Head to head · Open-weight models you run yourself · October 2026 research run
gpt-oss-120b vs Qwen3-Coder-30B-A3B-Instruct
gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 4 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach. 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-120bOpenAI · Open-weight model
BB70.2/100#6 of 100 open-weight models
Qwen3-Coder-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C55.7/100#78 of 100 open-weight models
Which one, for what
gpt-oss-120b BB
Best for Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support
Ahead on
- Capability, 54 against 33
- Risk, 95 against 55
- Agent interface, 90 against 75
- Documentation and upkeep, 45 against 31
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5
Watch for
Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision
Qwen3-Coder-30B-A3B-Instruct C
Best for Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.
Ahead on
- Reach, 90 against 75
Watch for
No safety results, prompt-injection guidance or security contact, though it's meant to run commands
Score by category
| Category | Weight this run | gpt-oss-120b | Qwen3-Coder-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 54 | 33 | gpt-oss-120b +21 |
| Reach | 25%25 | 75 | 90 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Risk | 20%20 | 95 | 55 | gpt-oss-120b +40 |
| Agent interface | 10%10 | 90 | 75 | gpt-oss-120b +15 |
| Documentation and upkeep | 10%10 | 45 | 31 | gpt-oss-120b +14 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 70.2/100 | C 55.7/100 |
Facts side by side
| Fact | gpt-oss-120b | Qwen3-Coder-30B-A3B-Instruct |
|---|---|---|
| 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 | 2025-12-03 |
| 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-120b
A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.
Qwen3-Coder-30B-A3B-Instruct
A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.
Before you call either
gpt-oss-120b
- Start it with
ollama run gpt-oss:120bon the reference machine, the build that fits - On the Strix Halo machine raise the GPU memory limit first with
amd-ttm --set 100, since the default of about 62.8 GiB is below the 78 GB it needs - With vLLM use
vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choiceper vLLM's recipe, or parse raw output with openai-harmony - Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
- Set
Reasoning: lowin the system message for short tasks, medium is the default - Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
- Expect one tool call per turn and loop, don't wait for parallel calls
Qwen3-Coder-30B-A3B-Instruct
- Quickest start is
ollama run qwen3-coder:30b, 19 GB - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder, 61 GB of weights fits the GPU share - Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
- Pass tool-call arguments in the history as objects, the template iterates them as a mapping
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
- For code completion use the fill-in-the-middle tokens
<|fim_prefix|>,<|fim_suffix|>and<|fim_middle|>from Qwen's README - Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection
Questions
Which is better for AI agents, gpt-oss-120b or Qwen3-Coder-30B-A3B-Instruct?
gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 4 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach.
Other comparisons with gpt-oss-120b or Qwen3-Coder-30B-A3B-Instruct
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- Gemma 4 12B IT vs gpt-oss-120b
- Gemma 4 12B IT vs Qwen3-Coder-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs gpt-oss-120b
- Gemma 4 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct
- Gemma 4 31B IT vs gpt-oss-120b
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- Gemma 4 E2B IT vs gpt-oss-120b
- Gemma 4 E2B IT vs Qwen3-Coder-30B-A3B-Instruct
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- GLM-4.5-Air vs gpt-oss-120b
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- GLM-4.7-Flash vs gpt-oss-120b
- GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct
- GLM-5.3-Flash vs gpt-oss-120b
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- GLM-5.3 vs gpt-oss-120b
- GLM-5.3 vs Qwen3-Coder-30B-A3B-Instruct
- gpt-oss-120b vs gpt-oss-20b
- gpt-oss-120b vs Granite 4.2 3B
- gpt-oss-120b vs Hy3
- gpt-oss-120b vs Inkling
- gpt-oss-120b vs Inkling-Small
- gpt-oss-120b vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- gpt-oss-120b vs Kimi K3
- gpt-oss-120b vs Kolibri-1
- gpt-oss-120b vs Laguna M.1
- gpt-oss-120b vs Laguna S 2.1
- gpt-oss-120b vs Laguna XS 2.1
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- gpt-oss-120b vs Qwen3.5-0.8B
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- gpt-oss-120b vs Qwen3.5-2B
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- Qwen3.5-4B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Coder-Next
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- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
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- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B
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- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
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Machine-readable
- This page as Markdown
/compare/gpt-oss-120b-vs-qwen3-coder-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gpt-oss-120b.json·/api/v1/tools/qwen3-coder-30b-a3b-instruct.json - From a terminal
anchor compare gpt-oss-120b qwen3-coder-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "gpt-oss-120b", "b": "qwen3-coder-30b-a3b-instruct"}at/mcp, no key