# gpt-oss-20b vs Qwen3.6-35B-A3B

> gpt-oss-20b scores 69.9 (B) to Qwen3.6-35B-A3B's 63.2 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b
- Markdown: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

gpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 4 of 5 scored categories. Qwen3.6-35B-A3B leads on capability. Both do open-weight models you run yourself.

- gpt-oss-20b: grade B, 69.9/100, rank #8 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-20b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json
- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### gpt-oss-20b (B)

Good 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 85
- Risk, 85 against 55
- Agent interface, 90 against 70
- Documentation and upkeep, 42 against 36

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

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

### Qwen3.6-35B-A3B (B)

Good for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.

Ahead on:
- Capability, 58 against 49

Watch for: No safety evaluation, no agent-use guidance and no way to report a security flaw


## Score by category

| Category | Weight | gpt-oss-20b | Qwen3.6-35B-A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 49 | 58 | Qwen3.6-35B-A3B +9 |
| Reach | 25% (25 this run) | 90 | 85 | gpt-oss-20b +5 |
| Risk | 20% (20 this run) | 85 | 55 | gpt-oss-20b +30 |
| Agent interface | 10% (10 this run) | 90 | 70 | gpt-oss-20b +20 |
| Documentation and upkeep | 10% (10 this run) | 42 | 36 | gpt-oss-20b +6 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **69.9 · B** | **63.2 · B** | |

## Facts side by side

| Fact | gpt-oss-20b | Qwen3.6-35B-A3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenAI | Alibaba (Qwen team) |
| 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 | 2025-08-26 | 2026-04-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no 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 reviews | 3.5/5 (4) | 2.8/5 (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.

**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

## 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

### Qwen3.6-35B-A3B

1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

## Questions

### Which is better for AI agents, gpt-oss-20b or Qwen3.6-35B-A3B?

gpt-oss-20b scores 69.9 (B) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 4 of 5 scored categories. Qwen3.6-35B-A3B leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-20b", "b": "qwen3-6-35b-a3b"}`. From a terminal: `anchor compare gpt-oss-20b qwen3-6-35b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json

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