{
  "data": {
    "a": {
      "slug": "kimi-k3",
      "name": "Kimi K3",
      "vendor": "Moonshot AI",
      "vendorUrl": "https://www.moonshot.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.",
      "url": "https://www.anchorterminal.com/tools/kimi-k3",
      "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json",
      "repo": "https://huggingface.co/moonshotai/Kimi-K3",
      "license": "Kimi K3 License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "moonshotai/Kimi-K3"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (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/moonshotai/Kimi-K3",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "modified-mit",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-09-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 62.4,
        "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": 24,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 75,
          "interface": 65,
          "reach": 50,
          "risk": 65,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.",
        "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work",
        "strengths": [
          "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn",
          "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card",
          "A third-party cyber evaluation by the UK AI Security Institute and CAISI",
          "Safetensors only, with a SHA-256 for every shard, downloadable with no account",
          "Served by Moonshot's API and five inference providers for agents without the hardware"
        ],
        "weaknesses": [
          "1,561 GB of files at its smallest publisher build, far beyond any single machine",
          "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code",
          "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered",
          "No disclosure route for flaws and no prompt-injection guidance",
          "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages"
        ],
        "agentNotes": [
          "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000",
          "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect",
          "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable",
          "Thinking is always on; set reasoning_effort to low or high when max is too slow",
          "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones",
          "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear",
          "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 62.4
          }
        ],
        "editorialScores": {
          "capability": 75,
          "interface": 65,
          "reach": 50,
          "risk": 65,
          "upkeep": 41
        },
        "provenanceScore": 53,
        "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": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n  --context-length 65536 --mem-fraction-static 0.82 \\\n  --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"moonshotai/Kimi-K3\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"high\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kimi-k3"
      },
      "weights": {
        "trainedBy": "Moonshot AI",
        "params": "2.8T",
        "active": "104B",
        "context": 1048576,
        "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang"
        ],
        "builds": [
          {
            "name": "Kimi-K3",
            "url": "https://huggingface.co/moonshotai/Kimi-K3",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 1560.9,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Trained in MXFP4; the only build Moonshot publishes"
          }
        ],
        "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000",
        "runBuild": "Kimi-K3",
        "checked": "2026-10-21",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Moonshot AI (Kimi)",
        "family": "Kimi K3",
        "url": "https://www.anchorterminal.com/open-weights/#moonshot",
        "olderCount": 18,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)",
        "domain": "moonshot.ai",
        "domainRegistered": "",
        "domainNote": "moonshot.ai's registration date couldn't be read through RDAP on 21 October 2026.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.",
          "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.",
          "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json"
    },
    "answer": "Kimi K3 scores 62.4 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface.",
    "b": {
      "slug": "ornith-1-5-35b-a3b",
      "name": "Ornith-1.5-35B-A3B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-35B-A3B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 35B parameters with 3B active per token on Qwen3.5's architecture, with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:35b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all eight of Ornith's build repositories for this size are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 72 GB for the BF16 files, 19.5 GB for Ornith's MLX 4-bit build or 21.7 GB for its Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 36,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 90,
          "risk": 50,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.",
        "bestFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Fits in about 24 GB from Ornith's MLX 4-bit build and 32 GB from its Q4_K_M GGUF, NVFP4 or Ollama's build, with only about 3B parameters active per token",
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "A weight shard, every GGUF, an FP8 shard and the NVFP4 shards were replaced in place between 23 and 26 August 2026 with no note",
          "Ornith answered none of the 35 discussions",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks",
          "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "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": 85,
          "reach": 90,
          "risk": 50,
          "upkeep": 31
        },
        "provenanceScore": 13,
        "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": "ollama run ornith-1.5:35b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-35B-A3B --served-model-name Ornith-1.5-35B-A3B --tensor-parallel-size 2 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-35b-a3b"
      },
      "sameCompany": [
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-35B-A3B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-35B-A3B (Apache 2.0)",
        "params": "35B (3B active)",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, fast for its size with about 3B active parameters.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-35B-A3B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 19.5,
            "memoryGB": 24,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-35B-A3B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 23.5,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-35B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 25.3,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.7,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "ollama ornith-1.5:35b",
            "url": "https://ollama.com/library/ornith-1.5:35b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 23,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (22 GB) with a BF16 vision projector (903 MB), linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "Ornith-1.5-35B-A3B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 39.4,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Ornith-1.5-35B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 37.8,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 29.2,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-A3B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-35B-A3B-MLX-6bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-6bit",
            "who": "publisher",
            "precision": "6bit",
            "sizeGB": 28.2,
            "memoryGB": 48,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.1,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-A3B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 69.3,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:35b",
        "runBuild": "ollama ornith-1.5:35b",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "params": "122B (MoE)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.",
          "The card's blog badge links deep-reinforce.com/ornith.html, which redirects to Ornith's 1.0 blog post on ornith.ai.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Moonshot AI",
        "b": "Ornith",
        "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": "Kimi K3 License (modified MIT)",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-02",
        "b": "2026-08-23",
        "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": "2.3/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Kimi K3 scores 62.4 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface.",
        "question": "Which is better for AI agents, Kimi K3 or Ornith-1.5-35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 50",
          "Documentation and upkeep, 41 against 31"
        ],
        "also": null,
        "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work",
        "slug": "kimi-k3",
        "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine"
      },
      {
        "aheadOn": [
          "Reach, 90 against 50",
          "Agent interface, 85 against 65"
        ],
        "also": null,
        "goodFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-35b-a3b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.json",
        "title": "Agents-A1-4B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.json",
        "title": "Apertus v1.5 8B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.json",
        "title": "Apodex-1.1-mini vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.json",
        "title": "Apodex-1.1-mini vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.json",
        "title": "BigBang-v1 vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.json",
        "title": "Cosmos-Reason2-2B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.json",
        "title": "Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.json",
        "title": "Darwin-36B-Opus vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.json",
        "title": "Darwin-36B-Opus vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.json",
        "title": "DeepSeek V4.1 Flash vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.json",
        "title": "DeepSeek-V4-Flash-0731 vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3.json",
        "title": "DiffusionGemma 26B A4B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b.json",
        "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.json",
        "title": "Gemma 4 12B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.json",
        "title": "Gemma 4 12B IT vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.json",
        "title": "Gemma 4 26B A4B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b.json",
        "title": "Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.json",
        "title": "Gemma 4 31B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.json",
        "title": "Gemma 4 31B IT vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.json",
        "title": "Gemma 4 E2B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.json",
        "title": "Gemma 4 E2B IT vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.json",
        "title": "Gemma 4 E4B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.json",
        "title": "Gemma 4 E4B IT vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b.json",
        "title": "GLM-4.5-Air vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.json",
        "title": "GLM-4.7-Flash vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.json",
        "title": "GLM-4.7-Flash vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.json",
        "title": "GLM-5.3-Flash vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.json",
        "title": "GLM-5.3-Flash vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3.json",
        "title": "GLM-5.3 vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.json",
        "title": "GLM-5.3 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3.json",
        "title": "gpt-oss-120b vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3"
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      {
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-kimi-k3.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.json",
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      },
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b.json",
        "title": "occamy-1.0 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "ornith-1-5-35b-a3b",
        "key": "reach",
        "kimi-k3": 50,
        "name": "Reach",
        "ornith-1-5-35b-a3b": 90,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "kimi-k3",
        "key": "risk",
        "kimi-k3": 65,
        "name": "Risk",
        "ornith-1-5-35b-a3b": 50,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "ornith-1-5-35b-a3b",
        "key": "interface",
        "kimi-k3": 65,
        "name": "Agent interface",
        "ornith-1-5-35b-a3b": 85,
        "weight": 10
      },
      {
        "by": 10,
        "edge": "kimi-k3",
        "key": "upkeep",
        "kimi-k3": 41,
        "name": "Documentation and upkeep",
        "ornith-1-5-35b-a3b": 31,
        "weight": 10
      }
    ],
    "summary": "Kimi K3 scores 62.4 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.",
      "ornith-1-5-35b-a3b": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b",
    "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.min.md"
  },
  "markdown": "Kimi K3 scores 62.4 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json\n- Ornith-1.5-35B-A3B: grade C, 61/100, rank #43 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.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### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Risk, 65 against 50\n- Documentation and upkeep, 41 against 31\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\n\n### Ornith-1.5-35B-A3B (C)\n\nGood for: Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 90 against 50\n- Agent interface, 85 against 65\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n\n## Score by category\n\n| Category | Weight | Kimi K3 | Ornith-1.5-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 90 | Ornith-1.5-35B-A3B +40 |\n| Risk | 20% (20 this run) | 65 | 50 | Kimi K3 +15 |\n| Agent interface | 10% (10 this run) | 65 | 85 | Ornith-1.5-35B-A3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 31 | Kimi K3 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | Ornith-1.5-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | Ornith |\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 | Kimi K3 License (modified MIT) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-02 | 2026-08-23 |\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 | 2.3/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\n\n**Ornith-1.5-35B-A3B.** A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.\n\n## Before you call either\n\n### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\n\n### Ornith-1.5-35B-A3B\n\n1. Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Kimi K3 or Ornith-1.5-35B-A3B?\n\nKimi K3 scores 62.4 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"ornith-1-5-35b-a3b\"}`. From a terminal: `anchor compare kimi-k3 ornith-1-5-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json\n\n## Other comparisons with Kimi K3 or Ornith-1.5-35B-A3B\n\n- [Agents-A1-4B vs Kimi K3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.md)\n- [Agents-A1-4B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.md)\n- [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.md)\n- [Apertus v1.5 8B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.md)\n- [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.md)\n- [Apodex-1.1-mini vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.md)\n- [BigBang-v1 vs Kimi K3](https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.md)\n- [BigBang-v1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Kimi K3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.md)\n- [Darwin-36B-Opus vs Kimi K3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.md)\n- [Darwin-36B-Opus vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek-V3.2 vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b.md)\n- [DiffusionGemma 26B A4B IT vs Kimi K3](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 12B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.md)\n- [Gemma 4 12B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 26B A4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 31B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.md)\n- [Gemma 4 31B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E2B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.md)\n- [GLM-4.5-Air vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.md)\n- [GLM-4.5-Air vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b.md)\n- [GLM-4.7-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.md)\n- [GLM-4.7-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.md)\n- [GLM-5.3-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3 vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3.md)\n- [GLM-5.3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.md)\n- [gpt-oss-120b vs Kimi K3](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3.md)\n- [gpt-oss-120b vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-35b-a3b.md)\n- [gpt-oss-20b vs Kimi K3](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3.md)\n- [gpt-oss-20b vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-35b-a3b.md)\n- [Granite 4.2 3B vs Kimi K3](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.md)\n- [Granite 4.2 3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b.md)\n- [Hy3 vs Kimi K3](https://www.anchorterminal.com/compare/hy3-vs-kimi-k3.md)\n- [Hy3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.md)\n- [Inkling-Small vs Kimi K3](https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3.md)\n- [Inkling-Small vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-35b-a3b.md)\n- [Inkling vs Kimi K3](https://www.anchorterminal.com/compare/inkling-vs-kimi-k3.md)\n- [Inkling vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-35b-a3b.md)\n- [Kimi K3 vs Kolibri-1](https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.md)\n- [Kimi K3 vs Laguna M.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.md)\n- [Kimi K3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.md)\n- [Kimi K3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.md)\n- [Kimi K3 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Kimi K3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.md)\n- [Kimi K3 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.md)\n- [Kimi K3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.md)\n- [Kimi K3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.md)\n- [Kimi K3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.md)\n- [Kimi K3 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kimi K3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b.md)\n- [Kimi K3 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b.md)\n- [Kimi K3 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Kimi K3 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.md)\n- [Kimi K3 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-ultra-550b-a55b.md)\n- [Kimi K3 vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.md)\n- [Kimi K3 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think.md)\n- [Kimi K3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.md)\n- [Kimi K3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.md)\n- [Kimi K3 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Kimi K3 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507.md)\n- [Kimi K3 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b.md)\n- [Kimi K3 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b.md)\n- [Kimi K3 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b.md)\n- [Kimi K3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.md)\n- [Kimi K3 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-4b.md)\n- [Kimi K3 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.md)\n- [Kimi K3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-27b.md)\n- [Kimi K3 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.md)\n- [Kimi K3 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.md)\n- [Kimi K3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next.md)\n- [Kimi K3 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.md)\n- [Kimi K3 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-2b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct.md)\n- [Kimi K3 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/kimi-k3-vs-qwopus3-8-27b-flash-v2.md)\n- [Kimi K3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b.md)\n- [Kimi K3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.md)\n- [Kimi K3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b.md)\n- [Kimi K3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Kimi K3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b.md)\n- [Kolibri-1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna M.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna S 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna XS 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.md)\n- [Ling-3.0-tiny vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-35b-a3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-35b-a3b.md)\n- [MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.md)\n- [MiniCPM5-2B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.5 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M2.7 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.md)\n- [Muse Glimmer 30B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Nano 30B A3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Nano 4B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b.md)\n- [Nemotron 3 Ultra 550B A55B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b.md)\n- 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