{
  "data": {
    "a": {
      "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"
      }
    },
    "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.",
    "b": {
      "slug": "otel-2-0-llm-31b-it",
      "name": "OTel-2.0-LLM-31B-IT",
      "vendor": "AT\u0026T (Open Telco AI)",
      "vendorUrl": "https://github.com/farbodtavakkoli/OTel",
      "kind": "weights",
      "category": "open-weights",
      "summary": "OTel-2.0-LLM-31B-IT is AT\u0026T's open-weight telecoms model, Gemma 4 31B IT post-trained on about 440 billion telecom tokens from GSMA's corpus of standards, for RAG and question answering over 3GPP, ETSI and other specifications, published in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json",
      "dns": "otel-2-0-llm-31b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "farbodtavakkoli/OTel-2.0-LLM-31B-IT"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata gives it, with Google's Gemma 4 licence page as the link. Running it yourself costs the hardware, 62.6 GB in BF16, which the card puts on two 80 GB GPUs or one of 192 GB (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/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "fine-tune",
        "telecom",
        "domain-specific",
        "rag",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-11-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.8,
        "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": 83,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 74
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.",
        "bestFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware",
        "strengths": [
          "The card states what the training covered and left out, and what hasn't been validated",
          "Training sources and sizes are named, with GSMA as the provider of the corpus",
          "Apache 2.0, ungated, safetensors only, no code to run",
          "A full vLLM serving guide with sizing, stop tokens and troubleshooting",
          "The owner answers questions on the repository"
        ],
        "weaknesses": [
          "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked",
          "The files are in a personal account, not an AT\u0026T or GSMA organisation",
          "Weights replaced in place three times, and an old shard set left in the repository",
          "No tool-call format, structured output or thinking",
          "Post-trained at 4,096 tokens, so long-context use is unproven",
          "No safety results and no security contact"
        ],
        "agentNotes": [
          "Download the 15 shards the index names, not the whole repository, which is twice the size",
          "Pin a revision. The card says the weights may change under the same name",
          "Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's",
          "Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel",
          "Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review",
          "Don't use it to run network tools unattended. The card says that hasn't been evaluated"
        ],
        "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": 56.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 74
        },
        "provenanceScore": 61,
        "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": "# with vLLM 0.19.0 or later, on two 80 GB GPUs, per the card:\nvllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --served-model-name OTel-2.0-LLM-31B-IT \\\n  --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16 --port 8000",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"OTel-2.0-LLM-31B-IT\", \"messages\": [{\"role\": \"user\", \"content\": \"What model is this?\"}], \"temperature\": 0}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/otel-2-0-llm-31b-it"
      },
      "weights": {
        "trainedBy": "AT\u0026T Chief Data Office, with GSMA, Red Hat, Microsoft, AMD, Dell, MLCommons and Pleias named as contributors",
        "builtOn": "google/gemma-4-31b-it (post-trained with OSFT; named on the card and in its metadata)",
        "params": "31B",
        "context": 262144,
        "goodFor": "Telecoms question answering and RAG over 3GPP, ETSI, GSMA, CAMARA, ITU, O-RAN and TM Forum material, in English, as the generation stage with retrieval and human review around it.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "OTel-2.0-LLM-31B-IT",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.55,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The 15 shards the index names. The repository also holds an earlier 14-shard set of 64.21 GB, so download by the index and not the whole repository. No quantised build from the publisher or the usual converters"
          }
        ],
        "run": "vllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16",
        "runBuild": "OTel-2.0-LLM-31B-IT",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "AT\u0026T (Open Telco AI)",
        "family": "OTel 2.0",
        "url": "https://www.anchorterminal.com/open-weights/#att-open-telco",
        "others": [
          {
            "name": "OTel-2.0-LLM-31B-IT-QLoRA",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT-QLoRA",
            "params": "31B"
          },
          {
            "name": "OTel-2.0-Decision-26B-A4B-4bit",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-Decision-26B-A4B-4bit",
            "params": "26B total, 4B active (per name; card not read)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "AT\u0026T (the card and GSMA's newsroom name AT\u0026T as the releaser, and the model answers that AT\u0026T Chief Data Office trained it). Published from a contributor's personal accounts on Hugging Face and GitHub",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No vendor domain is recorded. The project's pages are a personal GitHub repository and Hugging Face account, and open-telco.ai, the site GSMA's article links, answered 403 on 10 November 2026.",
          "GSMA's newsroom article of 24 July 2026 names AT\u0026T as the releaser and GSMA as the source of the corpus. It doesn't link the Hugging Face repository.",
          "The Hub lists the publishing account as a member of the AT\u0026T and GSMA organisations.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The GitHub repository has no SECURITY.md (404 on 10 November 2026)."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.json"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "nemotron-3-5-bolt-e4b-4gb",
        "name": "Reach",
        "reasons": {
          "a": "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.",
          "b": "Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50."
        },
        "scores": {
          "a": 90,
          "b": 50
        }
      },
      {
        "by": 25,
        "category": "risk",
        "leads": "nemotron-3-5-bolt-e4b-4gb",
        "name": "Risk",
        "reasons": {
          "a": "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.",
          "b": "File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT\u0026T's or GSMA's site links to: GSMA's article names AT\u0026T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. 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 licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55."
        },
        "scores": {
          "a": 80,
          "b": 55
        }
      },
      {
        "by": 25,
        "category": "interface",
        "leads": "nemotron-3-5-bolt-e4b-4gb",
        "name": "Agent interface",
        "reasons": {
          "a": "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.",
          "b": "Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 9,
        "category": "upkeep",
        "leads": "otel-2-0-llm-31b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "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.",
          "b": "Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74."
        },
        "scores": {
          "a": 65,
          "b": 74
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "b": "AT\u0026T (Open Telco AI)",
        "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": "OpenMDW-1.1",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-23",
        "b": "2026-11-01",
        "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": "4/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.",
        "question": "Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or OTel-2.0-LLM-31B-IT?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 50",
          "Risk, 80 against 55",
          "Agent interface, 85 against 60"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 74 against 65"
        ],
        "also": null,
        "goodFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware",
        "slug": "otel-2-0-llm-31b-it",
        "watchFor": "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked"
      }
    ],
    "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-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/agents-a1-4b-vs-otel-2-0-llm-31b-it.json",
        "title": "Agents-A1-4B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it"
      },
      {
        "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/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.json",
        "title": "Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it"
      },
      {
        "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/apodex-1-1-mini-vs-otel-2-0-llm-31b-it.json",
        "title": "Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it"
      },
      {
        "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": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.json",
        "title": "Arete-27B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Laguna M.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM3-1.7B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM3-Audio-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM3-Omni-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM3-VL-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Ling-3.1-flash-1019 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "MiniCPM-V 4.7 1B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb"
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        "title": "Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Muse Glimmer 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "title": "Nanbeige4.2-3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "title": "Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Nanbeige4.3-33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it.json",
        "title": "Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-5-lightning-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-4b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-4b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-4B",
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-27b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-35b-a3b.json",
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        "title": "OTel-2.0-LLM-31B-IT vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.json",
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-8b-instruct"
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2"
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.json",
        "title": "OTel-2.0-LLM-31B-IT vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview.json",
        "title": "OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview"
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      {
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b"
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        "title": "OTel-2.0-LLM-31B-IT vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b.json",
        "title": "OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.json",
        "title": "OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json",
        "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b.json",
        "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.json",
        "title": "OTel-2.0-LLM-31B-IT vs ZAYA1-8B",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "nemotron-3-5-bolt-e4b-4gb",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-5-bolt-e4b-4gb": 90,
        "otel-2-0-llm-31b-it": 50,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "nemotron-3-5-bolt-e4b-4gb",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-5-bolt-e4b-4gb": 80,
        "otel-2-0-llm-31b-it": 55,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "nemotron-3-5-bolt-e4b-4gb",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-5-bolt-e4b-4gb": 85,
        "otel-2-0-llm-31b-it": 60,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "otel-2-0-llm-31b-it",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-5-bolt-e4b-4gb": 65,
        "otel-2-0-llm-31b-it": 74,
        "weight": 10
      }
    ],
    "summary": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "otel-2-0-llm-31b-it": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it",
    "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.\n\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- OTel-2.0-LLM-31B-IT: grade C, 56.8/100, rank #111 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.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### 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- Reach, 90 against 50\n- Risk, 80 against 55\n- Agent interface, 85 against 60\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### OTel-2.0-LLM-31B-IT (C)\n\nGood for: Telecoms teams that want an open model for standards questions and RAG on their own hardware\n\nAhead on:\n- Documentation and upkeep, 74 against 65\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n\n## Why the edge\n\n### Reach: NVIDIA Nemotron 3.5 Bolt E4B 4GB +40\n\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- OTel-2.0-LLM-31B-IT (50): Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n\n### Risk: NVIDIA Nemotron 3.5 Bolt E4B 4GB +25\n\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- OTel-2.0-LLM-31B-IT (55): File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT\u0026T's or GSMA's site links to: GSMA's article names AT\u0026T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. 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 licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55.\n\n### Agent interface: NVIDIA Nemotron 3.5 Bolt E4B 4GB +25\n\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- OTel-2.0-LLM-31B-IT (60): Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n\n### Documentation and upkeep: OTel-2.0-LLM-31B-IT +9\n\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- OTel-2.0-LLM-31B-IT (74): Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.\n\n## Score by category\n\n| Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | OTel-2.0-LLM-31B-IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 50 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +40 |\n| Risk | 20% (20 this run) | 80 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |\n| Agent interface | 10% (10 this run) | 85 | 60 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 74 | OTel-2.0-LLM-31B-IT +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | OTel-2.0-LLM-31B-IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | AT\u0026T (Open Telco AI) |\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 | OpenMDW-1.1 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-23 | 2026-11-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 4/5 (4) | none |\n\n## Verdicts\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**OTel-2.0-LLM-31B-IT.** A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.\n\n## Before you call either\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### OTel-2.0-LLM-31B-IT\n\n1. Download the 15 shards the index names, not the whole repository, which is twice the size\n2. Pin a revision. The card says the weights may change under the same name\n3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's\n4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel\n5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review\n6. Don't use it to run network tools unattended. The card says that hasn't been evaluated\n\n## Questions\n\n### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or OTel-2.0-LLM-31B-IT?\n\nNVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-5-bolt-e4b-4gb\", \"b\": \"otel-2-0-llm-31b-it\"}`. From a terminal: `anchor compare nemotron-3-5-bolt-e4b-4gb otel-2-0-llm-31b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json and https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json\n\n## Other comparisons with NVIDIA Nemotron 3.5 Bolt E4B 4GB or OTel-2.0-LLM-31B-IT\n\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- [Agents-A1-4B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.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- [Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.md)\n- [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 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E4B 4GB](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cola2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.md)\n- [Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.md)\n- [Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek-V3.2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek-V3.2 vs 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4GB](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling-Small vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-small-vs-otel-2-0-llm-31b-it.md)\n- [Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kimi K3.1 Pro vs 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OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it.md)\n- [MiMo-V2.6 Distill Qwen 9B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiMo-V2.6 Distill Qwen 9B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-otel-2-0-llm-31b-it.md)\n- [MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it.md)\n- [MiniCPM-o 4.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiniCPM-o 4.5 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-otel-2-0-llm-31b-it.md)\n- [MiniCPM-V 4.7 1B vs NVIDIA Nemotron 3.5 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A3B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-30b-a3b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-super-120b-a12b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs North Mini Code 1.0](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-north-mini-code-1-0.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs occamy-1.0](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-occamy-1-0.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-olmo-3-7b-think.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-122b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-35b-a3b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-397b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-9b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-30b-a3b-instruct-2507.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-0-8b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-2B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-2b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-397b-a17b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-4B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-4b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-9B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-27B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-30b-a3b-instruct.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-next.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs 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