Head to head · Open-weight models you run yourself · October 2026 research run
Arete-27B vs gpt-oss-20b
gpt-oss-20b scores 69.9 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 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
Which one, for what
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 42
Watch for
No quantised build from Nous Research, so the GGUF route depends on a third party
Best for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time
Ahead on
- Reach, 90 against 50
- Risk, 85 against 55
- Agent interface, 90 against 60
Watch for
No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered
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 gpt-oss-20b +40
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.
gpt-oss-20b 90
Memory earns 25, since OpenAI's own MXFP4 build is 13.76 GB and a fifth on top is 16.5 GB, just past the 16 GB band, and Ollama's 14 GB build lands in the same 24 GB band. Runtimes earn the full 25 from model code in released Transformers, vLLM and SGLang, the architecture in llama.cpp and the model in Ollama's library. Builds earn 10, because OpenAI ships one quantised precision, MXFP4, and the original and metal folders are the same weights for its reference code. Terms earn 15 for Apache 2.0, whose usage policy only asks users to follow the law. Download earns 10, since the repository isn't gated. Hosted earns 5, with five inference providers live for it on its Hugging Face page on 1 November 2026, while OpenAI's own API lists it with no usable rate limit and isn't counted.
Risk gpt-oss-20b +30
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.
gpt-oss-20b 85
File safety earns 25, since the weights are safetensors and metal/model.bin is OpenAI's own raw Metal format rather than pickle, and nothing in the repository runs as code (a judgement call on the .bin name). Provenance earns 15, files in OpenAI's own organisation and a checksum on every file. Safety evaluation earns 10, because the published safety results for this size are OpenAI's own, and the outside review covered gpt-oss-120b. Agent guidance earns 15 for an instruction hierarchy the model was trained to follow, with prompt-injection results, though OpenAI reports it trails o4-mini there. Licence risk keeps 15, with nothing to deduct under Apache 2.0. The security programme earns 5 for OpenAI's security.txt with a Bugcrowd contact. Public advisories for the gpt-oss code couldn't be checked, so that half scores as absent.
Agent interface gpt-oss-20b +30
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.
gpt-oss-20b 90
The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since OpenAI's harmony guide says how functions are declared and how calls come back, and OpenAI ships the openai-harmony parser. Trained tool calling earns 10, because each call ends the message and waits for its result, so it makes one call at a time. Structured output earns 15 for a JSON Schema given in the developer message, documented in the harmony guide. Reasoning control earns 10, with low, medium or high effort per request and the reasoning on its own channel, though it can't be switched off. Context earns 10 for 131,072 tokens, which OpenAI describes as trained in even though the config reaches it with YaRN.
Documentation and upkeep Arete-27B +21
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.
gpt-oss-20b 42
The model card earns 17, covering six of the seven points and missing a statement of supported languages. Training data earns 10, a general description with a June 2024 cut-off and no sources or mix. Last change earns 5, because the files haven't changed since August 2025 and the reference code and harmony parser last changed on 1 and 5 November 2025, still within a year on 1 November 2026, while this year's commits only touch the browser tool, CI and a version number. Responsiveness earns 0, with no OpenAI reply to any of the 40 most recent discussions and none at all since August 2025. Version policy earns 5 for a first release kept in its own repository. File health earns 5, since template reports and an install report wait without a reply.
Score by category
| Category | Weight this run | Arete-27B | gpt-oss-20b | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 90 | gpt-oss-20b +40 |
| Risk | 20%20 | 55 | 85 | gpt-oss-20b +30 |
| Agent interface | 10%10 | 60 | 90 | gpt-oss-20b +30 |
| Documentation and upkeep | 10%10 | 63 | 42 | Arete-27B +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 55.1/100 | B 69.9/100 |
Facts side by side
| Fact | Arete-27B | gpt-oss-20b |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | OpenAI |
| 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 | 2025-08-26 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.5/5 (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.
gpt-oss-20b
gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.
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_thinkingin 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
gpt-oss-20b
- Start it with
ollama run gpt-oss:20b(the build that fits the reference machine), orvllm serve openai/gpt-oss-20bwith a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel - Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
- Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in
<|call|>, reasoning on the analysis channel - Set effort with
Reasoning: high(or low, medium) in the system message, orreasoning_effortin the template; medium is the default - Ask for JSON by putting a JSON Schema under
# Response Formatsin the developer message - Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
- Expect one tool call per turn; run several in sequence rather than in parallel
Questions
Which is better for AI agents, Arete-27B or gpt-oss-20b?
gpt-oss-20b scores 69.9 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Arete-27B leads on documentation and upkeep.
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Machine-readable
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/api/v1/tools/arete-27b.json·/api/v1/tools/gpt-oss-20b.json - From a terminal
anchor compare arete-27b gpt-oss-20b(the CLI) - Over MCP
compare_tools {"a": "arete-27b", "b": "gpt-oss-20b"}at/mcp, no key