{ "data": { "a": { "slug": "minicpm-v-4-7-1b", "name": "MiniCPM-V 4.7 1B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-V 4.7 1B is OpenBMB's small open-weight vision-language model, 1.55B parameters, that reads images and video on a phone or laptop, calls tools, and ships in BF16, 4-bit and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json", "dns": "minicpm-v-4-7-1b.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-AWQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-GPTQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-int4" }, { "registry": "ollama", "name": "openbmb/minicpm-v4.7:1b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, 3.1 GB for the BF16 file or about 1.5 GB for the IQ4_XS GGUF with its projector (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "video", "on-device", "small", "tool-calling", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 60, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "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.", "bestFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "strengths": [ "Runs in about 2 GB from OpenBMB's own GGUF with its projector", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions", "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code", "Images, several images and video in one small model, with thinking off by default", "OpenBMB publishes 4-bit builds, a GGUF repository and a speculative-decoding head itself" ], "weaknesses": [ "No limitations section, safety results, agent-use guidance or security contact", "Training data isn't described, and the card gives no context length, languages or knowledge cut-off", "The card shows a tool-calling request but not the reply, and names no parser", "Nobody has asked a question on the repository yet, so there's no record of OpenBMB answering for this size", "No inference provider is mapped on Hugging Face" ], "agentNotes": [ "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama 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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run openbmb/minicpm-v4.7:1b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-1B --port 8000 --host 0.0.0.0", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"openbmb/minicpm-v4.7:1b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-v-4-7-1b" }, "sameCompany": [ "minicpm-v-4-7-35b-a3b", "minicpm-o-4-5", "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)", "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_text with 24 layers", "params": "1.55B", "context": 262144, "goodFor": "Small vision-language model for images, several images and video on a phone or a laptop, with tool calling and thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM-V-4.7-1B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "who": "publisher", "precision": "bf16", "sizeGB": 3.11, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "MiniCPM-V-4.7-1B-AWQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-AWQ", "who": "publisher", "precision": "w4a16", "sizeGB": 2.37, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "AWQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-GPTQ", "who": "publisher", "precision": "w4a16", "sizeGB": 1.87, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-int4", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-int4", "who": "publisher", "precision": "int4", "sizeGB": 1.57, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "MiniCPM-V-4_7-1B-F16", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 3.13, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.02 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 2.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "1.08 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q6_K", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 1.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.84 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.78, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.67 GB of weights plus mmproj-model-f16.gguf (1.11 GB), or 1.57 GB with the 4x projector (0.90 GB). The same file with thinking on is in the thinking/ folder" }, { "name": "MiniCPM-V-4_7-1B-IQ4_XS", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 1.54, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.64 GB of weights plus the 0.90 GB 4x projector, the smallest at 4 bits. The IQ3 files are below the 4-bit line and are not listed" }, { "name": "ollama openbmb/minicpm-v4.7:1b", "url": "https://ollama.com/openbmb/minicpm-v4.7", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenBMB's own namespace on Ollama, not Ollama's library. 1.8 GB as the tags page shows it, with q8_0 (2.2 GB) and fp16 (3.1 GB) tags beside it" } ], "run": "llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja", "runBuild": "MiniCPM-V-4_7-1B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-V 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM-V 4.7 35B-A3B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "params": "35.2B total, 3B active (MoE; 35,212,875,824 per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/OpenBMB/MiniCPM-V", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.", "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.", "unchecked: the domain's registration date." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.json" }, "answer": "MiniCPM-V 4.7 1B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on risk, agent interface and documentation and upkeep.", "b": { "slug": "nemotron-3-5-bolt-e4b-4gb", "name": "NVIDIA Nemotron 3.5 Bolt E4B 4GB", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3.5 Bolt E4B 4GB is NVIDIA's open-weight small language model for local voice assistants, interactive characters and device agents, post-trained from Google's Gemma 4 E4B, with 8B parameters (4B effective) and a 64K-token context.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json", "dns": "nemotron-3-5-bolt-e4b-4gb.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16" }, { "registry": "ollama", "name": "nemotron-3.5-bolt:e4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB, NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 and both DFlash drafter repositories (checked 2026-10-24).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 for NVIDIA's post-training and Apache 2.0 for the Gemma 4 base. Running it costs the hardware, 6.18 GB for NVIDIA's Q4_K_M GGUF and 16.06 GB for the BF16 repository. NVIDIA's API catalogue also serves it as nvidia/nemotron-3.5-bolt-e4b, which this listing doesn't grade (checked 2026-10-24).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-24).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-03" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "gemma4", "fine-tune", "reasoning", "tool-calling", "conversational", "edge", "gguf", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-23", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 39, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.", "bestFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.", "strengths": [ "NVIDIA's Q4_K_M GGUF is 6.18 GB, and NVIDIA measured 3.5 to 5 GB of VRAM in use on RTX and Jetson", "Released support in llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio", "Trained for multi-step tool use and instruction hierarchy, with the gemma4 parser named and several calls per turn", "Thinking switches per request and can be budgeted, and conversational_mode asks for brief spoken replies", "OpenMDW-1.1 on NVIDIA's part and Apache 2.0 on the Gemma 4 base, no gate, and the RL datasets published", "DFlash drafters for llama.cpp and vLLM, which NVIDIA says speed decoding by up to 2.7 times" ], "weaknesses": [ "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway", "No safety evaluation results published", "One quantised build from NVIDIA, and its GGUF is text only with no vision or audio projector", "The card says thinking is on by default while the template defaults to off", "Independent results so far cover instruction following only (IFBench and MultiChallenge)" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K", "With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking", "With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja", "Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do", "Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says", "Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt", "Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says", "Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "provenanceScore": 76, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3.5-bolt:e4b\n# or, with llama.cpp and NVIDIA's own GGUF (the card's recipe, without Docker):\nhf download nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --local-dir bolt\nllama-server -m bolt/Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000\n# or, with vLLM 0.31.0 or later on the BF16 weights:\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 --served-model-name nemotron-bolt --max-model-len 65536 \\\n --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 \\\n --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nemotron-bolt\", \"messages\": [{\"role\": \"user\", \"content\": \"What is 17 multiplied by 23?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-5-bolt-e4b-4gb" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA (post-training), on Google's Gemma 4 E4B", "builtOn": "google/gemma-4-E4B-it (reinforcement-learning post-training, declared in the BF16 card's metadata; base Apache 2.0)", "params": "8B", "active": "4B effective", "context": 65536, "goodFor": "A small, fast cousin for voice and device assistants, game characters and robot interfaces that interleave short replies with multi-turn tool calls on a consumer GPU or a Jetson.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.18, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf, 6,180,446,208 bytes, imatrix-calibrated on about 1M tokens of RL rollouts. Text only, no vision or audio projector. Replaced on 23 October 2026 to set the context metadata to 65,536 tokens" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit", "who": "lm-studio", "precision": "4bit", "sizeGB": 4.23, "memoryGB": 8, "format": "mlx", "runsOn": [ "LM Studio" ], "note": "The smallest 4-bit build from a runtime's library, 4,231,265,397 bytes for the repository. LM Studio's catalogue page also lists its Q4_K_M, Q6_K and Q8_0 GGUFs and an 8-bit MLX build, and gives 5 GB as minimum memory" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 6.18, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "6,180,446,208 bytes, a different file from NVIDIA's by checksum. Q6_K is 6.22 GB and Q8_0 8.03 GB" }, { "name": "ollama nemotron-3.5-bolt:e4b-q4_K_M", "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 7.9, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Size as Ollama's tags page shows it. The e4b tag is 7.2 to 7.9 GB by platform, with text and image input. Ollama shows a 128K context, above NVIDIA's 64K" }, { "name": "ollama nemotron-3.5-bolt:e4b-nvfp4", "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 7.2, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama (MLX)" ], "note": "MLX build for Apple silicon with a 64K context, size as Ollama's tags page shows it. Ollama also lists e4b-mxfp8 (12 GB), e4b-q8_0 (9.8 GB) and e4b-bf16 (17 GB)" }, { "name": "ggml-org NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 8.03, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation, with a BF16 GGUF (15.05 GB) and a DFlash NVFP4 drafter GGUF. No 4-bit build there" }, { "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 16.06, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM", "Transformers", "SGLang" ], "note": "Whole repository, 15.99 GB of weights. The card intends it mainly for fine-tuning and evaluation, and its vLLM recipe sets the image, video and audio limits to 0" } ], "run": "llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000", "runBuild": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)", "checked": "2026-10-24", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/commits/main", "securityTxt": "unknown", "checked": "2026-10-24", "notes": [ "The card names NVIDIA Corporation as model developer. The files sit in the verified nvidia organisation on Hugging Face, the one developer.nvidia.com and the card's blog link to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licences stand in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repositories' commit history. Since release only the cards changed, apart from the GGUF replaced on release day to set its context to 65,536 tokens.", "www.nvidia.com/.well-known/security.txt stays unknown, as recorded for the other Nemotron listings. The card links NVIDIA's vulnerability submission page." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.json", "live": { "slug": "nemotron-3-5-bolt-e4b-4gb", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:24.848883176Z", "changedAt": "2026-11-10T16:23:24.848883176Z", "fingerprint": "7228d064ba98" } ], "updatedAt": "2026-11-10T16:23:24.848883176Z" } }, "edges": [ { "by": 25, "category": "risk", "leads": "nemotron-3-5-bolt-e4b-4gb", "name": "Risk", "reasons": { "a": "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.", "b": "File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins." }, "scores": { "a": 55, "b": 80 } }, { "by": 17, "category": "upkeep", "leads": "nemotron-3-5-bolt-e4b-4gb", "name": "Documentation and upkeep", "reasons": { "a": "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.", "b": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines." }, "scores": { "a": 48, "b": 65 } }, { "by": 15, "category": "interface", "leads": "nemotron-3-5-bolt-e4b-4gb", "name": "Agent interface", "reasons": { "a": "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.", "b": "The chat template earns 20, a Jinja file in the repository and the GGUF. The tool-calling format earns 25, with OpenAI-style tools, the gemma4 parser named and an asynchronous pattern documented. Trained tool calling earns 20, multi-step tool-use training and several calls per turn. Structured output earns 5, trained but with no instructions. Reasoning control earns 10, thinking per request, the reasoning returned apart and a budget. Context earns 5 for 64K, half the base's 128K." }, "scores": { "a": 70, "b": 85 } }, { "by": 5, "category": "reach", "leads": "minicpm-v-4-7-1b", "name": "Reach", "reasons": { "a": "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.", "b": "Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it." }, "scores": { "a": 95, "b": 90 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenBMB", "b": "NVIDIA", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-30", "b": "2026-10-23", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "4/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniCPM-V 4.7 1B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, MiniCPM-V 4.7 1B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 90" ], "also": null, "goodFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "slug": "minicpm-v-4-7-1b", "watchFor": "No limitations section, safety results, agent-use guidance or security contact" }, { "aheadOn": [ "Risk, 80 against 55", "Agent interface, 85 against 70", "Documentation and upkeep, 65 against 48" ], "also": null, "goodFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.", "slug": "nemotron-3-5-bolt-e4b-4gb", "watchFor": "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.json", "title": "Agents-A1-4B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.json", "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.json", "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.json", "title": "Arete-27B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Arete-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": 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4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b.json", "title": "Gemma 4 E2B IT vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-v-4-7-1b.json", "title": "Gemma 4 E4B IT vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": 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"https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b.json", "title": "Inkling vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-1b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": 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"title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-30b-a3b-instruct-2507.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-0-8b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-0.8B", "url": 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E4B 4GB vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-recursive-flint.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ripple-65b-preview.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-sensenova-loop-9b-preview.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-thinkingcap-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-zaya1-8b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "minicpm-v-4-7-1b", "key": "reach", "minicpm-v-4-7-1b": 95, "name": "Reach", "nemotron-3-5-bolt-e4b-4gb": 90, "weight": 25 }, { "by": 25, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "risk", "minicpm-v-4-7-1b": 55, "name": "Risk", "nemotron-3-5-bolt-e4b-4gb": 80, "weight": 20 }, { "by": 15, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "interface", "minicpm-v-4-7-1b": 70, "name": "Agent interface", "nemotron-3-5-bolt-e4b-4gb": 85, "weight": 10 }, { "by": 17, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "upkeep", "minicpm-v-4-7-1b": 48, "name": "Documentation and upkeep", "nemotron-3-5-bolt-e4b-4gb": 65, "weight": 10 } ], "summary": "MiniCPM-V 4.7 1B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "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-bolt-e4b-4gb": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb", "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb.min.md" }, "markdown": "MiniCPM-V 4.7 1B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #49 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiniCPM-V 4.7 1B (C)\n\nGood 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\n\nAhead on:\n- Reach, 95 against 90\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C)\n\nGood for: Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.\n\nAhead on:\n- Risk, 80 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 65 against 48\n\nWatch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway\n\n\n## Why the edge\n\n### Risk: NVIDIA Nemotron 3.5 Bolt E4B 4GB +25\n\n- 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.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (80): File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins.\n\n### Documentation and upkeep: NVIDIA Nemotron 3.5 Bolt E4B 4GB +17\n\n- 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.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (65): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines.\n\n### Agent interface: NVIDIA Nemotron 3.5 Bolt E4B 4GB +15\n\n- 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.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (85): The chat template earns 20, a Jinja file in the repository and the GGUF. The tool-calling format earns 25, with OpenAI-style tools, the gemma4 parser named and an asynchronous pattern documented. Trained tool calling earns 20, multi-step tool-use training and several calls per turn. Structured output earns 5, trained but with no instructions. Reasoning control earns 10, thinking per request, the reasoning returned apart and a budget. Context earns 5 for 64K, half the base's 128K.\n\n### Reach: MiniCPM-V 4.7 1B +5\n\n- 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.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (90): Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 1B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | MiniCPM-V 4.7 1B +5 |\n| Risk | 20% (20 this run) | 55 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |\n| Agent interface | 10% (10 this run) | 70 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +15 |\n| Documentation and upkeep | 10% (10 this run) | 48 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 1B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | NVIDIA |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-30 | 2026-10-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 4/5 (4) |\n\n## Verdicts\n\n**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.\n\n**NVIDIA Nemotron 3.5 Bolt E4B 4GB.** A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.\n\n## Before you call either\n\n### MiniCPM-V 4.7 1B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n5. Check tool calls in your runtime first, since the card names no parser\n6. Validate JSON output yourself, since structured output isn't documented\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K\n2. With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking\n3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja\n4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do\n5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says\n6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt\n7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says\n8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 1B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?\n\nMiniCPM-V 4.7 1B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-1b\", \"b\": \"nemotron-3-5-bolt-e4b-4gb\"}`. From a terminal: `anchor compare minicpm-v-4-7-1b nemotron-3-5-bolt-e4b-4gb`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n\n## Other comparisons with MiniCPM-V 4.7 1B or NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n- [Agents-A1-4B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.md)\n- [Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Apertus v1.5 8B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.md)\n- [Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Apodex-1.1-mini vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.md)\n- [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Arete-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [BigBang-v1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.md)\n- [BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Carbon-X vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b.md)\n- [Carbon-X vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cola2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.md)\n- [Cola2 vs NVIDIA Nemotron 3.5 Bolt E4B 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}, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "MiniCPM-V 4.7 1B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "" } ], "description": "MiniCPM-V 4.7 1B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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