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

Arete-27B vs Qwen3-VL-8B-Instruct

Arete-27B and Qwen3-VL-8B-Instruct score within a point of each other on agent readiness, 55.1 (C) and 54.9 (C). Qwen3-VL-8B-Instruct leads on reach. 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-8B-InstructAlibaba (Qwen team) · Open-weight model C54.9/100#106 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-8B-Instruct C

Best for An agent with 8 GB or more to spare that needs a local model to read images, documents and video

Ahead on

  • Reach, 100 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-8B-Instruct +50

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-8B-Instruct 100

Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach 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). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.

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-8B-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 merged on 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). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.

Score by category

CategoryWeight this runArete-27BQwen3-VL-8B-InstructEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%2550100Qwen3-VL-8B-Instruct +50
Risk20%205555even
Agent interface10%106060even
Documentation and upkeep10%106331Arete-27B +32
Negative events≤1500
TotalC 55.1/100C 54.9/100

Facts side by side

FactArete-27BQwen3-VL-8B-Instruct
KindOpen-weight modelOpen-weight model
VendorNous ResearchAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-062025-11-01
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.8/5 (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-8B-Instruct

Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.

Before you call either

Arete-27B

  1. 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
  2. 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
  3. Thinking switches with enable_thinking in the template. The card's own evaluation ran with it off
  4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
  5. Validate JSON output yourself, since structured output isn't documented

Qwen3-VL-8B-Instruct

  1. Start it with llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:8b-instruct
  2. In Ollama the plain qwen3-vl:8b tag is the Thinking edition, so name 8b-instruct
  3. With the GGUF, load the mmproj vision file too, or images won't be read
  4. Parse tool calls as JSON inside <tool_call> tags, or use SGLang's --tool-call-parser qwen
  5. 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
  6. Validate JSON output yourself, since structured output isn't documented
  7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

Questions

Which is better for AI agents, Arete-27B or Qwen3-VL-8B-Instruct?

Arete-27B and Qwen3-VL-8B-Instruct score within a point of each other on agent readiness, 55.1 (C) and 54.9 (C). Qwen3-VL-8B-Instruct leads on reach.

Other comparisons with Arete-27B or Qwen3-VL-8B-Instruct

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