Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 1B vs Nemotron 3.5 Lightning 30B A3B
Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach. 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 85
Watch for
No limitations section, safety results, agent-use guidance or security contact
Nemotron 3.5 Lightning 30B A3B B
Best for Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence
Ahead on
- Risk, 75 against 55
- Agent interface, 90 against 70
- Documentation and upkeep, 75 against 48
Watch for
No safety evaluation results published
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 Nemotron 3.5 Lightning 30B A3B +27
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 3.5 Lightning 30B A3B 75
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code as for the other Nemotron listings. The repositories last changed on 10 September, 53 days ago, and TensorRT-LLM, which the card's serving section runs, changed its Nemotron-H model code on 14 October, 19 days ago. Responsiveness earns 5, one NVIDIA reply across 14 questions. Version policy earns 5 as a first release. File health earns 5, since reports about the card's recipes wait without a reply and the llama.cpp recipe names a quantisation the linked repository doesn't hold.
Risk Nemotron 3.5 Lightning 30B A3B +20
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 3.5 Lightning 30B A3B 75
File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.
Agent interface Nemotron 3.5 Lightning 30B A3B +20
MiniCPM-V 4.7 1B 70
Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.
Nemotron 3.5 Lightning 30B A3B 90
The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens.
Reach MiniCPM-V 4.7 1B +10
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 3.5 Lightning 30B A3B 85
Memory earns 25, since ggml-org's 18.9 GB Q4_0 GGUF and Ollama's 19 GB nvfp4 build both need under 24 GB with the fifth added. Runtimes earn the full 25 from released vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is NVFP4. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and Fireworks hosts it.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 1B | Nemotron 3.5 Lightning 30B A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 85 | MiniCPM-V 4.7 1B +10 |
| Risk | 20%20 | 55 | 75 | Nemotron 3.5 Lightning 30B A3B +20 |
| Agent interface | 10%10 | 70 | 90 | Nemotron 3.5 Lightning 30B A3B +20 |
| Documentation and upkeep | 10%10 | 48 | 75 | Nemotron 3.5 Lightning 30B A3B +27 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 64.7/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 1B | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|
| 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 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-30 | 2026-08-11 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
MiniCPM-V 4.7 1B
No score change since it was listed.
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 3.5 Lightning 30B A3B
A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.
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 3.5 Lightning 30B A3B
- Quickest start is
ollama run nemotron-3.5-lightning, about 19 to 25 GB of files depending on the platform - On the reference machine use
llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs - With vLLM serve it with
--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder(SGLang uses--reasoning-parser nemotron_3) - Use
temperature=1.0andtop_p=0.95, as the card says - Thinking is on by default. Pass
chat_template_kwargswithenable_thinkingfalse for short tasks - For coding agents pass
force_nonempty_content: trueinchat_template_kwargs, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content - Go past 262,144 tokens only with the card's flags (
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1,--max-model-len 1048576) and enough memory
Questions
Which is better for AI agents, MiniCPM-V 4.7 1B or Nemotron 3.5 Lightning 30B A3B?
Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach.
Other comparisons with MiniCPM-V 4.7 1B or Nemotron 3.5 Lightning 30B A3B
- Agents-A1-4B vs MiniCPM-V 4.7 1B
- Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B
- Apertus v1.5 8B vs MiniCPM-V 4.7 1B
- Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B
- Apodex-1.1-mini vs MiniCPM-V 4.7 1B
- Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B A3B
- Arete-27B vs MiniCPM-V 4.7 1B
- Arete-27B vs Nemotron 3.5 Lightning 30B A3B
- BigBang-v1 vs MiniCPM-V 4.7 1B
- BigBang-v1 vs Nemotron 3.5 Lightning 30B A3B
- Carbon-X vs MiniCPM-V 4.7 1B
- Carbon-X vs Nemotron 3.5 Lightning 30B A3B
- Cola2 vs MiniCPM-V 4.7 1B
- Cola2 vs Nemotron 3.5 Lightning 30B A3B
- Cosmos-Reason2-2B vs MiniCPM-V 4.7 1B
- Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B
- Darwin-36B-Opus vs MiniCPM-V 4.7 1B
- Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs MiniCPM-V 4.7 1B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek V4.1 Flash vs MiniCPM-V 4.7 1B
- DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 1B
- DeepSeek-V4-Flash-0731 vs Nemotron 3.5 Lightning 30B A3B
- Devstral Small 2 vs MiniCPM-V 4.7 1B
- Devstral Small 2 vs Nemotron 3.5 Lightning 30B A3B
- DiffusionGemma 26B A4B IT vs MiniCPM-V 4.7 1B
- DiffusionGemma 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B
- EXAONE 4.5 33B vs MiniCPM-V 4.7 1B
- EXAONE 4.5 33B vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 12B IT vs MiniCPM-V 4.7 1B
- Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 26B A4B IT vs MiniCPM-V 4.7 1B
- Gemma 4 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 31B IT vs MiniCPM-V 4.7 1B
- Gemma 4 31B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 E2B IT vs MiniCPM-V 4.7 1B
- Gemma 4 E2B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 E4B IT vs MiniCPM-V 4.7 1B
- Gemma 4 E4B IT vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.5-Air vs MiniCPM-V 4.7 1B
- GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.7-Flash vs MiniCPM-V 4.7 1B
- GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B
- GLM-5.3-Flash vs MiniCPM-V 4.7 1B
- GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B
- GLM-5.3 vs MiniCPM-V 4.7 1B
- GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B
- gpt-oss-120b vs MiniCPM-V 4.7 1B
- gpt-oss-120b vs Nemotron 3.5 Lightning 30B A3B
- gpt-oss-20b vs MiniCPM-V 4.7 1B
- gpt-oss-20b vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 30B vs MiniCPM-V 4.7 1B
- Granite 4.2 30B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 3B vs MiniCPM-V 4.7 1B
- Granite 4.2 3B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 8B vs MiniCPM-V 4.7 1B
- Granite 4.2 8B vs Nemotron 3.5 Lightning 30B A3B
- Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B
- Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B
- Hy3 vs MiniCPM-V 4.7 1B
- Hy3 vs Nemotron 3.5 Lightning 30B A3B
- Inkling-Small vs MiniCPM-V 4.7 1B
- Inkling-Small vs Nemotron 3.5 Lightning 30B A3B
- Inkling vs MiniCPM-V 4.7 1B
- Inkling vs Nemotron 3.5 Lightning 30B A3B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3.5 Lightning 30B A3B
- Kimi K3.1 Pro vs MiniCPM-V 4.7 1B
- Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B
- Kimi K3 vs MiniCPM-V 4.7 1B
- Kimi K3 vs Nemotron 3.5 Lightning 30B A3B
- Kolibri-1 vs MiniCPM-V 4.7 1B
- Kolibri-1 vs Nemotron 3.5 Lightning 30B A3B
- Kronos vs MiniCPM-V 4.7 1B
- Kronos vs Nemotron 3.5 Lightning 30B A3B
- Laguna M.1 vs MiniCPM-V 4.7 1B
- Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna S 2.1 vs MiniCPM-V 4.7 1B
- Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna XS 2.1 vs MiniCPM-V 4.7 1B
- Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B
- LBS-27B vs MiniCPM-V 4.7 1B
- LBS-27B vs Nemotron 3.5 Lightning 30B A3B
- LFM2.5-2.6B vs MiniCPM-V 4.7 1B
- LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-1.7B vs MiniCPM-V 4.7 1B
- LFM3-1.7B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-Audio-2B vs MiniCPM-V 4.7 1B
- LFM3-Audio-2B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-Omni-2B vs MiniCPM-V 4.7 1B
- LFM3-Omni-2B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-VL-2B vs MiniCPM-V 4.7 1B
- LFM3-VL-2B vs Nemotron 3.5 Lightning 30B A3B
- Ling-3.0-tiny vs MiniCPM-V 4.7 1B
- Ling-3.0-tiny vs Nemotron 3.5 Lightning 30B A3B
- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 1B
- Ling-3.1-flash-1019 vs Nemotron 3.5 Lightning 30B A3B
- LongCat-2.5 vs MiniCPM-V 4.7 1B
- LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 1B
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3.5 Lightning 30B A3B
- MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B
- MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM-o 4.5 vs MiniCPM-V 4.7 1B
- MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM-V 4.7 1B vs MiniCPM-V 4.7 35B-A3B
- MiniCPM-V 4.7 1B vs MiniCPM5-2B
- MiniCPM-V 4.7 1B vs MiniMax-M2.5
- MiniCPM-V 4.7 1B vs MiniMax-M2.7
- MiniCPM-V 4.7 1B vs MiniMax-M3.1-Flash
- MiniCPM-V 4.7 1B vs Mistral Large 4
- MiniCPM-V 4.7 1B vs Muse Glimmer 30B
- MiniCPM-V 4.7 1B vs Nanbeige4.2-3B
- MiniCPM-V 4.7 1B vs Nanbeige4.3-33B
- MiniCPM-V 4.7 1B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano 30B A3B
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano 4B
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiniCPM-V 4.7 1B vs Nemotron 3 Super 120B A12B
- MiniCPM-V 4.7 1B vs Nemotron 3 Ultra 550B A55B
- MiniCPM-V 4.7 1B vs North Mini Code 1.0
- MiniCPM-V 4.7 1B vs occamy-1.0
- MiniCPM-V 4.7 1B vs Olmo 3 7B Think
- MiniCPM-V 4.7 1B vs Ornith-1.5-122B
- MiniCPM-V 4.7 1B vs Ornith-1.5-35B-A3B
- MiniCPM-V 4.7 1B vs Ornith-1.5-397B
- MiniCPM-V 4.7 1B vs Ornith-1.5-9B
- MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT
- MiniCPM-V 4.7 1B vs Qwen-AgentWorld-35B-A3B
- MiniCPM-V 4.7 1B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-V 4.7 1B vs Qwen3-4B-Instruct-2507
- MiniCPM-V 4.7 1B vs Qwen3.5-0.8B
- MiniCPM-V 4.7 1B vs Qwen3.5-122B-A10B
- MiniCPM-V 4.7 1B vs Qwen3.5-2B
- MiniCPM-V 4.7 1B vs Qwen3.5-397B-A17B
- MiniCPM-V 4.7 1B vs Qwen3.5-4B
- MiniCPM-V 4.7 1B vs Qwen3.5-9B
- MiniCPM-V 4.7 1B vs Qwen3.6-27B
- MiniCPM-V 4.7 1B vs Qwen3.6-35B-A3B
- MiniCPM-V 4.7 1B vs Qwen3.8-27B
- MiniCPM-V 4.7 1B vs Qwen3.8-Flash-Next
- MiniCPM-V 4.7 1B vs Qwen3-Coder-30B-A3B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-Coder-Next
- MiniCPM-V 4.7 1B vs Qwen3-Omni-30B-A3B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-2B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-32B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-4B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 1B vs Qwopus3.8-27B-Flash-V2
- MiniCPM-V 4.7 1B vs Recursive Flint
- MiniCPM-V 4.7 1B vs Ripple-65B Preview
- MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview
- MiniCPM-V 4.7 1B vs SmolLM3-3B
- MiniCPM-V 4.7 1B vs Spark-X2.6-4B
- MiniCPM-V 4.7 1B vs Step 3.7 Flash
- MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B
- MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next
- MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2
- MiniCPM-V 4.7 1B vs WeVisOne-VL-2B
- MiniCPM-V 4.7 1B vs WeVisOne-VL-4B
- MiniCPM-V 4.7 1B vs ZAYA1-8B
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM5-2B vs Nemotron 3.5 Lightning 30B A3B
- MiniMax-M2.5 vs Nemotron 3.5 Lightning 30B A3B
- MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B
- MiniMax-M3.1-Flash vs Nemotron 3.5 Lightning 30B A3B
- Mistral Large 4 vs Nemotron 3.5 Lightning 30B A3B
- Muse Glimmer 30B vs Nemotron 3.5 Lightning 30B A3B
- Nanbeige4.2-3B vs Nemotron 3.5 Lightning 30B A3B
- Nanbeige4.3-33B vs Nemotron 3.5 Lightning 30B A3B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3.5 Lightning 30B A3B
- Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 30B A3B
- Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 4B
- Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Super 120B A12B
- Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Ultra 550B A55B
- Nemotron 3.5 Lightning 30B A3B vs North Mini Code 1.0
- Nemotron 3.5 Lightning 30B A3B vs occamy-1.0
- Nemotron 3.5 Lightning 30B A3B vs Olmo 3 7B Think
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-122B
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B
- Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-4B-Instruct-2507
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-0.8B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-122B-A10B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-2B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-397B-A17B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-4B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-9B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-27B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-27B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-Flash-Next
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-30B-A3B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-Next
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-2B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-32B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwopus3.8-27B-Flash-V2
- Nemotron 3.5 Lightning 30B A3B vs Recursive Flint
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview
- Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B
- Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B
- Nemotron 3.5 Lightning 30B A3B vs Step 3.7 Flash
- Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B
- Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-lightning-30b-a3b.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-3-5-lightning-30b-a3b.json - From a terminal
anchor compare minicpm-v-4-7-1b nemotron-3-5-lightning-30b-a3b(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-1b", "b": "nemotron-3-5-lightning-30b-a3b"}at/mcp, no key