# Qwen3-Coder-Next vs SmolLM3-3B

> SmolLM3-3B scores 61 (C) to Qwen3-Coder-Next's 57.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b
- Markdown: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.md (~6,050 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.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

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.

- Qwen3-Coder-Next: grade C, 57.6/100, rank #70 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json
- SmolLM3-3B: grade C, 61/100, rank #47 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.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

### Qwen3-Coder-Next (C)

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

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

### SmolLM3-3B (C)

Good 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

| Category | Weight | Qwen3-Coder-Next | SmolLM3-3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 85 | 85 | even |
| Risk | 20% (20 this run) | 50 | 65 | SmolLM3-3B +15 |
| Agent interface | 10% (10 this run) | 75 | 80 | SmolLM3-3B +5 |
| Documentation and upkeep | 10% (10 this run) | 41 | 54 | SmolLM3-3B +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **57.6 · C** | **61 · C** | |

## Facts side by side

| Fact | Qwen3-Coder-Next | 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 | 2026-02-03 | 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 | 2.8/5 (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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-coder-next", "b": "smollm3-3b"}`. From a terminal: `anchor compare qwen3-coder-next smollm3-3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json

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

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- [Qwen3-VL-8B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.md)
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- [SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.md)

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