Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3-VL-32B-Instruct vs SmolLM3-3B
SmolLM3-3B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 3 of 4 scored categories. 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-32B-InstructAlibaba (Qwen team) · Open-weight model
C56.4/100#75 of 100 open-weight models
SmolLM3-3BHugging Face · Open-weight model
C61/100#47 of 100 open-weight models
Which one, for what
Best for An agent with a 32 GB GPU or more that wants the strongest dense Qwen3-VL model for images, documents and video
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 Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device
Ahead on
- Risk, 65 against 55
- Agent interface, 80 against 60
- Documentation and upkeep, 54 against 31
Also in its favour
- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5
Watch for
65,536 tokens trained; 131,072 needs a YaRN edit to config.json
Score by category
| Category | Weight this run | Qwen3-VL-32B-Instruct | SmolLM3-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 55 | 65 | SmolLM3-3B +10 |
| Agent interface | 10%10 | 60 | 80 | SmolLM3-3B +20 |
| Documentation and upkeep | 10%10 | 31 | 54 | SmolLM3-3B +23 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.4/100 | C 61/100 |
Facts side by side
| Fact | Qwen3-VL-32B-Instruct | SmolLM3-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Hugging Face |
| 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 | 2025-09-10 |
| 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 | 2.5/5 (4) | 3.3/5 (4) |
Verdicts
Qwen3-VL-32B-Instruct
The largest dense Qwen3-VL cousin, for harder work on screenshots, documents, charts and video, needing about 25 GB from Alibaba's Q4_K_M GGUF with its vision file, in every major runtime under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it, so plan to run it yourself.
SmolLM3-3B
A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.
Before you call either
Qwen3-VL-32B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:32b-instruct - In Ollama the plain
qwen3-vl:32btag is the Thinking edition, so name32b-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
- If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's
SmolLM3-3B
- Start it with
vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text - Pass
chat_template_kwargswithenable_thinkingfalse for short tasks, since thinking is on by default; a/thinkor/no_thinkflag in the system prompt overrides the kwarg - Put earlier tool calls in the assistant message content, since the template ignores a
tool_callsfield when it renders history - Add the YaRN
rope_scalingblock (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens - Use temperature 0.6 and top_p 0.95, the card's recommended sampling
- Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
- Validate JSON output yourself or use the runtime's guided decoding
Questions
Which is better for AI agents, Qwen3-VL-32B-Instruct or SmolLM3-3B?
SmolLM3-3B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 3 of 4 scored categories.
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Machine-readable
- This page as Markdown
/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-vl-32b-instruct.json·/api/v1/tools/smollm3-3b.json - From a terminal
anchor compare qwen3-vl-32b-instruct smollm3-3b(the CLI) - Over MCP
compare_tools {"a": "qwen3-vl-32b-instruct", "b": "smollm3-3b"}at/mcp, no key