Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 1B vs Nemotron 3 Nano 4B
MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on risk and documentation and upkeep. 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 a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model
Ahead on
- Reach, 95 against 90
Watch for
No limitations section, safety results, agent-use guidance or security contact
Best for Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English
Ahead on
- Risk, 65 against 55
- Documentation and upkeep, 55 against 48
Watch for
The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository
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.
Risk Nemotron 3 Nano 4B +10
MiniCPM-V 4.7 1B 55
File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Nemotron 3 Nano 4B 65
File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.
Documentation and upkeep Nemotron 3 Nano 4B +7
MiniCPM-V 4.7 1B 48
Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.
Nemotron 3 Nano 4B 55
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public.
Reach MiniCPM-V 4.7 1B +5
MiniCPM-V 4.7 1B 95
Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.
Nemotron 3 Nano 4B 90
Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 1B | Nemotron 3 Nano 4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 90 | MiniCPM-V 4.7 1B +5 |
| Risk | 20%20 | 55 | 65 | Nemotron 3 Nano 4B +10 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 48 | 55 | Nemotron 3 Nano 4B +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.8/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 1B | Nemotron 3 Nano 4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-30 | 2026-03-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.3/5 (4) |
Verdicts
MiniCPM-V 4.7 1B
A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.
Nemotron 3 Nano 4B
A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.
Before you call either
MiniCPM-V 4.7 1B
- Start it with
llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja, orollama run openbmb/minicpm-v4.7:1b - For image input in llama.cpp add
mmproj-model-f16.gguf, or the smaller 4x projector - The GGUF files at the repository root have thinking off. Use the files in
thinking/when you want the trace - 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
- Validate JSON output yourself, since structured output isn't documented
Nemotron 3 Nano 4B
- Quickest start is
ollama run nemotron-3-nano:4b, a 2.8 GB build - With llama.cpp use
llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file - With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with
--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3 - The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
- Thinking is on by default. Pass
enable_thinking=Falseto the chat template for short tasks - Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
- It's tuned for English only, and validate JSON yourself, since structured output isn't documented
Questions
Which is better for AI agents, MiniCPM-V 4.7 1B or Nemotron 3 Nano 4B?
MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on risk and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-v-4-7-1b.json·/api/v1/tools/nemotron-3-nano-4b.json - From a terminal
anchor compare minicpm-v-4-7-1b nemotron-3-nano-4b(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-1b", "b": "nemotron-3-nano-4b"}at/mcp, no key