# DeepSeek V4.1 Flash vs MiniCPM5-2B > MiniCPM5-2B scores 60.2 (C) to DeepSeek V4.1 Flash's 59 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.md (~6,250 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.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 DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 5 scored categories. DeepSeek V4.1 Flash leads on capability. Both do open-weight models you run yourself. - DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json - 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 - 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 ### DeepSeek V4.1 Flash (C) Good for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on: - Capability, 69 against 27 Watch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine ### 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 50 - Risk, 55 against 50 - Documentation and upkeep, 97 against 48 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ## Score by category | Category | Weight | DeepSeek V4.1 Flash | MiniCPM5-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 69 | 27 | DeepSeek V4.1 Flash +42 | | Reach | 25% (25 this run) | 50 | 90 | MiniCPM5-2B +40 | | Risk | 20% (20 this run) | 50 | 55 | MiniCPM5-2B +5 | | Agent interface | 10% (10 this run) | 75 | 75 | even | | Documentation and upkeep | 10% (10 this run) | 48 | 97 | MiniCPM5-2B +49 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **59 · C** | **60.2 · C** | | ## Facts side by side | Fact | DeepSeek V4.1 Flash | MiniCPM5-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | OpenBMB | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-17 | 2026-09-29 | | 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) | 3/5 (4) | ## Verdicts **DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories. **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. ## Before you call either ### DeepSeek V4.1 Flash 1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive 2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command 3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding 4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4 5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75 6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends 7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update ### 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 ## Questions ### Which is better for AI agents, DeepSeek V4.1 Flash or MiniCPM5-2B? MiniCPM5-2B scores 60.2 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 3 of 5 scored categories. DeepSeek V4.1 Flash leads on capability. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "minicpm5-2b"}`. 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