# MiniCPM5-2B vs Qwen3-VL-8B-Instruct > MiniCPM5-2B scores 60.2 (C) to Qwen3-VL-8B-Instruct's 54.9 (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-vl-8b-instruct - Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.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-VL-8B-Instruct's 54.9 (C), and leads in 2 of 5 scored categories. Qwen3-VL-8B-Instruct 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-VL-8B-Instruct: grade C, 54.9/100, rank #82 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.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: - Agent interface, 75 against 60 - Documentation and upkeep, 97 against 31 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ### Qwen3-VL-8B-Instruct (C) Good for: An agent with 8 GB or more to spare that needs a local model to read images, documents and video Ahead on: - Reach, 100 against 90 Watch for: No safety evaluation, agent-use guidance or security contact ## Score by category | Category | Weight | MiniCPM5-2B | Qwen3-VL-8B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 27 | 28 | Qwen3-VL-8B-Instruct +1 | | Reach | 25% (25 this run) | 90 | 100 | Qwen3-VL-8B-Instruct +10 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 75 | 60 | MiniCPM5-2B +15 | | Documentation and upkeep | 10% (10 this run) | 97 | 31 | MiniCPM5-2B +66 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.2 · C** | **54.9 · C** | | ## Facts side by side | Fact | MiniCPM5-2B | Qwen3-VL-8B-Instruct | | --- | --- | --- | | 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 | 2025-11-01 | | 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-VL-8B-Instruct.** A small vision-language cousin for reading screenshots, documents, charts and video, in about 6 GB from Alibaba's Q4_K_M GGUF and every major runtime, under plain Apache 2.0. It has no thinking mode and documents tool calls only in the template. The main risk is no published safety work, so add your own guardrails before it drives a GUI. ## 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-VL-8B-Instruct 1. Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct` 2. In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct` 3. With the GGUF, load the mmproj vision file too, or images won't be read 4. Parse tool calls as JSON inside `` tags, or use SGLang's `--tool-call-parser qwen` 5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text 6. Validate JSON output yourself, since structured output isn't documented 7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later ## Questions ### Which is better for AI agents, MiniCPM5-2B or Qwen3-VL-8B-Instruct? MiniCPM5-2B scores 60.2 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "qwen3-vl-8b-instruct"}`. 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[Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp