Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct
Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 0 of 4 scored categories. Qwen3-VL-8B-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
Qwen3-VL-2B-InstructAlibaba (Qwen team) · Open-weight model
C61/100#45 of 100 open-weight models
Qwen3-VL-8B-InstructAlibaba (Qwen team) · Open-weight model
C54.9/100#82 of 100 open-weight models
Which one, for what
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
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety evaluation, agent-use guidance or security contact
Best 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 95
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Qwen3-VL-2B-Instruct | Qwen3-VL-8B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 100 | Qwen3-VL-8B-Instruct +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 31 | 31 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.9/100 |
Facts side by side
| Fact | Qwen3-VL-2B-Instruct | Qwen3-VL-8B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | 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 | 2025-11-01 | 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 | 2.8/5 (4) | 2.8/5 (4) |
Verdicts
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.
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
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
Qwen3-VL-8B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:8b-instruct - In Ollama the plain
qwen3-vl:8btag is the Thinking edition, so name8b-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, Qwen3-VL-2B-Instruct or Qwen3-VL-8B-Instruct?
Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 0 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.
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Machine-readable
- This page as Markdown
/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-8b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-vl-2b-instruct.json·/api/v1/tools/qwen3-vl-8b-instruct.json - From a terminal
anchor compare qwen3-vl-2b-instruct qwen3-vl-8b-instruct(the CLI) - Over MCP
compare_tools {"a": "qwen3-vl-2b-instruct", "b": "qwen3-vl-8b-instruct"}at/mcp, no key