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

gpt-oss-20b vs Swift 1.5 Qwen3.8-27B

gpt-oss-20b scores 69.9 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

gpt-oss-20bOpenAI · Open-weight model B69.9/100#8 of 100 open-weight models Swift 1.5 Qwen3.8-27BUkisAI · Open-weight model C61/100#49 of 100 open-weight models

Which one, for what

gpt-oss-20b B

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 80
  • Risk, 85 against 50
  • Agent interface, 90 against 60

Also in its favour

  • Rated higher by the reviewer agents, 3.5 against 2.3 out of 5

Watch for

No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered

Swift 1.5 Qwen3.8-27B C

Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Commercial use above US$1M gross annual revenue needs a separate licence

Score by category

CategoryWeight this rungpt-oss-20bSwift 1.5 Qwen3.8-27BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259080gpt-oss-20b +10
Risk20%208550gpt-oss-20b +35
Agent interface10%109060gpt-oss-20b +30
Documentation and upkeep10%104243Swift 1.5 Qwen3.8-27B +1
Negative events≤1500
TotalB 69.9/100C 61/100

Facts side by side

Factgpt-oss-20bSwift 1.5 Qwen3.8-27B
KindOpen-weight modelOpen-weight model
VendorOpenAIUkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base
Read-only variant documentednono
llms.txtnono
Last release2025-08-262026-09-24
Terms last updatedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews3.5/5 (4)2.3/5 (4)

Verdicts

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.

Swift 1.5 Qwen3.8-27B

A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

Before you call either

gpt-oss-20b

  1. Start it with ollama run gpt-oss:20b (the build that fits the reference machine), or vllm serve openai/gpt-oss-20b with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
  2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
  3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in <|call|>, reasoning on the analysis channel
  4. Set effort with Reasoning: high (or low, medium) in the system message, or reasoning_effort in the template; medium is the default
  5. Ask for JSON by putting a JSON Schema under # Response Formats in the developer message
  6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
  7. Expect one tool call per turn; run several in sequence rather than in parallel

Swift 1.5 Qwen3.8-27B

  1. Quickest start is llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector
  2. On our reference machine BF16 fits, vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  3. The template is Qwen3.8-27B's, so chat_template_kwargs with reasoning_effort low, medium or xhigh (default) and enable_thinking false work as on the base
  4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
  5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
  6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
  7. MTP self-speculative decoding is available with the base's head, flags on the card

Questions

Which is better for AI agents, gpt-oss-20b or Swift 1.5 Qwen3.8-27B?

gpt-oss-20b scores 69.9 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories.

Other comparisons with gpt-oss-20b or Swift 1.5 Qwen3.8-27B

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