# MiniCPM5-2B vs Qwen3-Coder-Next > MiniCPM5-2B scores 60.2 (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/minicpm5-2b-vs-qwen3-coder-next - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next.md (~6,150 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next.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 MiniCPM5-2B scores 60.2 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 3 of 5 scored categories. Qwen3-Coder-Next leads on capability. Both do open-weight models you run yourself. - MiniCPM5-2B: grade C, 60.2/100, rank #56 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json - 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 - 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 ### MiniCPM5-2B (C) Good for: On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data Ahead on: - Reach, 90 against 85 - Risk, 55 against 50 - Documentation and upkeep, 97 against 41 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### 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. Ahead on: - Capability, 42 against 27 Watch for: No LICENSE file anywhere and a broken licence link on the card ## Score by category | Category | Weight | MiniCPM5-2B | Qwen3-Coder-Next | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 27 | 42 | Qwen3-Coder-Next +15 | | Reach | 25% (25 this run) | 90 | 85 | MiniCPM5-2B +5 | | Risk | 20% (20 this run) | 55 | 50 | MiniCPM5-2B +5 | | Agent interface | 10% (10 this run) | 75 | 75 | even | | Documentation and upkeep | 10% (10 this run) | 97 | 41 | MiniCPM5-2B +56 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **57.6 · C** | | ## Facts side by side | Fact | MiniCPM5-2B | Qwen3-Coder-Next | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | 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 | 2026-09-29 | 2026-02-03 | | 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 | 3/5 (4) | 2.8/5 (4) | ## Verdicts **MiniCPM5-2B.** A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0. **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. ## Before you call either ### MiniCPM5-2B 1. Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m) 2. On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text 3. On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls 4. Tool calls are XML, ``, not Hermes JSON 5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does 6. Expect a `` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented 8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix ### 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 ## Questions ### Which is better for AI agents, MiniCPM5-2B or Qwen3-Coder-Next? MiniCPM5-2B scores 60.2 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 3 of 5 scored categories. Qwen3-Coder-Next leads on capability. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-coder-next.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "qwen3-coder-next"}`. 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