Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), 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
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Which one, for what
Best for Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence
Ahead on
- Reach, 90 against 55
- Agent interface, 70 against 60
- Documentation and upkeep, 68 against 26
Watch for
No limitations section, safety results, agent-use guidance or security contact
Best for Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba
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 MiniCPM-V 4.7 35B-A3B +42
MiniCPM-V 4.7 35B-A3B 68
Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.
Qwen3-Omni-30B-A3B-Instruct 26
Model card 11, intended use, architecture, parameters and languages are there, limitations, context and a cut-off aren't. Training data 5, the report's description has no cut-off. Last change 0, nothing that runs the model has changed for over a year. Responsiveness 0, Qwen answered none of the 40 most recent discussions. Version policy 5, Qwen2.5-Omni kept without notes. File health 5, contradictory licence and language metadata and an unanswered report about long audio.
Reach MiniCPM-V 4.7 35B-A3B +35
MiniCPM-V 4.7 35B-A3B 90
Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.
Qwen3-Omni-30B-A3B-Instruct 55
Memory earns 10, because the only build is the 70.5 GB BF16 checkpoint, about 85 GB with a fifth added, which fits only the reference machine's band. Runtimes earn 20. Transformers runs the whole model, and released vLLM, SGLang and llama.cpp run its Thinker with text out, while Ollama and LM Studio have nothing. Builds score 0, with no FP8, 4-bit or GGUF build from Alibaba. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no provider serves this checkpoint and Alibaba's API serves a different Flash model.
Agent interface MiniCPM-V 4.7 35B-A3B +10
MiniCPM-V 4.7 35B-A3B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.
Qwen3-Omni-30B-A3B-Instruct 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the cookbook shows how tools are declared and calls come back and the card covers audio, image and video input and speech output, but no parser is named. Trained tool calling 20, two calls in one turn from speech in the cookbook. Structured output 5, speech in a chosen voice and text-only output are documented, with no JSON. Reasoning control 0, no thinking in this edition. Context 5, 65,536 tokens in the config and trained at 32,768.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 35B-A3B | Qwen3-Omni-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 55 | MiniCPM-V 4.7 35B-A3B +35 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 60 | MiniCPM-V 4.7 35B-A3B +10 |
| Documentation and upkeep | 10%10 | 68 | 26 | MiniCPM-V 4.7 35B-A3B +42 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | E 41.8/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 35B-A3B | Qwen3-Omni-30B-A3B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-03 | 2025-09-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2/5 (4) |
Verdicts
MiniCPM-V 4.7 35B-A3B
A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.
Qwen3-Omni-30B-A3B-Instruct
An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.
Before you call either
MiniCPM-V 4.7 35B-A3B
- Start it with
llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja, orollama run openbmb/minicpm-v4.7:35b-a3b - For image input in llama.cpp add
mmproj-model-f16.gguf, or the 4x projector for more visual detail at a higher cost - The GGUF files at the repository root have thinking off. Use the files in
thinking/when you want the trace - With Transformers pass
enable_thinkingtoapply_chat_template, and passdownsample_modeto bothapply_chat_templateandgenerate - Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
- Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format
- Validate JSON output yourself, since structured output isn't documented
Qwen3-Omni-30B-A3B-Instruct
- For text answers,
mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API - The card's command passes
--allowed-local-media-path /, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path - Send audio as
audio_urland images asimage_urlcontent parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately - For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz
- Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
- Parse tool calls as JSON inside
<tool_call>tags yourself, since no parser is named, and confirm actions triggered by speech before running them - Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
- Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16
Questions
Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3-Omni-30B-A3B-Instruct?
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories.
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- Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint
- Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct
- Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet Unified EN 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0
- Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602
Machine-readable
- This page as Markdown
/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-v-4-7-35b-a3b.json·/api/v1/tools/qwen3-omni-30b-a3b-instruct.json - From a terminal
anchor compare minicpm-v-4-7-35b-a3b qwen3-omni-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "qwen3-omni-30b-a3b-instruct"}at/mcp, no key