Head to head · Open-weight models you run yourself · October 2026 research run
gpt-oss-120b vs Qwen3-VL-2B-Instruct
gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3-VL-2B-Instruct's 61 (C), and leads in 3 of 4 scored categories. Qwen3-VL-2B-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-VL-2B-InstructAlibaba (Qwen team) · Open-weight model
C61/100#45 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
- Risk, 95 against 55
- Agent interface, 90 against 60
- 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
Best for An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB
Ahead on
- Reach, 95 against 75
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | gpt-oss-120b | Qwen3-VL-2B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 95 | Qwen3-VL-2B-Instruct +20 |
| Risk | 20%20 | 95 | 55 | gpt-oss-120b +40 |
| Agent interface | 10%10 | 90 | 60 | gpt-oss-120b +30 |
| Documentation and upkeep | 10%10 | 45 | 31 | gpt-oss-120b +14 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 70.2/100 | C 61/100 |
Facts side by side
| Fact | gpt-oss-120b | Qwen3-VL-2B-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 | 2025-11-01 |
| 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-VL-2B-Instruct
The smallest Qwen3-VL cousin, for reading screenshots, documents, charts and video on a laptop or edge box, running in about 2 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. Qwen hasn't answered repository questions or published safety work, so keep a human or a check in the loop before it drives a GUI.
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-VL-2B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-2B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:2b-instruct - In Ollama the plain
qwen3-vl:2btag is the Thinking edition, so name2b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
Questions
Which is better for AI agents, gpt-oss-120b or Qwen3-VL-2B-Instruct?
gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3-VL-2B-Instruct's 61 (C), and leads in 3 of 4 scored categories. Qwen3-VL-2B-Instruct leads on reach.
Other comparisons with gpt-oss-120b or Qwen3-VL-2B-Instruct
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- Gemma 4 26B A4B IT vs gpt-oss-120b
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- Gemma 4 E2B IT vs gpt-oss-120b
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- GLM-4.5-Air vs gpt-oss-120b
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- GLM-4.7-Flash vs gpt-oss-120b
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- GLM-5.3-Flash vs gpt-oss-120b
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- GLM-5.3 vs gpt-oss-120b
- GLM-5.3 vs Qwen3-VL-2B-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
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- gpt-oss-120b vs Kolibri-1
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- gpt-oss-120b vs Laguna S 2.1
- gpt-oss-120b vs Laguna XS 2.1
- gpt-oss-120b vs LFM2.5-2.6B
- gpt-oss-120b vs Ling-3.0-tiny
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- Qwen3-VL-2B-Instruct vs SmolLM3-3B
- Qwen3-VL-2B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
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Machine-readable
- This page as Markdown
/compare/gpt-oss-120b-vs-qwen3-vl-2b-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-vl-2b-instruct.json - From a terminal
anchor compare gpt-oss-120b qwen3-vl-2b-instruct(the CLI) - Over MCP
compare_tools {"a": "gpt-oss-120b", "b": "qwen3-vl-2b-instruct"}at/mcp, no key