Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B
MiniCPM-o 4.5 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on risk and agent interface. Both do open-weight models you run yourself.
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MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Ornith-1.5-35B-A3BOrnith · Open-weight model
C61/100#43 of 100 open-weight models
Which one, for what
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
- Documentation and upkeep, 66 against 31
Also in its favour
- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Best for Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required
Ahead on
- Risk, 50 against 40
- Agent interface, 85 against 35
Watch for
No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Ornith-1.5-35B-A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 40 | 50 | Ornith-1.5-35B-A3B +10 |
| Agent interface | 10%10 | 35 | 85 | Ornith-1.5-35B-A3B +50 |
| Documentation and upkeep | 10%10 | 66 | 31 | MiniCPM-o 4.5 +35 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Ornith-1.5-35B-A3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-16 | 2026-08-23 |
| 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 | 2.8/5 (4) | 2.3/5 (4) |
Verdicts
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.
Ornith-1.5-35B-A3B
A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.
Before you call either
MiniCPM-o 4.5
- Quickest start for text and images is
ollama run openbmb/minicpm-o4.5, about 6 GB - With vLLM 0.16.0 or later pass
--trust-remote-code, and sendstop_token_ids[151643, 151645] as OpenBMB's guide does - 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
- In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with
--mmproj, and use--jinja --reasoning-budget 0to turn thinking off - Keep prompts within 40,960 tokens
- Don't rely on tool calls or JSON output, neither is documented; validate any you get
Ornith-1.5-35B-A3B
- Start it with
ollama run ornith-1.5:35b, orllama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144as the card gives it - In vLLM pass
--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3, and in SGLang--tool-call-parser qwen3_coder --reasoning-parser qwen3, so tool calls and reasoning come back parsed - Thinking is on by default. Send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
- Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks
- Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Ornith-1.5-35B-A3B?
MiniCPM-o 4.5 and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-o-4-5.json·/api/v1/tools/ornith-1-5-35b-a3b.json - From a terminal
anchor compare minicpm-o-4-5 ornith-1-5-35b-a3b(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "ornith-1-5-35b-a3b"}at/mcp, no key