Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 35B-A3B vs Ripple-65B Preview
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface. 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
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 45
- Documentation and upkeep, 68 against 49
Watch for
No limitations section, safety results, agent-use guidance or security contact
Best for Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.
Ahead on
- Agent interface, 75 against 70
Watch for
No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote 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.
Reach MiniCPM-V 4.7 35B-A3B +45
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.
Ripple-65B Preview 45
Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.
Documentation and upkeep MiniCPM-V 4.7 35B-A3B +19
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.
Ripple-65B Preview 49
Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.
Agent interface Ripple-65B Preview +5
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.
Ripple-65B Preview 75
Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 35B-A3B | Ripple-65B Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 45 | MiniCPM-V 4.7 35B-A3B +45 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 75 | Ripple-65B Preview +5 |
| Documentation and upkeep | 10%10 | 68 | 49 | MiniCPM-V 4.7 35B-A3B +19 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 53.3/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 35B-A3B | Ripple-65B Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | Ricursive Intelligence |
| 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 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
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.
Ripple-65B Preview
A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.
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
Ripple-65B Preview
- Run Ricursive's launcher from inference/server with
uv run --script serve.pyon two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default - The launcher turns on
--reasoning-parser rippleand--tool-call-parser ripple; without them tool calls come back as raw text inside tool_call tags - For
tool_choice: "required"setstrict: trueandadditionalProperties: falseon each function, which the README says stopped extra argument fields - Leave room in
max_tokensfor reasoning as well as the answer, since thinking is always on - Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support
Questions
Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Ripple-65B Preview?
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.
Other comparisons with MiniCPM-V 4.7 35B-A3B or Ripple-65B Preview
- Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B
- Agents-A1-4B vs Ripple-65B Preview
- Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B
- Apertus v1.5 8B vs Ripple-65B Preview
- Apodex-1.1-mini vs MiniCPM-V 4.7 35B-A3B
- Apodex-1.1-mini vs Ripple-65B Preview
- Arete-27B vs MiniCPM-V 4.7 35B-A3B
- Arete-27B vs Ripple-65B Preview
- BigBang-v1 vs MiniCPM-V 4.7 35B-A3B
- BigBang-v1 vs Ripple-65B Preview
- Carbon-X vs MiniCPM-V 4.7 35B-A3B
- Carbon-X vs Ripple-65B Preview
- Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B
- Cosmos-Reason2-2B vs Ripple-65B Preview
- Darwin-36B-Opus vs MiniCPM-V 4.7 35B-A3B
- Darwin-36B-Opus vs Ripple-65B Preview
- DeepSeek-V3.2 vs MiniCPM-V 4.7 35B-A3B
- DeepSeek-V3.2 vs Ripple-65B Preview
- DeepSeek V4.1 Flash vs MiniCPM-V 4.7 35B-A3B
- DeepSeek V4.1 Flash vs Ripple-65B Preview
- DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 35B-A3B
- DeepSeek-V4-Flash-0731 vs Ripple-65B Preview
- DiffusionGemma 26B A4B IT vs MiniCPM-V 4.7 35B-A3B
- DiffusionGemma 26B A4B IT vs Ripple-65B Preview
- Gemma 4 12B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 12B IT vs Ripple-65B Preview
- Gemma 4 26B A4B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 26B A4B IT vs Ripple-65B Preview
- Gemma 4 31B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 31B IT vs Ripple-65B Preview
- Gemma 4 E2B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 E2B IT vs Ripple-65B Preview
- Gemma 4 E4B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 E4B IT vs Ripple-65B Preview
- GLM-4.5-Air vs MiniCPM-V 4.7 35B-A3B
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.7-Flash vs MiniCPM-V 4.7 35B-A3B
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-5.3-Flash vs MiniCPM-V 4.7 35B-A3B
- GLM-5.3-Flash vs Ripple-65B Preview
- GLM-5.3 vs MiniCPM-V 4.7 35B-A3B
- GLM-5.3 vs Ripple-65B Preview
- gpt-oss-120b vs MiniCPM-V 4.7 35B-A3B
- gpt-oss-120b vs Ripple-65B Preview
- gpt-oss-20b vs MiniCPM-V 4.7 35B-A3B
- gpt-oss-20b vs Ripple-65B Preview
- Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 30B vs Ripple-65B Preview
- Granite 4.2 3B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 3B vs Ripple-65B Preview
- Granite 4.2 8B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 8B vs Ripple-65B Preview
- Hy-Memory-30B-A3B vs MiniCPM-V 4.7 35B-A3B
- Hy-Memory-30B-A3B vs Ripple-65B Preview
- Hy3 vs MiniCPM-V 4.7 35B-A3B
- Hy3 vs Ripple-65B Preview
- Inkling-Small vs MiniCPM-V 4.7 35B-A3B
- Inkling-Small vs Ripple-65B Preview
- Inkling vs MiniCPM-V 4.7 35B-A3B
- Inkling vs Ripple-65B Preview
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 35B-A3B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview
- Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B
- Kimi K3.1 Pro vs Ripple-65B Preview
- Kimi K3 vs MiniCPM-V 4.7 35B-A3B
- Kimi K3 vs Ripple-65B Preview
- Kolibri-1 vs MiniCPM-V 4.7 35B-A3B
- Kolibri-1 vs Ripple-65B Preview
- Kronos vs MiniCPM-V 4.7 35B-A3B
- Kronos vs Ripple-65B Preview
- Laguna M.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna M.1 vs Ripple-65B Preview
- Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna XS 2.1 vs Ripple-65B Preview
- LBS-27B vs MiniCPM-V 4.7 35B-A3B
- LBS-27B vs Ripple-65B Preview
- LFM2.5-2.6B vs MiniCPM-V 4.7 35B-A3B
- LFM2.5-2.6B vs Ripple-65B Preview
- LFM3-1.7B vs MiniCPM-V 4.7 35B-A3B
- LFM3-1.7B vs Ripple-65B Preview
- LFM3-Audio-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-Audio-2B vs Ripple-65B Preview
- LFM3-Omni-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-Omni-2B vs Ripple-65B Preview
- LFM3-VL-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-VL-2B vs Ripple-65B Preview
- Ling-3.0-tiny vs MiniCPM-V 4.7 35B-A3B
- Ling-3.0-tiny vs Ripple-65B Preview
- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 35B-A3B
- Ling-3.1-flash-1019 vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiniCPM-o 4.5 vs MiniCPM-V 4.7 35B-A3B
- MiniCPM-o 4.5 vs Ripple-65B Preview
- MiniCPM-V 4.7 1B vs MiniCPM-V 4.7 35B-A3B
- MiniCPM-V 4.7 1B vs Ripple-65B Preview
- MiniCPM-V 4.7 35B-A3B vs MiniCPM5-2B
- MiniCPM-V 4.7 35B-A3B vs MiniMax-M2.5
- MiniCPM-V 4.7 35B-A3B vs MiniMax-M2.7
- MiniCPM-V 4.7 35B-A3B vs MiniMax-M3.1-Flash
- MiniCPM-V 4.7 35B-A3B vs Mistral Large 4
- MiniCPM-V 4.7 35B-A3B vs Muse Glimmer 30B
- MiniCPM-V 4.7 35B-A3B vs Nanbeige4.3-33B
- MiniCPM-V 4.7 35B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano 30B A3B
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano 4B
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Super 120B A12B
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Ultra 550B A55B
- MiniCPM-V 4.7 35B-A3B vs occamy-1.0
- MiniCPM-V 4.7 35B-A3B vs Olmo 3 7B Think
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-35B-A3B
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-397B
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-9B
- MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B
- MiniCPM-V 4.7 35B-A3B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-V 4.7 35B-A3B vs Qwen3-4B-Instruct-2507
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-0.8B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-122B-A10B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-2B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-397B-A17B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-4B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-9B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.6-27B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.6-35B-A3B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.8-27B
- MiniCPM-V 4.7 35B-A3B vs Qwen3.8-Flash-Next
- MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-Next
- MiniCPM-V 4.7 35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-2B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-32B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-4B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 35B-A3B vs Qwopus3.8-27B-Flash-V2
- MiniCPM-V 4.7 35B-A3B vs Recursive Flint
- MiniCPM-V 4.7 35B-A3B vs SmolLM3-3B
- MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B
- MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-27B
- MiniCPM-V 4.7 35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- MiniCPM-V 4.7 35B-A3B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM-V 4.7 35B-A3B vs Baidu Unlimited-OCR-2
- MiniCPM5-2B vs Ripple-65B Preview
- MiniMax-M2.5 vs Ripple-65B Preview
- MiniMax-M2.7 vs Ripple-65B Preview
- MiniMax-M3.1-Flash vs Ripple-65B Preview
- Mistral Large 4 vs Ripple-65B Preview
- Muse Glimmer 30B vs Ripple-65B Preview
- Nanbeige4.3-33B vs Ripple-65B Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 4B vs Ripple-65B Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview
- Nemotron 3 Super 120B A12B vs Ripple-65B Preview
- Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview
- occamy-1.0 vs Ripple-65B Preview
- Olmo 3 7B Think vs Ripple-65B Preview
- Ornith-1.5-122B vs Ripple-65B Preview
- Ornith-1.5-35B-A3B vs Ripple-65B Preview
- Ornith-1.5-397B vs Ripple-65B Preview
- Ornith-1.5-9B vs Ripple-65B Preview
- Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview
- Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview
- Qwen3-4B-Instruct-2507 vs Ripple-65B Preview
- Qwen3.5-0.8B vs Ripple-65B Preview
- Qwen3.5-122B-A10B vs Ripple-65B Preview
- Qwen3.5-2B vs Ripple-65B Preview
- Qwen3.5-397B-A17B vs Ripple-65B Preview
- Qwen3.5-4B vs Ripple-65B Preview
- Qwen3.5-9B vs Ripple-65B Preview
- Qwen3.6-27B vs Ripple-65B Preview
- Qwen3.6-35B-A3B vs Ripple-65B Preview
- Qwen3.8-27B vs Ripple-65B Preview
- Qwen3.8-Flash-Next vs Ripple-65B Preview
- Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Coder-Next vs Ripple-65B Preview
- Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-2B-Instruct vs Ripple-65B Preview
- Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-32B-Instruct vs Ripple-65B Preview
- Qwen3-VL-4B-Instruct vs Ripple-65B Preview
- Qwen3-VL-8B-Instruct vs Ripple-65B Preview
- Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview
- Recursive Flint vs Ripple-65B Preview
- Ripple-65B Preview vs SmolLM3-3B
- Ripple-65B Preview vs Spark-X2.6-4B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next
- Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)
- Ripple-65B Preview vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/minicpm-v-4-7-35b-a3b-vs-ripple-65b-preview.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/ripple-65b-preview.json - From a terminal
anchor compare minicpm-v-4-7-35b-a3b ripple-65b-preview(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "ripple-65b-preview"}at/mcp, no key