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

Cola2 vs MiniCPM-o 4.5

MiniCPM-o 4.5 scores 61 (C) on agent readiness against Cola2's 33.9 (F), 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 6359 open weights comparisons

Best conversational voice-agent APIs · All 581 voice agents comparisons

Which one, for what

Cola2 F

Best for Researchers working on latent or diffusion language models who want a checkpoint to study

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

Watch for

No licence stated for the weights

MiniCPM-o 4.5 C

Best for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.

Ahead on

  • Reach, 90 against 25
  • Agent interface, 35 against 20
  • Documentation and upkeep, 66 against 58

Watch for

Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni

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.

Reach MiniCPM-o 4.5 +65

Cola2 25

Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false 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 nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.

MiniCPM-o 4.5 90

Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it.

Agent interface MiniCPM-o 4.5 +15

Cola2 20

Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.

MiniCPM-o 4.5 35

Chat template 20, a Jinja template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code.

Documentation and upkeep MiniCPM-o 4.5 +8

Cola2 58

Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.

MiniCPM-o 4.5 66

Model card 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies.

Score by category

CategoryWeight this runCola2MiniCPM-o 4.5Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%252590MiniCPM-o 4.5 +65
Risk20%204040even
Agent interface10%102035MiniCPM-o 4.5 +15
Documentation and upkeep10%105866MiniCPM-o 4.5 +8
Negative events≤1500
TotalF 33.9/100C 61/100

Facts side by side

FactCola2MiniCPM-o 4.5
KindOpen-weight modelOpen-weight model
VendorByteDance (Seed)OpenBMB
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNone stated for the weights (the code is Apache 2.0)Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards
Read-only variant documentednono
llms.txtnono
Last release2026-11-062026-10-24
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.8/5 (4)

Verdicts

Cola2

A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.

MiniCPM-o 4.5

A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.

Before you call either

Cola2

  1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions
  2. Don't use it commercially on the strength of this listing. The weights have no stated licence
  3. If you study it, read modeling_cola2.py first: trust_remote_code runs it
  4. Pin Transformers below 5, as the card requires
  5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding
  6. Check the download with sha256sum -c SHA256SUMS

MiniCPM-o 4.5

  1. Quickest start for text and images is ollama run openbmb/minicpm-o4.5, about 6 GB
  2. With vLLM 0.16.0 or later pass --trust-remote-code, and send stop_token_ids [151643, 151645] as OpenBMB's guide does
  3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni
  4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with --mmproj, and use --jinja --reasoning-budget 0 to turn thinking off
  5. Keep prompts within 40,960 tokens
  6. Don't rely on tool calls or JSON output, neither is documented; validate any you get

Questions

Which is better for AI agents, Cola2 or MiniCPM-o 4.5?

MiniCPM-o 4.5 scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories.

Other comparisons with Cola2 or MiniCPM-o 4.5

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.