Head to head · Open-weight models you run yourself · October 2026 research run
Darwin-36B-Opus vs WeVisOne-VL-2B
Darwin-36B-Opus and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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 Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required
Ahead on
- Reach, 80 against 70
- Documentation and upkeep, 61 against 48
Watch for
The merge method is proprietary and the current weights include an undescribed targeted refinement
Best for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence
No category where it leads by five points or more, and no fact that sets it apart.
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.
Documentation and upkeep Darwin-36B-Opus +13
Darwin-36B-Opus 61
Model card 11, it covers architecture, parameters, context and languages but not intended use, limitations or a knowledge cut-off. Training data 5, the reasoning parent's fine-tune is described without datasets or a cut-off. Last change 20, on 1 November 2026 the publisher added a LICENSE file and a corrected eval_results note to the main repository and edited the POCKET-35B-GGUF card, read as this run reads card edits on other open-weight listings (Muse Glimmer 30B, MiMo-V2.6 Distill Qwen 9B, Apertus v1.5 8B). The weights date from 23 April and the GGUF build from 22 July. Responsiveness 20, the publisher answered 8 of the 13 discussions, and two of the rest are its own posts. Version policy 5, a first release whose weights were replaced twice in its first day with commit titles but no card note. File health 0, the card's head counts and base still disagree with the files, and the generation config drops the base's end-of-turn stop token. 11 + 5 + 20 + 20 + 5 + 0 = 61.
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.
Reach Darwin-36B-Opus +10
Darwin-36B-Opus 80
Memory 20, the publisher's Q4_K_M GGUF is 21.2 GB and needs about 25.4 GB with the fifth added, so 32 GB. Runtimes 20, the text-only Qwen3.5 MoE class runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, FINAL-Bench's POCKET-35B-GGUF has it at four precisions. Terms 15, Apache 2.0 with nothing added, with a LICENSE file since 1 November 2026, and Apache 2.0 on the base Qwen3.6-35B-A3B too. Download 10, nothing is gated. Hosted 0, no provider or API serves it.
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.
Score by category
| Category | Weight this run | Darwin-36B-Opus | WeVisOne-VL-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 70 | Darwin-36B-Opus +10 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 61 | 48 | Darwin-36B-Opus +13 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Darwin-36B-Opus | WeVisOne-VL-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | FINAL-Bench (VIDRAFT) | Tencent (WeChat Vision) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-23 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.5/5 (4) | none |
How the scores have moved
WeVisOne-VL-2B
No score change since it was listed.
Verdicts
Darwin-36B-Opus
A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.
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
Darwin-36B-Opus
- Quickest start is
llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja, about 21 GB of files - On the reference machine BF16 fits,
vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parsers for Qwen3.6's format - Thinking is on by default, send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
- generation_config.json stops only on the end-of-text token, so add
<|im_end|>as a stop token if a runtime keeps generating after the turn ends - Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
- Validate JSON output yourself, since structured output isn't documented
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, Darwin-36B-Opus or WeVisOne-VL-2B?
Darwin-36B-Opus and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C).
Other comparisons with Darwin-36B-Opus or WeVisOne-VL-2B
- Agents-A1-4B vs Darwin-36B-Opus
- Agents-A1-4B vs WeVisOne-VL-2B
- Apertus v1.5 8B vs Darwin-36B-Opus
- Apertus v1.5 8B vs WeVisOne-VL-2B
- Apodex-1.1-mini vs Darwin-36B-Opus
- Apodex-1.1-mini vs WeVisOne-VL-2B
- Arete-27B vs Darwin-36B-Opus
- Arete-27B vs WeVisOne-VL-2B
- BigBang-v1 vs Darwin-36B-Opus
- BigBang-v1 vs WeVisOne-VL-2B
- Carbon-X vs Darwin-36B-Opus
- Carbon-X vs WeVisOne-VL-2B
- Cola2 vs Darwin-36B-Opus
- Cola2 vs WeVisOne-VL-2B
- Cosmos-Reason2-2B vs Darwin-36B-Opus
- Cosmos-Reason2-2B vs WeVisOne-VL-2B
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs DeepSeek V4.1 Flash
- Darwin-36B-Opus vs DeepSeek-V4-Flash-0731
- Darwin-36B-Opus vs Devstral Small 2
- Darwin-36B-Opus vs DiffusionGemma 26B A4B IT
- Darwin-36B-Opus vs EXAONE 4.5 33B
- Darwin-36B-Opus vs Gemma 4 12B IT
- Darwin-36B-Opus vs Gemma 4 26B A4B IT
- Darwin-36B-Opus vs Gemma 4 31B IT
- Darwin-36B-Opus vs Gemma 4 E2B IT
- Darwin-36B-Opus vs Gemma 4 E4B IT
- Darwin-36B-Opus vs GLM-4.5-Air
- Darwin-36B-Opus vs GLM-4.7-Flash
- Darwin-36B-Opus vs GLM-5.3
- Darwin-36B-Opus vs GLM-5.3-Flash
- Darwin-36B-Opus vs gpt-oss-120b
- Darwin-36B-Opus vs gpt-oss-20b
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Granite 4.2 3B
- Darwin-36B-Opus vs Granite 4.2 8B
- Darwin-36B-Opus vs Hy-Memory-30B-A3B
- Darwin-36B-Opus vs Hy3
- Darwin-36B-Opus vs Inkling
- Darwin-36B-Opus vs Inkling-Small
- Darwin-36B-Opus vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Darwin-36B-Opus vs Kimi K3
- Darwin-36B-Opus vs Kimi K3.1 Pro
- Darwin-36B-Opus vs Kolibri-1
- Darwin-36B-Opus vs Kronos
- Darwin-36B-Opus vs Laguna M.1
- Darwin-36B-Opus vs Laguna S 2.1
- Darwin-36B-Opus vs Laguna XS 2.1
- Darwin-36B-Opus vs LBS-27B
- Darwin-36B-Opus vs LFM2.5-2.6B
- Darwin-36B-Opus vs LFM3-1.7B
- Darwin-36B-Opus vs LFM3-Audio-2B
- Darwin-36B-Opus vs LFM3-Omni-2B
- Darwin-36B-Opus vs LFM3-VL-2B
- Darwin-36B-Opus vs Ling-3.0-tiny
- Darwin-36B-Opus vs Ling-3.1-flash-1019
- Darwin-36B-Opus vs LongCat-2.5
- Darwin-36B-Opus vs MiMo-V2.6 Distill Qwen 9B
- Darwin-36B-Opus vs MiMo-V2.6-Flash-RL
- Darwin-36B-Opus vs MiniCPM-o 4.5
- Darwin-36B-Opus vs MiniCPM-V 4.7 1B
- Darwin-36B-Opus vs MiniCPM-V 4.7 35B-A3B
- Darwin-36B-Opus vs MiniCPM5-2B
- Darwin-36B-Opus vs MiniMax-M2.5
- Darwin-36B-Opus vs MiniMax-M2.7
- Darwin-36B-Opus vs MiniMax-M3.1-Flash
- Darwin-36B-Opus vs Mistral Large 4
- Darwin-36B-Opus vs Muse Glimmer 30B
- Darwin-36B-Opus vs Nanbeige4.2-3B
- Darwin-36B-Opus vs Nanbeige4.3-33B
- Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B
- Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B
- Darwin-36B-Opus vs Nemotron 3 Nano 4B
- Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Darwin-36B-Opus vs Nemotron 3 Super 120B A12B
- Darwin-36B-Opus vs Nemotron 3 Ultra 550B A55B
- Darwin-36B-Opus vs North Mini Code 1.0
- Darwin-36B-Opus vs occamy-1.0
- Darwin-36B-Opus vs Olmo 3 7B Think
- Darwin-36B-Opus vs Ornith-1.5-122B
- Darwin-36B-Opus vs Ornith-1.5-35B-A3B
- Darwin-36B-Opus vs Ornith-1.5-397B
- Darwin-36B-Opus vs Ornith-1.5-9B
- Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT
- Darwin-36B-Opus vs Qwen-AgentWorld-35B-A3B
- Darwin-36B-Opus vs Qwen3-30B-A3B-Instruct-2507
- Darwin-36B-Opus vs Qwen3-4B-Instruct-2507
- Darwin-36B-Opus vs Qwen3.5-0.8B
- Darwin-36B-Opus vs Qwen3.5-122B-A10B
- Darwin-36B-Opus vs Qwen3.5-2B
- Darwin-36B-Opus vs Qwen3.5-397B-A17B
- Darwin-36B-Opus vs Qwen3.5-4B
- Darwin-36B-Opus vs Qwen3.5-9B
- Darwin-36B-Opus vs Qwen3.6-27B
- Darwin-36B-Opus vs Qwen3.6-35B-A3B
- Darwin-36B-Opus vs Qwen3.8-27B
- Darwin-36B-Opus vs Qwen3.8-Flash-Next
- Darwin-36B-Opus vs Qwen3-Coder-30B-A3B-Instruct
- Darwin-36B-Opus vs Qwen3-Coder-Next
- Darwin-36B-Opus vs Qwen3-Omni-30B-A3B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-2B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-30B-A3B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-32B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-4B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-8B-Instruct
- Darwin-36B-Opus vs Qwopus3.8-27B-Flash-V2
- Darwin-36B-Opus vs Recursive Flint
- Darwin-36B-Opus vs Ripple-65B Preview
- Darwin-36B-Opus vs SenseNova-Loop-9B-Preview
- Darwin-36B-Opus vs SmolLM3-3B
- Darwin-36B-Opus vs Spark-X2.6-4B
- Darwin-36B-Opus vs Step 3.7 Flash
- Darwin-36B-Opus vs Swift 1.5 Qwen3.8-27B
- Darwin-36B-Opus vs Swift 1.5 Qwen3.8-Flash-Next
- Darwin-36B-Opus vs ThinkingCap (Qwen3.8 27B)
- Darwin-36B-Opus vs Baidu Unlimited-OCR-2
- Darwin-36B-Opus vs WeVisOne-VL-4B
- Darwin-36B-Opus vs ZAYA1-8B
- DeepSeek-V3.2 vs WeVisOne-VL-2B
- DeepSeek V4.1 Flash vs WeVisOne-VL-2B
- DeepSeek-V4-Flash-0731 vs WeVisOne-VL-2B
- Devstral Small 2 vs WeVisOne-VL-2B
- DiffusionGemma 26B A4B IT vs WeVisOne-VL-2B
- EXAONE 4.5 33B vs WeVisOne-VL-2B
- Gemma 4 12B IT vs WeVisOne-VL-2B
- Gemma 4 26B A4B IT vs WeVisOne-VL-2B
- Gemma 4 31B IT vs WeVisOne-VL-2B
- Gemma 4 E2B IT vs WeVisOne-VL-2B
- Gemma 4 E4B IT vs WeVisOne-VL-2B
- GLM-4.5-Air vs WeVisOne-VL-2B
- GLM-4.7-Flash vs WeVisOne-VL-2B
- GLM-5.3-Flash vs WeVisOne-VL-2B
- GLM-5.3 vs WeVisOne-VL-2B
- gpt-oss-120b vs WeVisOne-VL-2B
- gpt-oss-20b vs WeVisOne-VL-2B
- Granite 4.2 30B vs WeVisOne-VL-2B
- Granite 4.2 3B vs WeVisOne-VL-2B
- Granite 4.2 8B vs WeVisOne-VL-2B
- Hy-Memory-30B-A3B vs WeVisOne-VL-2B
- Hy3 vs WeVisOne-VL-2B
- Inkling-Small vs WeVisOne-VL-2B
- Inkling vs WeVisOne-VL-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B
- Kimi K3.1 Pro vs WeVisOne-VL-2B
- Kimi K3 vs WeVisOne-VL-2B
- Kolibri-1 vs WeVisOne-VL-2B
- Kronos vs WeVisOne-VL-2B
- Laguna M.1 vs WeVisOne-VL-2B
- Laguna S 2.1 vs WeVisOne-VL-2B
- Laguna XS 2.1 vs WeVisOne-VL-2B
- LBS-27B vs WeVisOne-VL-2B
- LFM2.5-2.6B vs WeVisOne-VL-2B
- LFM3-1.7B vs WeVisOne-VL-2B
- LFM3-Audio-2B vs WeVisOne-VL-2B
- LFM3-Omni-2B vs WeVisOne-VL-2B
- LFM3-VL-2B vs WeVisOne-VL-2B
- Ling-3.0-tiny vs WeVisOne-VL-2B
- Ling-3.1-flash-1019 vs WeVisOne-VL-2B
- LongCat-2.5 vs WeVisOne-VL-2B
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B
- MiniCPM-o 4.5 vs WeVisOne-VL-2B
- MiniCPM-V 4.7 1B vs WeVisOne-VL-2B
- MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B
- MiniCPM5-2B vs WeVisOne-VL-2B
- MiniMax-M2.5 vs WeVisOne-VL-2B
- MiniMax-M2.7 vs WeVisOne-VL-2B
- MiniMax-M3.1-Flash vs WeVisOne-VL-2B
- Mistral Large 4 vs WeVisOne-VL-2B
- 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
- Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-2B
- Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B
- 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
- Ornith-1.5-9B vs WeVisOne-VL-2B
- 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
Machine-readable
- This page as Markdown
/compare/darwin-36b-opus-vs-wevisone-vl-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/darwin-36b-opus.json·/api/v1/tools/wevisone-vl-2b.json - From a terminal
anchor compare darwin-36b-opus wevisone-vl-2b(the CLI) - Over MCP
compare_tools {"a": "darwin-36b-opus", "b": "wevisone-vl-2b"}at/mcp, no key