# MiniCPM5-2B vs MiniMax-M3.1-Flash

> MiniCPM5-2B scores 60.2 (C) to MiniMax-M3.1-Flash's 50.5 (D) 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-minimax-m3-1-flash
- Markdown: https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.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: 2026-10-25

MiniCPM5-2B scores 60.2 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability. Both do open-weight models you run yourself.

- MiniCPM5-2B: grade C, 60.2/100, rank #60 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json
- MiniMax-M3.1-Flash: grade D, 50.5/100, rank #99 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3260 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 40
- Risk, 55 against 35
- Agent interface, 75 against 70
- Documentation and upkeep, 97 against 41

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5

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

### MiniMax-M3.1-Flash (D)

Good for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.

Ahead on:
- Capability, 64 against 27

Watch for: No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did


## Score by category

| Category | Weight | MiniCPM5-2B | MiniMax-M3.1-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 64 | MiniMax-M3.1-Flash +37 |
| Reach | 25% (25 this run) | 90 | 40 | MiniCPM5-2B +50 |
| Risk | 20% (20 this run) | 55 | 35 | MiniCPM5-2B +20 |
| Agent interface | 10% (10 this run) | 75 | 70 | MiniCPM5-2B +5 |
| Documentation and upkeep | 10% (10 this run) | 97 | 41 | MiniCPM5-2B +56 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.2 · C** | **50.5 · D** | |

## Facts side by side

| Fact | MiniCPM5-2B | MiniMax-M3.1-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MiniMax Community License (custom; commercial use needs a "Built with MiniMax M3.1-Flash" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-29 | 2026-10-23 |
| 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) | 2.3/5 (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.

**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.

## 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

### MiniMax-M3.1-Flash

1. Check the use case first; commercial products must show "Built with MiniMax M3.1-Flash" and notify MiniMax once, or get written authorisation above $20M yearly revenue
2. On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144
3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code
4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128
5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95
6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off
7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage
8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort

## Questions

### Which is better for AI agents, MiniCPM5-2B or MiniMax-M3.1-Flash?

MiniCPM5-2B scores 60.2 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm5-2b", "b": "minimax-m3-1-flash"}`. From a terminal: `anchor compare minicpm5-2b minimax-m3-1-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json and https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json

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