Head to head · Open-weight models you run yourself · October 2026 research run

MiniCPM5-2B vs Nanbeige4.2-3B

Nanbeige4.2-3B scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 1 of 4 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

MiniCPM5-2B C

Best 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 85
  • Documentation and upkeep, 92 against 72

Watch for

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

Nanbeige4.2-3B C

Best for Local agents that need tool calling and a long context on a laptop or a small GPU

Ahead on

  • Agent interface, 85 against 75

Watch for

Its own architecture: Transformers loads it only with the repository's code

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Documentation and upkeep MiniCPM5-2B +20

MiniCPM5-2B 92

Model card 17, everything but a knowledge cut-off. Training data 20, all eight training datasets are published on Hugging Face and named per stage with sizes. Last change 15, a template fix on 29 September 2026, 36 days ago, with nothing newer in OpenBMB's MiniCPM5 repositories on Hugging Face (builds last changed 12 September) or its GitHub repository (21 September). Responsiveness 20, OpenBMB members answered 16 of the 23 questions in the discussions, the newest reply on 29 September. Version policy 10, earlier models and checkpoints sit in their own repositories and the GitHub changelog dates each release. File health 10, config, tokenizer, template and card agree and no broken-file report waited on 21 October, though OpenBMB's GGUF, MLX and GPTQ repositories still carry the template from before the 29 September fix.

Nanbeige4.2-3B 72

Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.

Agent interface Nanbeige4.2-3B +10

MiniCPM5-2B 75

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card and OpenBMB's cookbooks document the XML call format, the tools request and the minicpm5 parser in SGLang and vLLM. Trained tool calling 20, agent SFT on 500K samples and a template that renders several calls in one turn. Structured output 0, nothing documents asking for JSON. Reasoning control 0, a judgement call, because OpenBMB's own cookbooks call Think the only supported mode for the 2B even though the template accepts enable_thinking false. Context 10, 131,072 tokens native with no rope scaling.

Nanbeige4.2-3B 85

Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Reach MiniCPM5-2B +5

MiniCPM5-2B 90

Memory 30, OpenBMB's own MLX 4-bit build is 1.42 GB and its Q4_K_M GGUF 1.56 GB, about 2 GB with the fifth added. Runtimes 25, the cap, since the model is a stock Llama and vLLM, SGLang, released Transformers, a released llama.cpp and Ollama all run it. Builds 15, OpenBMB ships GGUF at three precisions plus MLX and GPTQ 4-bit. Terms 10, the LICENSE is plain Apache 2.0 but the card prohibits jailbreaking and injection testing and limits use to ethical purposes, which reads as a use policy on top (dated 21 October 2026). Download 10, nothing is gated. Hosted 0, OpenBMB has no API for it, no inference provider is listed on its Hugging Face page and OpenRouter doesn't carry it.

Nanbeige4.2-3B 85

Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.

Score by category

CategoryWeight this runMiniCPM5-2BNanbeige4.2-3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259085MiniCPM5-2B +5
Risk20%205555even
Agent interface10%107585Nanbeige4.2-3B +10
Documentation and upkeep10%109272MiniCPM5-2B +20
Negative events≤1500
TotalC 59.7/100C 61/100

Facts side by side

FactMiniCPM5-2BNanbeige4.2-3B
KindOpen-weight modelOpen-weight model
VendorOpenBMBNanbeige LLM Lab (BOSS Zhipin)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0 (Hub metadata only; the repository has no LICENSE file)
Read-only variant documentednono
llms.txtnono
Last release2026-09-292026-09-11
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3/5 (4)none

How the scores have moved

MiniCPM5-2B

Nanbeige4.2-3B

No score change since it was listed.

Verdicts

MiniCPM5-2B

A 2B dense model for local tool calling and long context, with published training data. OpenBMB's own 4-bit builds are under 1.6 GB, and the model loads in vLLM, SGLang, llama.cpp, Ollama and Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.

Nanbeige4.2-3B

A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.

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

Nanbeige4.2-3B

  1. Serve it with SGLang 0.5.23 or later and pass --tool-call-parser nanbeige --reasoning-parser nanbeige
  2. Pass tools in the request. The card recommends tool_call_format="xml"
  3. Set preserve_thinking true for multi-turn tool use and coding, false for plain chat
  4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card
  5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's
  6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it

Questions

Which is better for AI agents, MiniCPM5-2B or Nanbeige4.2-3B?

Nanbeige4.2-3B scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 1 of 4 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep.

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