{
  "data": {
    "a": {
      "slug": "agents-a1-4b",
      "name": "Agents-A1-4B",
      "vendor": "InternScience (Shanghai AI Laboratory)",
      "vendorUrl": "https://internscience.github.io/Agents-A1/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/agents-a1-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json",
      "repo": "https://huggingface.co/InternScience/Agents-A1-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q8_0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-F16-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (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": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/InternScience/Agents-A1-4B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "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": 1197,
        "categoryRank": 27,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
        "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "strengths": [
          "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model",
          "FP8 and three GGUF precisions from the publisher",
          "Apache 2.0 through the chain, with no gate",
          "Trained for tool calling, several calls a turn, and 262,144 tokens of context",
          "An evaluation framework for its agent benchmarks is published on GitHub"
        ],
        "weaknesses": [
          "The base isn't declared, and the technical report doesn't cover the 4B",
          "No safety results, agent guidance or security contact",
          "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14",
          "Tool calling and structured output aren't documented beyond a parser name",
          "InternScience answered one of three discussions, and the history was squashed"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector",
          "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work",
          "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14",
          "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "provenanceScore": 40,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/agents-a1-4b"
      },
      "weights": {
        "trainedBy": "InternScience (Shanghai AI Laboratory)",
        "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B",
        "params": "4.5B",
        "context": 262144,
        "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Agents-A1-4B-Q8_0",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 5.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B-Q4_K_M",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 3.4,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.7 GB of weights and the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Agents-A1-4B-FP8",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 7.8,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Agents-A1-4B-F16",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          }
        ],
        "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Agents-A1-4B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "InternScience (Shanghai AI Laboratory)",
        "family": "Agents-A1",
        "url": "https://www.anchorterminal.com/open-weights/#internscience",
        "others": [
          {
            "name": "Agents-A1",
            "url": "https://huggingface.co/InternScience/Agents-A1",
            "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template",
        "domain": "internscience.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.",
          "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.",
          "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json"
    },
    "answer": "Inkling scores 64.2 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach.",
    "b": {
      "slug": "inkling",
      "name": "Inkling",
      "vendor": "Thinking Machines Lab",
      "vendorUrl": "https://thinkingmachines.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Inkling is Thinking Machines Lab's large open-weight mixture-of-experts model, 975B total and 41B active parameters, taking text, images and audio and returning text, with adjustable thinking effort, published in BF16 and NVFP4 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/inkling",
      "markdownUrl": "https://www.anchorterminal.com/tools/inkling.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling.json",
      "repo": "https://huggingface.co/thinkingmachines/Inkling",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "thinkingmachines/Inkling"
        },
        {
          "registry": "huggingface",
          "name": "thinkingmachines/Inkling-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21). 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, 1,904.8 GB for the BF16 files and 592.0 GB for the NVFP4 build. Thinking Machines' Tinker serves it as a beta serverless inference API at $1.00 per million input tokens and $4.05 per million output tokens (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": "2026-10-21"
      },
      "docsUrl": "https://thinkingmachines.ai/model-card/inkling/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "audio",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-07-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.2,
        "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": 1197,
        "categoryRank": 16,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 60,
          "interface": 85,
          "reach": 50,
          "risk": 70,
          "upkeep": 82
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.",
        "bestFor": "Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.",
        "strengths": [
          "Three inference providers and Thinking Machines' own Tinker host it today",
          "Safetensors only, with a checksum on every LFS file in the publisher's own organisation",
          "Thinking effort set per request, several tool calls a turn and a 1M-token context",
          "Released support in Transformers, vLLM and SGLang, with inkling tool and reasoning parsers",
          "Thinking Machines answered 8 of the 13 questions and reports in its discussions",
          "A training-content summary with sources, mix and a July 2026 collection cut-off"
        ],
        "weaknesses": [
          "The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation",
          "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time",
          "No release of ggml-org's llama.cpp, Ollama or LM Studio runs it",
          "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"
        ],
        "agentNotes": [
          "Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it",
          "On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`",
          "Use the inkling tool parser, not Hermes, and the inkling reasoning parser",
          "Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh",
          "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error",
          "Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card",
          "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": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.2
          }
        ],
        "editorialScores": {
          "capability": 60,
          "interface": 85,
          "reach": 50,
          "risk": 70,
          "upkeep": 82
        },
        "provenanceScore": 67,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# per SGLang's cookbook, NVFP4 on 8 B200 GPUs:\nsglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 \\\n  --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"thinkingmachines/Inkling-NVFP4\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"low\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/inkling"
      },
      "sameCompany": [
        "inkling-small"
      ],
      "sameModel": [
        "tinker"
      ],
      "weights": {
        "trainedBy": "Thinking Machines Lab",
        "params": "975B",
        "active": "41B",
        "context": 1048576,
        "goodFor": "Large mixture-of-experts generalist for coding, agents, factual recall and audio, with images in and thinking effort set per request.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Inkling-NVFP4",
            "url": "https://huggingface.co/thinkingmachines/Inkling-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 592,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "NVFP4 experts with FP8 and BF16 parts. Thinking Machines says W4A4 needs NVIDIA SM100 or newer, and W4A16 runs on H200"
          },
          {
            "name": "Inkling",
            "url": "https://huggingface.co/thinkingmachines/Inkling",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 1904.8,
            "memoryGB": 4096,
            "format": "safetensors",
            "note": "1,904.8 GB × 1.2 = 2,285.7 GB, so the 4,096 GB band. Thinking Machines states at least 2 TB of GPU memory"
          }
        ],
        "run": "sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling",
        "runBuild": "Inkling-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 1024
      },
      "family": {
        "lab": "Thinking Machines Lab",
        "family": "Inkling",
        "url": "https://www.anchorterminal.com/open-weights/#thinking-machines",
        "others": [
          {
            "name": "Inkling-Small",
            "url": "https://huggingface.co/thinkingmachines/Inkling-Small",
            "params": "265.96B (Hub safetensors count; the name states none)",
            "listing": "https://www.anchorterminal.com/tools/inkling-small"
          }
        ],
        "unrestrictedCopies": 1
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Thinking Machines Lab, Inc. (named on its model card page and in its training-content summary)",
        "domain": "thinkingmachines.ai",
        "domainRegistered": "",
        "domainNote": "thinkingmachines.ai's registration date wasn't read on 21 October 2026.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/thinkingmachines/Inkling/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "Thinking Machines' Inkling page links the thinkingmachines collection on Hugging Face, and its model card page links the repository. Hugging Face hasn't verified the organisation.",
          "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, 13 commits from 15 to 23 July 2026, the upload, a Transformers processor and template change on 15 July, card updates and community evaluation results.",
          "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, and the site publishes a coordinated vulnerability disclosure policy that leaves model safety to a separate incident contact."
        ],
        "score": 67
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "InternScience (Shanghai AI Laboratory)",
        "b": "Thinking Machines Lab",
        "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": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-14",
        "b": "2026-07-23",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Inkling scores 64.2 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach.",
        "question": "Which is better for AI agents, Agents-A1-4B or Inkling?"
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    ],
    "goodFor": [
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        "aheadOn": [
          "Reach, 90 against 50"
        ],
        "also": null,
        "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "slug": "agents-a1-4b",
        "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B"
      },
      {
        "aheadOn": [
          "Risk, 70 against 55",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 82 against 43"
        ],
        "also": null,
        "goodFor": "Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.",
        "slug": "inkling",
        "watchFor": "The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation"
      }
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      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-2b-instruct"
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling.json",
        "title": "DeepSeek-V3.2 vs Inkling",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.json",
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-4b-instruct-2507.json",
        "title": "Inkling vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-4b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-122b-a10b"
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        "title": "Inkling vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-4b.json",
        "title": "Inkling vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b.json",
        "title": "Inkling vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-omni-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-8b-instruct.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.json",
        "title": "Inkling vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b.json",
        "title": "Inkling vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Inkling vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Inkling vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Inkling vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "agents-a1-4b": 90,
        "by": 40,
        "edge": "agents-a1-4b",
        "inkling": 50,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "agents-a1-4b": 55,
        "by": 15,
        "edge": "inkling",
        "inkling": 70,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "agents-a1-4b": 70,
        "by": 15,
        "edge": "inkling",
        "inkling": 85,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "agents-a1-4b": 43,
        "by": 39,
        "edge": "inkling",
        "inkling": 82,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Inkling scores 64.2 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
      "inkling": "Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling",
    "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.min.md"
  },
  "markdown": "Inkling scores 64.2 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json\n- Inkling: grade B, 64.2/100, rank #20 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Reach, 90 against 50\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\n\n### Inkling (B)\n\nGood for: Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.\n\nAhead on:\n- Risk, 70 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 82 against 43\n\nWatch for: The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation\n\n\n## Score by category\n\n| Category | Weight | Agents-A1-4B | Inkling | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 50 | Agents-A1-4B +40 |\n| Risk | 20% (20 this run) | 55 | 70 | Inkling +15 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Inkling +15 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 82 | Inkling +39 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | Inkling |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | Thinking Machines Lab |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-07-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\n\n**Inkling.** Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.\n\n## Before you call either\n\n### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Inkling\n\n1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it\n2. On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`\n3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser\n4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh\n5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card\n7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n## Questions\n\n### Which is better for AI agents, Agents-A1-4B or Inkling?\n\nInkling scores 64.2 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"inkling\"}`. From a terminal: `anchor compare agents-a1-4b inkling`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/inkling.json\n\n## Other comparisons with Agents-A1-4B or Inkling\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md)\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md)\n- [Agents-A1-4B vs DeepSeek V4.1 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    "title": "Agents-A1-4B vs Inkling for AI agents in 2026: scores and prices",
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