{
  "data": {
    "a": {
      "slug": "inkling-2-small",
      "name": "Inkling 2 Small",
      "vendor": "Thinking Machines Lab",
      "vendorUrl": "https://thinkingmachines.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Inkling 2 Small is Thinking Machines Lab's open-weight mixture-of-experts model, 594B total and 17B active parameters, taking text and images and returning text, with adjustable thinking effort, published in NVFP4 and MXFP8 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/inkling-2-small",
      "markdownUrl": "https://www.anchorterminal.com/tools/inkling-2-small.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling-2-small.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json",
      "dns": "inkling-2-small.grade.anchorterminal.com",
      "repo": "https://huggingface.co/thinkingmachines/Inkling-2-Small",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "thinkingmachines/Inkling-2-Small"
        },
        {
          "registry": "huggingface",
          "name": "thinkingmachines/Inkling-2-Small-MXFP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-12). Thinking Machines' Model Acceptable Use Policy, linked from the card, says that downloading or using the weights binds you to it.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with Thinking Machines' Model Acceptable Use Policy on top. Running it yourself costs the hardware, 393.5 GB of files for the NVFP4 checkpoint and 622.8 GB for MXFP8, which Thinking Machines puts at 500 GB and 800 GB of GPU memory. Thinking Machines' Tinker serves it as a beta serverless inference API at $0.30 per million input tokens, $0.03 cached and $1.00 output (read 2026-11-06).",
      "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 2026-11-12).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-12"
      },
      "docsUrl": "https://thinkingmachines.ai/model-card/inkling-2-small/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "nvfp4",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-11-12",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.8,
        "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": 1524,
        "categoryRank": 78,
        "methodology": "0.8",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 40,
          "risk": 65,
          "upkeep": 67
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-12"
        },
        "negative": 0,
        "verdict": "Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.",
        "bestFor": "Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.",
        "strengths": [
          "Safetensors only, with a checksum on every LFS file in the publisher's own organisation",
          "Thinking effort set per request from none to xhigh, several tool calls a turn and a 1M-token context",
          "Two checkpoints from Thinking Machines, NVFP4 and MXFP8, under Apache 2.0 with no gate",
          "Thinking Machines' tml-renderers package renders prompts and parses tool calls for this model",
          "A training-content summary with sources, mix and an October 2026 collection cut-off",
          "Thinking Machines' own Tinker hosts it as beta serverless inference, and the card names seven more API providers"
        ],
        "weaknesses": [
          "The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory",
          "No released version of vLLM, SGLang, Transformers, llama.cpp or Ollama runs it on 12 November 2026",
          "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time",
          "Safety results are Thinking Machines' own, with no harness to rerun them and no prompt-injection guidance",
          "Structured output isn't documented, and the card gives no dated knowledge cut-off",
          "No independent benchmark results were read, so Capability is pending and the total is capped at 61"
        ],
        "agentNotes": [
          "No released runtime runs it yet. Serve the NVFP4 checkpoint from SGLang's dev-inkling-2-small image with the cookbook's recipe for your hardware, or from vLLM's vllm/vllm-openai:inkling-2-small image",
          "In vLLM pass `--reasoning-parser inkling --enable-auto-tool-choice --tool-call-parser inkling`. In SGLang the tool-call parser is `inkling2` and the reasoning parser `inkling`",
          "Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet",
          "Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99",
          "Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens",
          "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error",
          "Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part",
          "Use guided decoding or validate JSON yourself, since structured output isn't documented",
          "Without the hardware, call it on Tinker's OpenAI-compatible endpoint with a Tinker key, at $0.30 input and $1.00 output per million tokens as read on 6 November 2026",
          "Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.8",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58.8
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 40,
          "risk": 65,
          "upkeep": 67
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "interface": "2026-11-12",
            "reach": "2026-11-12",
            "risk": "2026-11-12",
            "upkeep": "2026-11-12"
          },
          "last": "2026-11-12",
          "lastMoved": "2026-11-12",
          "oldest": "2026-11-12",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# per SGLang's cookbook, the NVFP4 checkpoint on 4 B300 GPUs. Not in an SGLang release yet,\n# so run it inside the lmsysorg/sglang:dev-inkling-2-small image:\nSGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096 sglang serve \\\n  --model-path thinkingmachines/Inkling-2-Small --tp 4 --ep 4 \\\n  --moe-a2a-backend megamoe --attention-backend fa4 --bf16-gemm-backend torch --dtype bfloat16 \\\n  --page-size 128 --grammar-backend xgrammar --enable-multimodal \\\n  --reasoning-parser inkling --tool-call-parser inkling2",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"thinkingmachines/Inkling-2-Small\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"low\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/inkling-2-small"
      },
      "sameCompany": [
        "inkling",
        "inkling-small"
      ],
      "sameModel": [
        "tinker"
      ],
      "weights": {
        "trainedBy": "Thinking Machines Lab",
        "params": "594B",
        "active": "17B",
        "context": 1048576,
        "goodFor": "Mixture-of-experts generalist for coding, agents and tool use with images in, a 1M-token context and thinking effort set per request.",
        "runtimes": [],
        "builds": [
          {
            "name": "Inkling-2-Small",
            "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 393.5,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "The main checkpoint, in the precision the model was trained in. Routed experts in NVFP4 for layers 0 to 39 and MXFP8 for layers 40 to 47, per SGLang's cookbook. Thinking Machines states at least 500 GB of GPU memory"
          },
          {
            "name": "Inkling-2-Small-MXFP8",
            "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small-MXFP8",
            "who": "publisher",
            "precision": "mxfp8",
            "sizeGB": 622.8,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "Every routed expert in MXFP8. Thinking Machines states at least 800 GB of GPU memory and recommends the NVFP4 checkpoint. It's the only checkpoint Transformers' main branch loads"
          }
        ],
        "run": "SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096 sglang serve --model-path thinkingmachines/Inkling-2-Small --tp 4 --ep 4 --moe-a2a-backend megamoe --attention-backend fa4 --bf16-gemm-backend torch --dtype bfloat16 --page-size 128 --grammar-backend xgrammar --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling2",
        "runBuild": "Inkling-2-Small",
        "checked": "2026-11-12",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "Thinking Machines Lab",
        "family": "Inkling 2",
        "url": "https://www.anchorterminal.com/open-weights/#thinking-machines"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Thinking Machines Lab, Inc. (named as provider on its model card page and in its training-content summary)",
        "domain": "thinkingmachines.ai",
        "domainRegistered": "",
        "domainNote": "Not looked up: RDAP isn't requested under the 1 November 2026 rules.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/thinkingmachines/Inkling-2-Small/commits/main",
        "securityTxt": "valid",
        "checked": "2026-11-12",
        "notes": [
          "Thinking Machines' model card page links both repositories in the thinkingmachines organisation on Hugging Face, which isn't verified by Hugging Face.",
          "Nothing is hosted for this listing, so the Apache 2.0 licence and the Model Acceptable Use Policy stand in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 3 commits on 12 November 2026, the upload, a library tag from a merged pull request and a card update.",
          "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, read on 12 November 2026, and the model card page links a coordinated vulnerability disclosure policy.",
          "Thinking Machines' terms bar scraping the Services. The founder decided on 6 November 2026 that reading at a person's pace is fine, so six pages were read one at a time on 12 November: the acceptable use policy, the training data page and its summary for this model, the model card page, and the tml-renderers and thinking effort guides. thinkingmachines.ai's robots.txt has no rules, and tinker-docs.thinkingmachines.ai answers 404 for robots.txt."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-2-small.json",
      "live": {
        "slug": "inkling-2-small",
        "probe": {
          "target": "https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1",
          "method": "get",
          "lastAt": "2026-11-13T20:11:37.236512439Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 439,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 238,
          "p95ms24h": 467,
          "samples24h": 245,
          "samples30d": 1854,
          "days": [
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              "probes": 186,
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            {
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            {
              "date": "2026-11-09",
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            {
              "date": "2026-11-10",
              "probes": 244,
              "ok": 244
            },
            {
              "date": "2026-11-11",
              "probes": 244,
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            {
              "date": "2026-11-12",
              "probes": 245,
              "ok": 245
            },
            {
              "date": "2026-11-13",
              "probes": 206,
              "ok": 206
            }
          ],
          "since": "2026-11-06T00:00:00Z"
        },
        "securityTxt": {
          "url": "https://thinkingmachines.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2029-07-13T07:00:00.000Z",
          "checkedAt": "2026-11-14T16:19:22.779006645Z"
        },
        "llmsTxt": {
          "url": "https://tinker-docs.thinkingmachines.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-13T15:54:36.79283015Z"
        },
        "domain": {
          "domain": "thinkingmachines.ai",
          "registered": "2024-07-09",
          "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai",
          "checkedAt": "2026-11-08T15:05:10.558189316Z"
        },
        "pages": [
          {
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            "checkedAt": "2026-11-14T19:07:35.812797752Z",
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            "checkedAt": "2026-11-13T18:32:58.088087394Z",
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          {
            "url": "https://thinkingmachines.ai/legal/privacy/",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-11-13T18:32:53.884197991Z",
            "changedAt": "2026-11-06T13:43:40.660240815Z",
            "fingerprint": "3c33a4a74c61"
          },
          {
            "url": "https://thinkingmachines.ai/legal/terms/",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-11-13T18:32:56.034092745Z",
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        "updatedAt": "2026-11-14T19:07:35.812797752Z"
      },
      "streak": {
        "days": 9,
        "from": "2026-11-06",
        "sinceWatching": true,
        "best": 9
      }
    },
    "answer": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "nemotron-3-super-120b-a12b",
      "name": "Nemotron 3 Super 120B A12B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Super is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 120B parameters with 12B active, published on Hugging Face in BF16, FP8 and NVFP4 under the NVIDIA Nemotron Open Model License.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json",
      "dns": "nemotron-3-super-120b-a12b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
      "license": "NVIDIA Nemotron Open Model License",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-super"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 247.2 GB of BF16 files (8× H100-80GB minimum per the card), 80.3 GB for NVIDIA's NVFP4 build or 87 GB for Ollama's default build (checked 2026-10-21).",
      "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 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-01"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "hybrid",
        "reasoning",
        "tool-calling",
        "long-context",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-03-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 66.7,
        "grade": "B",
        "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": 1524,
        "categoryRank": 11,
        "methodology": "0.8",
        "run": "2026-10-01",
        "scores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 65,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.",
        "bestFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data",
        "strengths": [
          "Runs in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with NVIDIA's own FP8 and NVFP4 builds",
          "Training data listed by dataset with token counts and cut-offs, much of it published",
          "Tool calls documented with a named parser, several per turn, and thinking off, low effort or budgeted per request",
          "Free commercial licence with no threshold or use policy, no gate, and NVIDIA's own API serves it",
          "Context of 262,144 tokens by default and up to 1M per the card"
        ],
        "weaknesses": [
          "The card's serving commands pass --trust-remote-code",
          "No safety evaluation results published",
          "No NVIDIA reply in any of the repository's nine discussions",
          "Needs about 96 to 104 GB even at 4-bit, and NVIDIA's NVFP4 build needs Blackwell",
          "Structured output claimed but not documented"
        ],
        "agentNotes": [
          "On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)",
          "Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs",
          "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)",
          "Use `temperature=1.0` and `top_p=0.95` for every task, as the card says",
          "Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks",
          "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content",
          "Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.8",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 66.7
          }
        ],
        "editorialScores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 65,
          "upkeep": 70
        },
        "provenanceScore": 76,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "lastMoved": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3-super\n# or, with the card's vLLM command (abridged):\npip install vllm==0.18.1\nvllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 --served-model-name nvidia/nemotron-3-super \\\n  --tensor-parallel-size 8 --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 \\\n  --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"nvidia/nemotron-3-super\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n    \"temperature\": 1.0, \"top_p\": 0.95,\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-super-120b-a12b"
      },
      "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-3-5-bolt-e4b-4gb",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
        "params": "120B",
        "active": "12B",
        "context": 1048576,
        "goodFor": "Hybrid Mamba and mixture-of-experts reasoning model for agents, text only, with tool calling, a low-effort mode and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "unsloth NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (UD-Q4_K_S)",
            "url": "https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "unsloth",
            "precision": "q4_k_s",
            "sizeGB": 79.02,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not NVIDIA's. 79.02 GB of UD-Q4_K_S weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 80.3,
            "memoryGB": 128,
            "format": "safetensors",
            "note": "Pre-trained in NVFP4; runs on one B200 or DGX Spark per its card. NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "ollama nemotron-3-super:latest",
            "url": "https://ollama.com/library/nemotron-3-super:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 87,
            "memoryGB": 128,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 86.05,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 86.05 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. LM Studio's own catalogue page for this model wasn't checked. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 128.4,
            "memoryGB": 192,
            "format": "safetensors"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q6_K)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "lmstudio",
            "precision": "q6_k",
            "sizeGB": 112.92,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 112.92 GB of Q6_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q8_0)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
            "who": "lmstudio",
            "precision": "q8_0",
            "sizeGB": 128.47,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 128.47 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 247.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 --served-model-name nvidia/nemotron-3-super --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "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 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 3.5 Bolt E4B 4GB",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "params": "8B total, 4B effective",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb"
          },
          {
            "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,
        "unrestrictedCopies": 5
      },
      "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-Super-120B-A12B-BF16/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer, and the licence's notice reads Licensed by NVIDIA Corporation. The Hugging Face organisation nvidia is verified and linked from developer.nvidia.com. RDAP gives 20 April 1993 for nvidia.com.",
          "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Since 10 March 2026 only the card has changed, with some messages that say what (Update BF16 configs, Update KV Cache/Mamba dtype, Update for MTPv2) and several that read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026, so it is counted as unchecked rather than absent. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 76
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.json",
      "live": {
        "slug": "nemotron-3-super-120b-a12b",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-14T16:17:34.346018248Z"
        },
        "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-Super-120B-A12B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-14T19:06:45.900137495Z",
            "changedAt": "2026-11-14T19:06:45.900137495Z",
            "fingerprint": "aff254ed7471"
          }
        ],
        "updatedAt": "2026-11-14T19:06:45.900137495Z"
      }
    },
    "edges": [
      {
        "by": 35,
        "category": "reach",
        "leads": "nemotron-3-super-120b-a12b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line. Runtimes 0, no released version of the six runs it; vLLM and SGLang serve it from their own images and Transformers has it on its main branch only. Builds 15, two precisions from the publisher (NVFP4 and MXFP8). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and Together is live on Hugging Face. 0 + 0 + 15 + 10 + 10 + 5 = 40.",
          "b": "Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it."
        },
        "scores": {
          "a": 40,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "nemotron-3-super-120b-a12b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back for this model, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to xhigh, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Thinking Machines Lab",
        "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": "NVIDIA Nemotron Open Model License",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-12",
        "b": "2026-03-11",
        "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": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Inkling 2 Small or Nemotron 3 Super 120B A12B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.",
        "slug": "inkling-2-small",
        "watchFor": "The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory"
      },
      {
        "aheadOn": [
          "Reach, 75 against 40",
          "Agent interface, 90 against 85"
        ],
        "also": null,
        "goodFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data",
        "slug": "nemotron-3-super-120b-a12b",
        "watchFor": "The card's serving commands pass --trust-remote-code"
      }
    ],
    "history": {
      "inkling-2-small": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-12",
          "delta": 6.7,
          "from": 52.1,
          "fromGrade": "D",
          "to": 58.8,
          "toGrade": "C"
        }
      ],
      "nemotron-3-super-120b-a12b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-01",
          "delta": 0.5,
          "from": 66.2,
          "fromGrade": "B",
          "to": 66.7,
          "toGrade": "B"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small.json",
        "title": "Agents-A1-4B vs Inkling 2 Small",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-lfm3-omni-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-lfm3-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-1-flash-1019.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-longcat-2-5.json",
        "title": "Inkling 2 Small vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-longcat-2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mimo-v2-6-flash-rl.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-o-4-5.json",
        "title": "Inkling 2 Small vs MiniCPM-o 4.5",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-v-4-7-1b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-v-4-7-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm5-2b.json",
        "title": "Inkling 2 Small vs MiniCPM5-2B",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-m3-1-flash.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mistral-large-4.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nanbeige-4-3-33b.json",
        "title": "Inkling 2 Small vs Nanbeige4.3-33B",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-ultra-550b-a55b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-occamy-1-0.json",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-olmo-3-7b-think.json",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-122b.json",
        "title": "Inkling 2 Small vs Ornith-1.5-122B",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-35b-a3b.json",
        "title": "Inkling 2 Small vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-397b.json",
        "title": "Inkling 2 Small vs Ornith-1.5-397B",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-9b.json",
        "title": "Inkling 2 Small vs Ornith-1.5-9B",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Inkling 2 Small vs Qwen3-30B-A3B-Instruct-2507",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-0-8b.json",
        "title": "Inkling 2 Small vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-122b-a10b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-2b.json",
        "title": "Inkling 2 Small vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-397b-a17b.json",
        "title": "Inkling 2 Small vs Qwen3.5-397B-A17B",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-4b.json",
        "title": "Inkling 2 Small vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-9b.json",
        "title": "Inkling 2 Small vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-6-27b.json",
        "title": "Inkling 2 Small vs Qwen3.6-27B",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-6-35b-a3b.json",
        "title": "Inkling 2 Small vs Qwen3.6-35B-A3B",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-27b.json",
        "title": "Inkling 2 Small vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json",
        "title": "Nemotron 3 Super 120B A12B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview.json",
        "title": "Nemotron 3 Super 120B A12B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview.json",
        "title": "Nemotron 3 Super 120B A12B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.json",
        "title": "Nemotron 3 Super 120B A12B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Super 120B A12B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash.json",
        "title": "Nemotron 3 Super 120B A12B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b.json",
        "title": "Nemotron 3 Super 120B A12B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "nemotron-3-super-120b-a12b",
        "inkling-2-small": 40,
        "key": "reach",
        "name": "Reach",
        "nemotron-3-super-120b-a12b": 75,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "inkling-2-small": 65,
        "key": "risk",
        "name": "Risk",
        "nemotron-3-super-120b-a12b": 65,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "nemotron-3-super-120b-a12b",
        "inkling-2-small": 85,
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-super-120b-a12b": 90,
        "weight": 10
      },
      {
        "by": 3,
        "edge": "nemotron-3-super-120b-a12b",
        "inkling-2-small": 67,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-super-120b-a12b": 70,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "inkling-2-small": "Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.",
      "nemotron-3-super-120b-a12b": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b",
    "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b.min.md"
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  "markdown": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Inkling 2 Small: grade C, 58.8/100, rank #102 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-2-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json\n- Nemotron 3 Super 120B A12B: grade B, 66.7/100, rank #15 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.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 6472 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Inkling 2 Small (C)\n\nGood for: Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.\n\nWatch for: The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory\n\n### Nemotron 3 Super 120B A12B (B)\n\nGood for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data\n\nAhead on:\n- Reach, 75 against 40\n- Agent interface, 90 against 85\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Why the edge\n\n### Reach: Nemotron 3 Super 120B A12B +35\n\n- Inkling 2 Small (40): Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line. Runtimes 0, no released version of the six runs it; vLLM and SGLang serve it from their own images and Transformers has it on its main branch only. Builds 15, two precisions from the publisher (NVFP4 and MXFP8). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and Together is live on Hugging Face. 0 + 0 + 15 + 10 + 10 + 5 = 40.\n- Nemotron 3 Super 120B A12B (75): Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it.\n\n### Agent interface: Nemotron 3 Super 120B A12B +5\n\n- Inkling 2 Small (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back for this model, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to xhigh, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Nemotron 3 Super 120B A12B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card.\n\n## Score by category\n\n| Category | Weight | Inkling 2 Small | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 75 | Nemotron 3 Super 120B A12B +35 |\n| Risk | 20% (20 this run) | 65 | 65 | even |\n| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Super 120B A12B +5 |\n| Documentation and upkeep | 10% (10 this run) | 67 | 70 | Nemotron 3 Super 120B A12B +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.8 · C** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | Inkling 2 Small | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Thinking Machines Lab | 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 | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-12 | 2026-03-11 |\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 | 2.8/5 (4) |\n\n## How the scores have moved\n\n### Inkling 2 Small\n\n- +6.7 on 12 November 2026 (D to C): product\n\n### Nemotron 3 Super 120B A12B\n\n- +0.5 on 1 November 2026: correction\n\n## Verdicts\n\n**Inkling 2 Small.** Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.\n\n**Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.\n\n## Before you call either\n\n### Inkling 2 Small\n\n1. No released runtime runs it yet. Serve the NVFP4 checkpoint from SGLang's dev-inkling-2-small image with the cookbook's recipe for your hardware, or from vLLM's vllm/vllm-openai:inkling-2-small image\n2. In vLLM pass `--reasoning-parser inkling --enable-auto-tool-choice --tool-call-parser inkling`. In SGLang the tool-call parser is `inkling2` and the reasoning parser `inkling`\n3. Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet\n4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99\n5. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens\n6. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n7. Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part\n8. Use guided decoding or validate JSON yourself, since structured output isn't documented\n9. Without the hardware, call it on Tinker's OpenAI-compatible endpoint with a Tinker key, at $0.30 input and $1.00 output per million tokens as read on 6 November 2026\n10. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n### Nemotron 3 Super 120B A12B\n\n1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)\n2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says\n5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content\n7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)\n\n## Questions\n\n### Which is better for AI agents, Inkling 2 Small or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling-2-small\", \"b\": \"nemotron-3-super-120b-a12b\"}`. From a terminal: `anchor compare inkling-2-small nemotron-3-super-120b-a12b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json\n\n## Other comparisons with Inkling 2 Small or Nemotron 3 Super 120B A12B\n\n- [Agents-A1-4B vs Inkling 2 Small](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small.md)\n- [Agents-A1-4B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.md)\n- [Apertus v1.5 8B vs Inkling 2 Small](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-2-small.md)\n- [Apertus v1.5 8B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.md)\n- [Apodex-1.1-mini vs Inkling 2 Small](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-2-small.md)\n- [Apodex-1.1-mini vs Nemotron 3 Super 120B 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    "title": "Inkling 2 Small vs Nemotron 3 Super 120B A12B for AI agents (2026)",
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