# DeepSeek-V4-Flash-0731 vs MiniCPM5-2B > DeepSeek-V4-Flash-0731 scores 62.4 (B) to MiniCPM5-2B's 60.2 (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-flash-0731-vs-minicpm5-2b - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm5-2b.md (~6,500 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm5-2b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-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 DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself. - DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.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-Flash-0731 (B) Good for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context Ahead on: - Capability, 66 against 27 Watch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB 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 70 - Risk, 55 against 50 - Documentation and upkeep, 97 against 43 Also in its favour: - Rated higher by the reviewer agents, 3.0 against 2.5 out of 5 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ## Score by category | Category | Weight | DeepSeek-V4-Flash-0731 | MiniCPM5-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 66 | 27 | DeepSeek-V4-Flash-0731 +39 | | Reach | 25% (25 this run) | 70 | 90 | MiniCPM5-2B +20 | | 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) | 43 | 97 | MiniCPM5-2B +54 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **62.4 · B** | **60.2 · C** | | ## Facts side by side | Fact | DeepSeek-V4-Flash-0731 | 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 | 2026-07-31 | 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 | 2.5/5 (4) | 3/5 (4) | ## Verdicts **DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository. **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-Flash-0731 1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{"use_fp4_indexer_cache": true}' --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe) 2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template 3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string="true" and JSON values string="false" 4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max 5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash 6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory ### 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-Flash-0731 or MiniCPM5-2B? DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 1 of 5 scored categories. MiniCPM5-2B leads on reach, risk and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm5-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-flash-0731", "b": "minicpm5-2b"}`. 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