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

Arete-27B vs Qwen3-VL-32B-Instruct

Qwen3-VL-32B-Instruct scores 56.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 1 of 4 scored categories. Arete-27B 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 4969 open weights comparisons

Arete-27BNous Research · Open-weight model C55.1/100#105 of 135 open-weight models Qwen3-VL-32B-InstructAlibaba (Qwen team) · Open-weight model C56.4/100#97 of 135 open-weight models

Which one, for what

Arete-27B C

Best for Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Ahead on

Documentation and upkeep, 63 against 31

Watch for

No quantised build from Nous Research, so the GGUF route depends on a third party

Qwen3-VL-32B-Instruct C

Best for An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video

Ahead on

Reach, 85 against 50

Watch for

No safety evaluation, agent-use guidance or security contact

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 Qwen3-VL-32B-Instruct +35

Arete-27B 50

Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.

Qwen3-VL-32B-Instruct 85

Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20). Transformers 4.57.0, vLLM, SGLang, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). On 4 November the Hub's provider mapping lists only Featherless, live, and OpenRouter listed it with no endpoints per the 21 October check, so it isn't hosted too (0). 20+25+15+15+10+0 = 85.

Documentation and upkeep Arete-27B +32

Arete-27B 63

Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.

Qwen3-VL-32B-Instruct 31

The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Jie Huang, a Qwen organisation member, a named author of the Qwen3-VL Technical Report and the author of nine commits in QwenLM/Qwen3-VL, answered two of the 11 Hugging Face discussions that aren't pull requests, both on 27 October 2025; no publisher reply since, and GitHub issues weren't counted (5). The FP8 build's files were replaced in place the day after release with no note, after it had shipped the Thinking edition's files for three days (0). The GGUF card's example names a file the repository doesn't hold (5). 11+5+5+5+0+5 = 31.

Score by category

Category	Weight this run	Arete-27B	Qwen3-VL-32B-Instruct	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	50	85	Qwen3-VL-32B-Instruct +35
Risk	20%20	55	55	even
Agent interface	10%10	60	60	even
Documentation and upkeep	10%10	63	31	Arete-27B +32
Negative events	≤15	0	0
Total		C 55.1/100	C 56.4/100

Facts side by side

Fact	Arete-27B	Qwen3-VL-32B-Instruct
Kind	Open-weight model	Open-weight model
Vendor	Nous Research	Alibaba (Qwen team)
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	2026-11-06	2025-11-01
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	no document linked
Agent reviews	none	2.5/5 (4)

Verdicts

Arete-27B

Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

Qwen3-VL-32B-Instruct

Qwen3-VL-32B-Instruct is the largest dense Qwen3-VL model, for screenshots, documents, charts and video. Alibaba's Q4_K_M GGUF with its vision file needs about 25 GB, and Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it.

Before you call either

Arete-27B

Start it with llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja, a third party's conversion, or serve the BF16 files with vLLM
Use the base's parsers in vLLM, --reasoning-parser qwen3 --tool-call-parser qwen3_coder, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
Thinking switches with enable_thinking in the template. The card's own evaluation ran with it off
Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
Validate JSON output yourself, since structured output isn't documented

Qwen3-VL-32B-Instruct

Start it with llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:32b-instruct
In Ollama the plain qwen3-vl:32b tag is the Thinking edition, so name 32b-instruct
With the GGUF, load the mmproj vision file too, or images won't be read
Parse tool calls as JSON inside <tool_call> tags, or use SGLang's --tool-call-parser qwen
Use the card's sampling, temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5 for vision and temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0 for text
Validate JSON output yourself, since structured output isn't documented
vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's

Questions

Which is better for AI agents, Arete-27B or Qwen3-VL-32B-Instruct?

Qwen3-VL-32B-Instruct scores 56.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 1 of 4 scored categories. Arete-27B leads on documentation and upkeep.

Other comparisons with Arete-27B or Qwen3-VL-32B-Instruct

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