# gpt-oss-120b vs Qwen3-VL-8B-Instruct

> gpt-oss-120b scores 70.2 (BB) to Qwen3-VL-8B-Instruct's 54.9 (C) 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-120b-vs-qwen3-vl-8b-instruct
- Markdown: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.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-120b scores 70.2 (BB) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 4 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach. Both do open-weight models you run yourself.

- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json
- Qwen3-VL-8B-Instruct: grade C, 54.9/100, rank #82 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.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-120b (BB)

Good 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 28
- 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

### Qwen3-VL-8B-Instruct (C)

Good for: An agent with 8 GB or more to spare that needs a local model to read images, documents and video

Ahead on:
- Reach, 100 against 75

Watch for: No safety evaluation, agent-use guidance or security contact


## Score by category

| Category | Weight | gpt-oss-120b | Qwen3-VL-8B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 28 | gpt-oss-120b +26 |
| Reach | 25% (25 this run) | 75 | 100 | Qwen3-VL-8B-Instruct +25 |
| Risk | 20% (20 this run) | 95 | 55 | gpt-oss-120b +40 |
| Agent interface | 10% (10 this run) | 90 | 60 | gpt-oss-120b +30 |
| Documentation and upkeep | 10% (10 this run) | 45 | 31 | gpt-oss-120b +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **70.2 · BB** | **54.9 · C** | |

## Facts side by side

| Fact | gpt-oss-120b | Qwen3-VL-8B-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-8B-Instruct.** A small vision-language cousin for reading screenshots, documents, charts and video, in about 6 GB from Alibaba's Q4_K_M GGUF and every major runtime, under plain Apache 2.0. It has no thinking mode and documents tool calls only in the template. The main risk is no published safety work, so add your own guardrails before it drives a GUI.

## Before you call either

### gpt-oss-120b

1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits
2. 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
3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony
4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
5. Set `Reasoning: low` in the system message for short tasks, medium is the default
6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
7. Expect one tool call per turn and loop, don't wait for parallel calls

### Qwen3-VL-8B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct`
2. In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct`
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside `<tool_call>` tags, or use SGLang's `--tool-call-parser qwen`
5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text
6. Validate JSON output yourself, since structured output isn't documented
7. 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-8B-Instruct?

gpt-oss-120b scores 70.2 (BB) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 4 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-120b", "b": "qwen3-vl-8b-instruct"}`. From a terminal: `anchor compare gpt-oss-120b qwen3-vl-8b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json

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- [MiniCPM5-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.md)
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- [MiniMax-M2.7 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.md)
- [Muse Glimmer 30B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.md)
- [Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct.md)
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- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-8b-instruct.md)
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- [Olmo 3 7B Think vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.md)
- [Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct.md)
- [Ornith-1.5-397B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.md)
- [Ornith-1.5-9B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-8b-instruct.md)
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- [Qwen3.6-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-8b-instruct.md)
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- [Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3-VL-8B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-8B-Instruct vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b.md)
- [Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b.md)
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- [Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.md)

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