# MiniCPM5-2B vs Qwen3-4B-Instruct-2507

> MiniCPM5-2B scores 60.2 (C) to Qwen3-4B-Instruct-2507's 55.7 (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-qwen3-4b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.md (~6,300 tokens)
- Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.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 Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 1 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach. 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
- Qwen3-4B-Instruct-2507: grade C, 55.7/100, rank #77 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.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:
- Documentation and upkeep, 97 against 26

Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning

### Qwen3-4B-Instruct-2507 (C)

Good for: Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on:
- Reach, 95 against 90

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | MiniCPM5-2B | Qwen3-4B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 31 | Qwen3-4B-Instruct-2507 +4 |
| Reach | 25% (25 this run) | 90 | 95 | Qwen3-4B-Instruct-2507 +5 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 75 | 75 | even |
| Documentation and upkeep | 10% (10 this run) | 97 | 26 | MiniCPM5-2B +71 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.2 · C** | **55.7 · C** | |

## Facts side by side

| Fact | MiniCPM5-2B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| 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 | 2025-09-17 |
| 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) | 3/5 (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.

**Qwen3-4B-Instruct-2507.** A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba 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, `<function name=...><param name=...>`, 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 `<think>` 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

### Qwen3-4B-Instruct-2507

1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, MiniCPM5-2B or Qwen3-4B-Instruct-2507?

MiniCPM5-2B scores 60.2 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 1 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "qwen3-4b-instruct-2507"}`. From a terminal: `anchor compare minicpm5-2b qwen3-4b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json

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