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

MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B

MiniCPM5-2B scores 59.7 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 4969 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 55
  • Agent interface, 75 against 50
  • Documentation and upkeep, 92 against 41

Watch for

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

Qwen-AgentWorld-35B-A3B D

Best for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Not a general assistant, and the card doesn't describe using it to call tools

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 +51

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.

Qwen-AgentWorld-35B-A3B 41

Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.

Reach MiniCPM5-2B +35

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.

Qwen-AgentWorld-35B-A3B 55

Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.

Agent interface MiniCPM5-2B +25

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.

Qwen-AgentWorld-35B-A3B 50

Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.

Score by category

CategoryWeight this runMiniCPM5-2BQwen-AgentWorld-35B-A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259055MiniCPM5-2B +35
Risk20%205555even
Agent interface10%107550MiniCPM5-2B +25
Documentation and upkeep10%109241MiniCPM5-2B +51
Negative events≤1500
TotalC 59.7/100D 52.1/100

Facts side by side

FactMiniCPM5-2BQwen-AgentWorld-35B-A3B
KindOpen-weight modelOpen-weight model
VendorOpenBMBAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-09-292026-06-25
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

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.

Qwen-AgentWorld-35B-A3B

A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.

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

Qwen-AgentWorld-35B-A3B

  1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation
  2. With vLLM pass --language-model-only, or startup fails on the missing vision weights
  3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends
  4. Keep at least 128K of context, since the simulation depends on the history
  5. Thinking is on by default and comes back through the qwen3 reasoning parser
  6. A simulated observation is a prediction. Check anything that matters against the real environment

Questions

Which is better for AI agents, MiniCPM5-2B or Qwen-AgentWorld-35B-A3B?

MiniCPM5-2B scores 59.7 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.

Other comparisons with MiniCPM5-2B or Qwen-AgentWorld-35B-A3B

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.