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

Arete-27B vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 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 6359 open weights comparisons

Arete-27BNous Research · Open-weight model C55.1/100#123 of 160 open-weight models Qwen3.5-122B-A10BAlibaba (Qwen team) · Open-weight model B63.1/100#25 of 160 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 34

Watch for

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

Qwen3.5-122B-A10B B

Best for Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.

Ahead on

  • Reach, 80 against 50
  • Agent interface, 70 against 60

Watch for

Too big for a single 64 GB machine even at 4 bits

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 Qwen3.5-122B-A10B +30

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.

Qwen3.5-122B-A10B 80

Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5.

Documentation and upkeep Arete-27B +29

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.

Qwen3.5-122B-A10B 34

The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. No Qwen member has answered any of the 21 discussions that aren't pull requests, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and a template fix and a run report still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.

Agent interface Qwen3.5-122B-A10B +10

Arete-27B 60

Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.

Qwen3.5-122B-A10B 70

The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.

Score by category

CategoryWeight this runArete-27BQwen3.5-122B-A10BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255080Qwen3.5-122B-A10B +30
Risk20%205555even
Agent interface10%106070Qwen3.5-122B-A10B +10
Documentation and upkeep10%106334Arete-27B +29
Negative events≤1500
TotalC 55.1/100B 63.1/100

Facts side by side

FactArete-27BQwen3.5-122B-A10B
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 release2026-11-062026-02-24
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

Arete-27B

No score change since it was listed.

Qwen3.5-122B-A10B

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.

Qwen3.5-122B-A10B

A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.

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

Qwen3.5-122B-A10B

  1. Quickest start on a 128 GB machine is ollama run qwen3.5:122b (Q4_K_M, 81 GB of files, about 97 GB with context)
  2. Alibaba's 4-bit build serves with vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16, though that kernel isn't documented for AMD gfx1151
  3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do
  4. Turn thinking off with "chat_template_kwargs": {"enable_thinking": false} in the request body, since it's on by default and /think and /nothink aren't supported
  5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
  6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
  7. Add --language-model-only in vLLM to skip the vision encoder and free memory for the KV cache when you only need text
  8. Validate any JSON it returns, since structured output isn't documented

Questions

Which is better for AI agents, Arete-27B or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep.

Other comparisons with Arete-27B or Qwen3.5-122B-A10B

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