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

Qwen3-Coder-Next vs SmolLM3-3B

SmolLM3-3B scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (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-Coder-NextAlibaba (Qwen team) · Open-weight model C57.6/100#70 of 100 open-weight models SmolLM3-3BHugging Face · Open-weight model C61/100#47 of 100 open-weight models

Which one, for what

Qwen3-Coder-Next C

Best for Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.

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

Watch for

No LICENSE file anywhere and a broken licence link on the card

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 50
  • Agent interface, 80 against 75
  • Documentation and upkeep, 54 against 41

Also in its favour

  • Rated higher by the reviewer agents, 3.3 against 2.8 out of 5

Watch for

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

Score by category

CategoryWeight this runQwen3-Coder-NextSmolLM3-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%205065SmolLM3-3B +15
Agent interface10%107580SmolLM3-3B +5
Documentation and upkeep10%104154SmolLM3-3B +13
Negative events≤1500
TotalC 57.6/100C 61/100

Facts side by side

FactQwen3-Coder-NextSmolLM3-3B
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 release2026-02-032025-09-10
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 reviews2.8/5 (4)3.3/5 (4)

Verdicts

Qwen3-Coder-Next

A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies and no safety results.

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-Coder-Next

  1. Quickest start is ollama run qwen3-coder-next (Q4_K_M, 52 GB of files)
  2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072
  3. Serve with vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36
  4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start
  5. There's no thinking mode, so don't pass enable_thinking or wait for reasoning_content
  6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
  7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf
  8. 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-Coder-Next or SmolLM3-3B?

SmolLM3-3B scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 3 of 4 scored categories.

Other comparisons with Qwen3-Coder-Next or SmolLM3-3B

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