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Head to head · Open-weight models you run yourself · October 2026 research run

Qwen3.5-4B vs SmolLM3-3B

Qwen3.5-4B and SmolLM3-3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). SmolLM3-3B leads on risk, agent interface and documentation and upkeep. 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

Qwen3.5-4BAlibaba (Qwen team) · Open-weight model C61.2/100#33 of 100 open-weight models SmolLM3-3BHugging Face · Open-weight model C61/100#47 of 100 open-weight models

Which one, for what

Qwen3.5-4B C

Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

No quantised build from Alibaba for this size

SmolLM3-3B C

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 70
  • Documentation and upkeep, 54 against 34

Watch for

65,536 tokens trained; 131,072 needs a YaRN edit to config.json

Score by category

CategoryWeight this runQwen3.5-4BSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%205565SmolLM3-3B +10
Agent interface10%107080SmolLM3-3B +10
Documentation and upkeep10%103454SmolLM3-3B +20
Negative events≤1500
TotalC 61.2/100C 61/100

Facts side by side

FactQwen3.5-4BSmolLM3-3B
KindOpen-weight modelOpen-weight model
VendorAlibaba (Qwen team)Hugging Face
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-03-022025-09-10
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews3/5 (4)3.3/5 (4)

Verdicts

Qwen3.5-4B

A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.

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

Qwen3.5-4B

  1. Smallest start is ollama run qwen3.5:4b (Q4_K_M, about 4 GB with context)
  2. Full precision vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  3. Turn thinking off with "chat_template_kwargs": {"enable_thinking": false} in the request body, since it's on by default and /think and /nothink aren't supported
  4. There's no generation_config.json, so pass the card's sampling yourself, for example temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5 without thinking
  5. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
  6. Validate any JSON it returns, since structured output isn't documented

SmolLM3-3B

  1. Start it with vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text
  2. Pass chat_template_kwargs with enable_thinking false for short tasks, since thinking is on by default; a /think or /no_think flag in the system prompt overrides the kwarg
  3. Put earlier tool calls in the assistant message content, since the template ignores a tool_calls field when it renders history
  4. Add the YaRN rope_scaling block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
  5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
  6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
  7. Validate JSON output yourself or use the runtime's guided decoding

Questions

Which is better for AI agents, Qwen3.5-4B or SmolLM3-3B?

Qwen3.5-4B and SmolLM3-3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). SmolLM3-3B leads on risk, agent interface and documentation and upkeep.

Other comparisons with Qwen3.5-4B or SmolLM3-3B

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