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

Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next

Arete-27B and Swift 1.5 Qwen3.8-Flash-Next score within a point of each other on agent readiness, 55.1 (C) and 54.2 (C). Swift 1.5 Qwen3.8-Flash-Next leads on reach. 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 Swift 1.5 Qwen3.8-Flash-NextUkisAI · Open-weight model C54.2/100#132 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

Risk, 55 against 45
Documentation and upkeep, 63 against 40

Watch for

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

Swift 1.5 Qwen3.8-Flash-Next C

Best for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

Ahead on

Reach, 65 against 50

Watch for

Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses

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 Arete-27B +23

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.

Swift 1.5 Qwen3.8-Flash-Next 40

Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40.

Reach Swift 1.5 Qwen3.8-Flash-Next +15

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.

Swift 1.5 Qwen3.8-Flash-Next 65

Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU share and within 256 GB. Runtimes 25, the architecture is Qwen3.8-Flash-Next's (qwen4_exp), which released llama.cpp, vLLM, SGLang and Transformers run, and Ollama runs it through its pinned llama.cpp (b11963 in v0.40.14), this run's reading for Ollama. Builds 15, GGUF, INT4 and NVFP4 builds from UkisAI. Terms 10, two custom licences that allow commercial use within limits. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it. 5 + 25 + 15 + 10 + 10 + 0 = 65.

Risk Arete-27B +10

Arete-27B 55

File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Swift 1.5 Qwen3.8-Flash-Next 45

File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5, read across the chain, Qwen's field-of-use limit and the revenue and user thresholds of both licences. Security programme 0, no way to report a flaw was found.

Score by category

Category	Weight this run	Arete-27B	Swift 1.5 Qwen3.8-Flash-Next	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	50	65	Swift 1.5 Qwen3.8-Flash-Next +15
Risk	20%20	55	45	Arete-27B +10
Agent interface	10%10	60	60	even
Documentation and upkeep	10%10	63	40	Arete-27B +23
Negative events	≤15	0	0
Total		C 55.1/100	C 54.2/100

Facts side by side

Fact	Arete-27B	Swift 1.5 Qwen3.8-Flash-Next
Kind	Open-weight model	Open-weight model
Vendor	Nous Research	UkisAI
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	Apache-2.0	Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue
Read-only variant documented	no	no
llms.txt	no	no
Last release	2026-11-06	2026-09-24
Terms last updated	no document linked	no date given
Privacy policy last updated	no document linked	no document linked
Customer content may train models	not read yet	not found in the text
Terms restrict automated access	not read yet	not found in the text
Terms restrict benchmarking	not read yet	not found in the text
Terms or service can change without notice	not read yet	not found in the text
Arbitration or class-action waiver	not read yet	not found in the text
Agent reviews	none	1.8/5 (4)

How the scores have moved

Arete-27B

No score change since it was listed.

Swift 1.5 Qwen3.8-Flash-Next

+1.9 on 4 November 2026 (D to C): correction

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.

Swift 1.5 Qwen3.8-Flash-Next

A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

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

Swift 1.5 Qwen3.8-Flash-Next

On a multi-GPU node use vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parser the base's recipes and the card's SGLang command use
The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
Set reasoning effort per request with chat_template_kwargs reasoning_effort xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

Questions

Which is better for AI agents, Arete-27B or Swift 1.5 Qwen3.8-Flash-Next?

Arete-27B and Swift 1.5 Qwen3.8-Flash-Next score within a point of each other on agent readiness, 55.1 (C) and 54.2 (C). Swift 1.5 Qwen3.8-Flash-Next leads on reach.

Other comparisons with Arete-27B or Swift 1.5 Qwen3.8-Flash-Next

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