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Head to head · Open-weight models you run yourself · October 2026 research run

MiniMax-M3.1-Flash vs WeVisOne-VL-2B

WeVisOne-VL-2B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories. 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

MiniMax-M3.1-FlashMiniMax · Open-weight model C54.3/100#131 of 160 open-weight models WeVisOne-VL-2BTencent (WeChat Vision) · Open-weight model C61/100#83 of 160 open-weight models

Which one, for what

MiniMax-M3.1-Flash C

Best for Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

SGLang's cookbook still points to a pull request or a dev image on 4 November 2026, and vLLM's recipe still names a nightly image, though both runtimes' releases carry the model code

WeVisOne-VL-2B C

Best for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence

Ahead on

  • Reach, 70 against 55
  • Risk, 55 against 35
  • Documentation and upkeep, 48 against 41

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.

Risk WeVisOne-VL-2B +20

MiniMax-M3.1-Flash 35

File safety earns 15, safetensors only, less 10 because the SGLang route the card links passes trust_remote_code for the repository's configuration and processor code. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing; the launch post's security testing measured offensive capability, and the template's root role isn't documented as a trained hierarchy. Licence risk earns 5, after the military-use ban and the $20M threshold. The security programme earns nothing, with no security.txt, disclosure policy or advisories.

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.

Reach WeVisOne-VL-2B +15

MiniMax-M3.1-Flash 55

Memory earns nothing. MiniMax's only build is its 250 GB NVFP4 and MXFP8 release, about 300 GB with the fifth added, above 256 GB, and no runtime library has a local build. Runtimes earn 20. llama.cpp from b11861 runs it, Ollama v0.40.14 pins llama.cpp b11963 with the minimax-m3 architecture, vLLM v0.33.0 (tagged 1 November 2026) registers the architecture and meets its recipe's minimum version, and SGLang v0.5.23 (tagged 29 October) registers it with M3.1 processor code and tests, though its cookbook still points to a pull request. Transformers support is in progress per the card and LM Studio has no entry. Builds earn 10 for one publisher precision. Terms earn 10, since commercial use is free with attribution and a notice below $20M of yearly revenue. Download earns 10, with no gate. Hosted earns 5, since MiniMax's API and Novita serve it. 0 + 20 + 10 + 10 + 10 + 5 = 55.

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.

Documentation and upkeep WeVisOne-VL-2B +7

MiniMax-M3.1-Flash 41

The model card earns 11, missing limitations, languages and a knowledge cut-off. Training data earns nothing, as it isn't described. The last change earns 20, an evaluation-results commit of 30 October 2026 after the release commit of 23 October. Responsiveness earns nothing, with no MiniMax reply in nine discussions opened from 23 to 31 October. Version policy earns 5 for a first release in its own repository. File health earns 5, for the tokenizer's 40,960,000-token model_max_length against the 1M context, the Transformers library tag against the card, and an unanswered vLLM streaming report. 11 + 0 + 20 + 0 + 5 + 5 = 41.

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

CategoryWeight this runMiniMax-M3.1-FlashWeVisOne-VL-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255570WeVisOne-VL-2B +15
Risk20%203555WeVisOne-VL-2B +20
Agent interface10%107070even
Documentation and upkeep10%104148WeVisOne-VL-2B +7
Negative events≤1500
TotalC 54.3/100C 61/100

Facts side by side

FactMiniMax-M3.1-FlashWeVisOne-VL-2B
KindOpen-weight modelOpen-weight model
VendorMiniMaxTencent (WeChat Vision)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMiniMax Community License (custom; commercial use needs a "Built with MiniMax M3.1-Flash" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-232026-10-28
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews2.3/5 (4)none

How the scores have moved

MiniMax-M3.1-Flash

WeVisOne-VL-2B

No score change since it was listed.

Verdicts

MiniMax-M3.1-Flash

A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights. vLLM 0.33.0 and SGLang 0.5.23 carry its code. Commercial use needs attribution and notice.

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

MiniMax-M3.1-Flash

  1. Check the use case first; commercial products must show "Built with MiniMax M3.1-Flash" and notify MiniMax once, or get written authorisation above $20M yearly revenue
  2. On 8 H100 or H200 GPUs use vLLM 0.33.0 or later, or the recipe's image vllm/vllm-openai:minimax-m31-flash, with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144
  3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code
  4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128
  5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95
  6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off
  7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage
  8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort

WeVisOne-VL-2B

  1. Use Tencent's package: WeVisOneVL("tencent/WeVisOne-VL-2B", backend="vllm") then model.predict(image, task=..., query=...)
  2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
  3. A None answer means the target isn't in the image. Treat it as a result, not an error
  4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
  5. Send one image a turn. Video grids are rejected by the decoder
  6. The card says it isn't for identifying individuals or for high-impact decisions on its own

Questions

Which is better for AI agents, MiniMax-M3.1-Flash or WeVisOne-VL-2B?

WeVisOne-VL-2B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories.

Other comparisons with MiniMax-M3.1-Flash or WeVisOne-VL-2B

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