeVisOne-VL-2B
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- OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B
- Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B
- Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-2B
- Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B
- Qwen3.5-0.8B vs WeVisOne-VL-2B
- Qwen3.5-122B-A10B vs WeVisOne-VL-2B
- Qwen3.5-2B vs WeVisOne-VL-2B
- Qwen3.5-397B-A17B vs WeVisOne-VL-2B
- Qwen3.5-4B vs WeVisOne-VL-2B
- Qwen3.5-9B vs WeVisOne-VL-2B
- Qwen3.6-27B vs WeVisOne-VL-2B
- Qwen3.6-35B-A3B vs WeVisOne-VL-2B
- Qwen3.8-27B vs WeVisOne-VL-2B
- Qwen3.8-Flash-Next vs WeVisOne-VL-2B
- Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-Coder-Next vs WeVisOne-VL-2B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B
- Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B
- Recursive Flint vs WeVisOne-VL-2B
- Ripple-65B Preview vs WeVisOne-VL-2B
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B
- SmolLM3-3B vs WeVisOne-VL-2B
- Spark-X2.6-4B vs WeVisOne-VL-2B
- Step 3.7 Flash vs WeVisOne-VL-2B
- Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B
- WeVisOne-VL-2B vs WeVisOne-VL-4B
- WeVisOne-VL-2B vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B
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