Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-397B
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B 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 6359 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 65
- Risk, 55 against 50
- Documentation and upkeep, 68 against 36
Watch for
No limitations section, safety results, agent-use guidance or security contact
Best for A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required
Ahead on
- Agent interface, 85 against 70
Watch for
Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine
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 +32
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.
Ornith-1.5-397B 36
Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use.
Reach MiniCPM-V 4.7 35B-A3B +25
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.
Ornith-1.5-397B 65
Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it.
Agent interface Ornith-1.5-397B +15
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.
Ornith-1.5-397B 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.
Risk MiniCPM-V 4.7 35B-A3B +5
MiniCPM-V 4.7 35B-A3B 55
File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.
Ornith-1.5-397B 50
File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 35B-A3B | Ornith-1.5-397B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 65 | MiniCPM-V 4.7 35B-A3B +25 |
| Risk | 20%20 | 55 | 50 | MiniCPM-V 4.7 35B-A3B +5 |
| Agent interface | 10%10 | 70 | 85 | Ornith-1.5-397B +15 |
| Documentation and upkeep | 10%10 | 68 | 36 | MiniCPM-V 4.7 35B-A3B +32 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 59/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 35B-A3B | Ornith-1.5-397B |
|---|---|---|
| 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 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-03 | 2026-08-23 |
| 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.
Ornith-1.5-397B
Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.
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
Ornith-1.5-397B
- Start it with the card's vLLM command at
--tensor-parallel-size 8, orollama run ornith-1.5:397bon a machine with about 300 GB of memory - 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
- When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards
- Pin a revision when you download, since Ornith has replaced files in place
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Ornith-1.5-397B?
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface.
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- Ornith-1.5-397B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2
- Ornith-1.5-397B vs Recursive Flint
- Ornith-1.5-397B vs Ripple-65B Preview
- Ornith-1.5-397B vs SenseNova-Loop-9B-Preview
- Ornith-1.5-397B vs SmolLM3-3B
- Ornith-1.5-397B vs Spark-X2.6-4B
- Ornith-1.5-397B vs Step 3.7 Flash
- Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next
- Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-397B vs Baidu Unlimited-OCR-2
- Ornith-1.5-397B vs WeVisOne-VL-2B
- Ornith-1.5-397B vs WeVisOne-VL-4B
- Ornith-1.5-397B vs ZAYA1-8B
Machine-readable
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/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-397b.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/ornith-1-5-397b.json - From a terminal
anchor compare minicpm-v-4-7-35b-a3b ornith-1-5-397b(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "ornith-1-5-397b"}at/mcp, no key