{
  "data": {
    "a": {
      "slug": "smollm3-3b",
      "name": "SmolLM3-3B",
      "vendor": "Hugging Face",
      "vendorUrl": "https://huggingface.co",
      "kind": "weights",
      "category": "open-weights",
      "summary": "SmolLM3-3B is Hugging Face's open-weight dense 3B instruct model with switchable reasoning, tool calling and a 65,536-token native context, published in BF16 safetensors under Apache 2.0 with its training data and recipes.",
      "url": "https://www.anchorterminal.com/tools/smollm3-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/smollm3-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/smollm3-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json",
      "repo": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "HuggingFaceTB/SmolLM3-3B"
        },
        {
          "registry": "huggingface",
          "name": "HuggingFaceTB/SmolLM3-3B-ONNX"
        }
      ],
      "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 Apache 2.0. Running it yourself costs the hardware, 6.15 GB for the BF16 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/HuggingFaceTB/SmolLM3-3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "reasoning",
        "tool-calling",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2025-09-10",
      "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": 40,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 80,
          "reach": 85,
          "risk": 65,
          "upkeep": 54
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.",
        "bestFor": "Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device",
        "strengths": [
          "Training data, mixture weights, configs and intermediate checkpoints all published",
          "Plain Apache 2.0, no gate, no account",
          "About 7.4 GB in BF16, with Hugging Face's own ONNX builds down to 4-bit for ONNX Runtime",
          "Tool-call format and vLLM parser documented on the card",
          "Thinking turns off per request",
          "Safetensors only, with a SHA-256 for each shard and a Jinja template"
        ],
        "weaknesses": [
          "65,536 tokens trained; 131,072 needs a YaRN edit to config.json",
          "No change to the files since 10 September 2025 and no publisher reply in discussions since November 2025",
          "No safety evaluation or agent-use guidance",
          "No structured-output documentation",
          "No Ollama or LM Studio catalogue entry and no hosted provider",
          "The chat template renders tool results as user turns and drops tool_calls from earlier assistant messages"
        ],
        "agentNotes": [
          "Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text",
          "Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg",
          "Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history",
          "Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens",
          "Use temperature 0.6 and top_p 0.95, the card's recommended sampling",
          "Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none",
          "Validate JSON output yourself or use the runtime's guided decoding"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "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": 80,
          "reach": 85,
          "risk": 65,
          "upkeep": 54
        },
        "provenanceScore": 67,
        "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": "pip install -U transformers vllm\nvllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes",
        "http": "curl http://localhost:8000/v1/chat/completions -H \"Content-Type: application/json\" -d '{\n  \"model\": \"HuggingFaceTB/SmolLM3-3B\",\n  \"messages\": [{\"role\": \"user\", \"content\": \"Give me a brief explanation of gravity in simple terms.\"}],\n  \"temperature\": 0.6,\n  \"top_p\": 0.95,\n  \"max_tokens\": 16384,\n  \"chat_template_kwargs\": {\"enable_thinking\": false}\n}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/smollm3-3b"
      },
      "sameCompany": [
        "hugging-face-inference-endpoints"
      ],
      "weights": {
        "trainedBy": "Hugging Face",
        "builtOn": "HuggingFaceTB/SmolLM3-3B-Base",
        "params": "3.08B",
        "context": 65536,
        "goodFor": "Small fully open instruct model with switchable reasoning and tool calling, text only, multilingual.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "SmolLM3-3B",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 6.2,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "SmolLM3-3B-ONNX fp16",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_fp16.onnx_data",
            "who": "publisher",
            "precision": "fp16",
            "sizeGB": 6.2,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX int8",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_int8.onnx_data",
            "who": "publisher",
            "precision": "int8",
            "sizeGB": 3.1,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX uint8",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_uint8.onnx_data",
            "who": "publisher",
            "precision": "uint8",
            "sizeGB": 3.1,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX q4",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_q4.onnx_data",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 2.8,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX bnb4",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_bnb4.onnx_data",
            "who": "publisher",
            "precision": "bnb4",
            "sizeGB": 2.7,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          },
          {
            "name": "SmolLM3-3B-ONNX q4f16",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-ONNX/blob/main/onnx/model_q4f16.onnx_data",
            "who": "publisher",
            "precision": "q4f16",
            "sizeGB": 2.1,
            "memoryGB": 8,
            "format": "onnx",
            "note": "ONNX for Transformers.js and ONNX Runtime, none of the runtimes listed here"
          }
        ],
        "run": "vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes",
        "runBuild": "SmolLM3-3B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Hugging Face (SmolLM)",
        "family": "SmolLM3",
        "url": "https://www.anchorterminal.com/open-weights/#huggingface",
        "others": [
          {
            "name": "SmolLM3-3B-Base",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base",
            "params": "3B"
          },
          {
            "name": "SmolLM3-3B-checkpoints",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints",
            "params": "3B"
          },
          {
            "name": "SmolLM3-3B-GSM8K-SFT",
            "url": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B-GSM8K-SFT",
            "params": "3B"
          }
        ],
        "olderCount": 20,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Hugging Face, Inc.",
        "domain": "huggingface.co",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/HuggingFaceTB/SmolLM3-3B/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The model is published in HuggingFaceTB (Hugging Face Smol Models Research), the organisation Hugging Face's own launch post at huggingface.co/blog/smollm3 links to. Hugging Face's Terms of Service name Hugging Face, Inc.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. A few messages say what changed (an EOS token ID update on 8 July 2025, a template fix for vLLM on 9 July 2025); most read Update README.md.",
          "huggingface.co/.well-known/security.txt is valid, with a contact and an expiry of 1 July 2030.",
          "rdap.org has no RDAP service for .co domains, so the registration date wasn't checked."
        ],
        "score": 67
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/smollm3-3b.json"
    },
    "answer": "SmolLM3-3B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
    "b": {
      "slug": "swift-1-5-qwen3-8-27b",
      "name": "Swift 1.5 Qwen3.8-27B",
      "vendor": "UkisAI",
      "vendorUrl": "https://ukisai.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Swift 1.5 Qwen3.8-27B is UkisAI's post-trained Qwen3.8-27B that aims to think in fewer tokens, a 27B dense vision-language model with the base's 262,144-token context, published in BF16 with GGUF, AWQ, NVFP4, FP8 and MLX builds under UkisAI's own licence.",
      "url": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b",
      "markdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json",
      "repo": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b",
      "license": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ukisai/Swift-1.5-Qwen3.8-27b"
        },
        {
          "registry": "huggingface",
          "name": "ukisai/Swift-1.5-Qwen3.8-27B-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports the repository as not gated, though the card's metadata says gated true (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use. Commercial use is free for individuals and organisations whose gross annual revenue, affiliates included, is below US$1,000,000, and needs a separate Swift Enterprise License above it. The base Qwen3.8-27B stays under Apache 2.0. UkisAI's card also lists a free API for research purposes at ukisai.com/api/swift/v1. Running it yourself costs the hardware, 55.6 GB in BF16 and 15.9 to 18.4 GB for the 4-bit builds by the Hub's file lists (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/ukisai/Swift-1.5-Qwen3.8-27b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "fine-tune",
        "custom-licence",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-09-24",
      "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": 42,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 80,
          "risk": 50,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.",
        "bestFor": "Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.",
        "strengths": [
          "UkisAI's own 4-bit builds fit in about 20 GB, with GGUF, INT4, NVFP4, FP8 and MLX choices",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as Qwen3.8-27B does, with the same template and 262,144-token context",
          "Safetensors only, in the account UkisAI's site links to, with a checksum on every file",
          "Several tool calls a turn and post-training aimed at agent and coding tasks",
          "Ungated, with no account or terms to accept"
        ],
        "weaknesses": [
          "Commercial use above US$1M gross annual revenue needs a separate licence",
          "No reply from UkisAI in any discussion, including reports that the model is broken",
          "No safety results, agent guidance or security contact",
          "The card doesn't say how to set reasoning effort, turn thinking off or ask for JSON",
          "Training data described only in general terms, and the efficiency claims are UkisAI's own runs"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector",
          "On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`",
          "The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base",
          "UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point",
          "The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat",
          "Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates",
          "MTP self-speculative decoding is available with the base's head, flags on the card"
        ],
        "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": 60,
          "reach": 80,
          "risk": 50,
          "upkeep": 43
        },
        "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": "vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or the 4-bit GGUF with its vision projector:\nllama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ukisai/Swift-1.5-Qwen3.8-27b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello, Swift.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/swift-1-5-qwen3-8-27b"
      },
      "sameCompany": [
        "swift-1-5-qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "UkisAI, post-training Alibaba's Qwen3.8-27B",
        "builtOn": "Qwen/Qwen3.8-27B (fine-tune by way of Swift 1.0, Apache 2.0)",
        "params": "27B",
        "context": 262144,
        "goodFor": "A Qwen3.8-27B that thinks in fewer tokens, for coding and agent work with images in, tool calling and adjustable reasoning effort.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Swift-1.5-Qwen3.8-27B-GGUF Q4_K_M",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Swift-1.5-Qwen3.8-27B-Q4_K_M.gguf (17.44 GB) plus mmproj-Swift-1.5-Qwen3.8-27B-F16.gguf (0.93 GB), summed from the Hub's file list"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27B-GGUF IQ4_XS",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 16.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "The smallest 4-bit GGUF, 15.48 GB plus the 0.93 GB vision projector. The repository also has 2- and 3-bit files down to 8.9 GB, and a GSQ-RCO GGUF repository has 2- and 3-bit files from 8.4 GB"
          },
          {
            "name": "Swift-1.5-4bit-MLX",
            "url": "https://huggingface.co/ukisai/Swift-1.5-4bit-MLX",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 15.9,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Weights with the vision tensors, configs and tokenizer, 15.85 GB. Its card says stock MLX-LM 0.32.0 can't load it and ships a patch, and image and video chat aren't implemented"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b-W4A16-AWQ",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-W4A16-AWQ",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 19.6,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "compressed-tensors W4A16. UkisAI also publishes AutoRound INT4 (19.6 GB) and AWQ plus GPTQ INT4 (19.5 GB)"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b-NVFP4",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.9,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 31.3,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "AMD Quark W8A8, with Python loading helpers in the repository"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 55.6,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Swift-1.5-Qwen3.8-27b",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "UkisAI (ukisai)",
        "family": "Swift 1",
        "url": "https://www.anchorterminal.com/open-weights/#ukisai",
        "others": [
          {
            "name": "Swift-Qwen3.8-27b",
            "url": "https://huggingface.co/ukisai/Swift-Qwen3.8-27b",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Swift1.5-Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next",
            "params": "125B (Qwen3.8-Flash-Next's stated size; MoE; 179,999,981,459 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next"
          },
          {
            "name": "Swift-1.5-Ornith-9B",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Ornith-9B",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Swift-Bonsai-2-GGUF",
            "url": "https://huggingface.co/ukisai/Swift-Bonsai-2-GGUF",
            "params": "27B (26,895,998,464 by the Hub's GGUF count)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ukisai.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The licence names UkisAI as licensor with no legal entity. ukisai.com describes a Europe-based applied AI lab in Belgrade, Eindhoven and Wilmington. RDAP wasn't queried.",
          "The weights are published from the Hugging Face user account ukisai (not an organisation), which ukisai.com's Swift page links to.",
          "The Swift Open License stands in for terms of service for the weights. The research API isn't graded, so no privacy policy is scored.",
          "No changelog. The repository's history was squashed to two commits.",
          "ukisai.com/.well-known/security.txt is 404, and ukisai/.github has no SECURITY.md."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Hugging Face",
        "b": "UkisAI",
        "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": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-09-10",
        "b": "2026-09-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "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": "3.3/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "SmolLM3-3B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
        "question": "Which is better for AI agents, SmolLM3-3B or Swift 1.5 Qwen3.8-27B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 80",
          "Risk, 65 against 50",
          "Agent interface, 80 against 60",
          "Documentation and upkeep, 54 against 43"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.3 against 2.3 out of 5"
        ],
        "goodFor": "Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device",
        "slug": "smollm3-3b",
        "watchFor": "65,536 tokens trained; 131,072 needs a YaRN edit to config.json"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.",
        "slug": "swift-1-5-qwen3-8-27b",
        "watchFor": "Commercial use above US$1M gross annual revenue needs a separate licence"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.json",
        "title": "Agents-A1-4B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Agents-A1-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.json",
        "title": "Apertus v1.5 8B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.json",
        "title": "Apodex-1.1-mini vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b.json",
        "title": "BigBang-v1 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b.json",
        "title": "BigBang-v1 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-smollm3-3b.json",
        "title": "Cosmos-Reason2-2B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b.json",
        "title": "Darwin-36B-Opus vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Darwin-36B-Opus vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-smollm3-3b.json",
        "title": "DeepSeek-V3.2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.json",
        "title": "DeepSeek V4.1 Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-smollm3-3b.json",
        "title": "DeepSeek-V4-Flash-0731 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-27b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b.json",
        "title": "DiffusionGemma 26B A4B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b.json",
        "title": "Gemma 4 12B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-smollm3-3b.json",
        "title": "Gemma 4 26B A4B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.json",
        "title": "Gemma 4 31B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.json",
        "title": "Gemma 4 E2B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b.json",
        "title": "GLM-4.7-Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.json",
        "title": "GLM-5.3-Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-smollm3-3b.json",
        "title": "GLM-5.3 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b.json",
        "title": "Granite 4.2 3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b"
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      {
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-smollm3-3b.json",
        "title": "Qwen3.5-4B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json",
        "title": "Qwen3.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.json",
        "title": "Qwen3.6-27B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.json",
        "title": "Qwen3.6-35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b"
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      {
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        "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.json",
        "title": "Qwen3.8-27B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.json",
        "title": "Qwen3.8-Flash-Next vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.json",
        "title": "Qwen3-Coder-Next vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B",
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        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-8B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json",
        "title": "SmolLM3-3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "smollm3-3b",
        "key": "reach",
        "name": "Reach",
        "smollm3-3b": 85,
        "swift-1-5-qwen3-8-27b": 80,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "smollm3-3b",
        "key": "risk",
        "name": "Risk",
        "smollm3-3b": 65,
        "swift-1-5-qwen3-8-27b": 50,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "smollm3-3b",
        "key": "interface",
        "name": "Agent interface",
        "smollm3-3b": 80,
        "swift-1-5-qwen3-8-27b": 60,
        "weight": 10
      },
      {
        "by": 11,
        "edge": "smollm3-3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "smollm3-3b": 54,
        "swift-1-5-qwen3-8-27b": 43,
        "weight": 10
      }
    ],
    "summary": "SmolLM3-3B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "smollm3-3b": "A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.",
      "swift-1-5-qwen3-8-27b": "A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "SmolLM3-3B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.\n\n- SmolLM3-3B: grade C, 61/100, rank #47 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json\n- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #49 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.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### SmolLM3-3B (C)\n\nGood for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device\n\nAhead on:\n- Reach, 85 against 80\n- Risk, 65 against 50\n- Agent interface, 80 against 60\n- Documentation and upkeep, 54 against 43\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.3 out of 5\n\nWatch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json\n\n### Swift 1.5 Qwen3.8-27B (C)\n\nGood for: Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.\n\nWatch for: Commercial use above US$1M gross annual revenue needs a separate licence\n\n\n## Score by category\n\n| Category | Weight | SmolLM3-3B | Swift 1.5 Qwen3.8-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 80 | SmolLM3-3B +5 |\n| Risk | 20% (20 this run) | 65 | 50 | SmolLM3-3B +15 |\n| Agent interface | 10% (10 this run) | 80 | 60 | SmolLM3-3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 54 | 43 | SmolLM3-3B +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | SmolLM3-3B | Swift 1.5 Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Hugging Face | UkisAI |\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 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-09-10 | 2026-09-24 |\n| Terms last updated | 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 | 3.3/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.\n\n**Swift 1.5 Qwen3.8-27B.** A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.\n\n## Before you call either\n\n### SmolLM3-3B\n\n1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text\n2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg\n3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history\n4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens\n5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling\n6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n### Swift 1.5 Qwen3.8-27B\n\n1. Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector\n2. On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base\n4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point\n5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat\n6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates\n7. MTP self-speculative decoding is available with the base's head, flags on the card\n\n## Questions\n\n### Which is better for AI agents, SmolLM3-3B or Swift 1.5 Qwen3.8-27B?\n\nSmolLM3-3B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"smollm3-3b\", \"b\": \"swift-1-5-qwen3-8-27b\"}`. From a terminal: `anchor compare smollm3-3b swift-1-5-qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json\n\n## Other comparisons with SmolLM3-3B or Swift 1.5 Qwen3.8-27B\n\n- [Agents-A1-4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.md)\n- [Agents-A1-4B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-27b.md)\n- [Apertus v1.5 8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.md)\n- [Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b.md)\n- [Apodex-1.1-mini vs SmolLM3-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.md)\n- [Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b.md)\n- 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Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 26B A4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-smollm3-3b.md)\n- [Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.md)\n- [Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-smollm3-3b.md)\n- [Gemma 4 E4B IT vs Swift 1.5 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vs SmolLM3-3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b.md)\n- [gpt-oss-120b vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b.md)\n- [gpt-oss-20b vs SmolLM3-3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-smollm3-3b.md)\n- [gpt-oss-20b vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.md)\n- [Granite 4.2 3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b.md)\n- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Hy3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b.md)\n- [Hy3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.md)\n- [Inkling-Small vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.md)\n- [Inkling-Small vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.md)\n- [Inkling vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.md)\n- [Inkling vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.md)\n- [Kimi K3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-smollm3-3b.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- [Kolibri-1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/kolibri-1-vs-smollm3-3b.md)\n- [Kolibri-1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.md)\n- [Laguna M.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.md)\n- [Laguna M.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.md)\n- [Laguna S 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b.md)\n- [Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.md)\n- [Laguna XS 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.md)\n- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.md)\n- [LFM2.5-2.6B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.md)\n- [LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.md)\n- 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SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.md)\n- [MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.md)\n- [Muse Glimmer 30B vs SmolLM3-3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.md)\n- [Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Nano 30B A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b.md)\n- [Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Nano 4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.md)\n- [Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-smollm3-3b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Super 120B A12B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.md)\n- [Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Ultra 550B A55B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b.md)\n- [Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b.md)\n- [occamy-1.0 vs SmolLM3-3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md)\n- [Olmo 3 7B Think vs SmolLM3-3B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.md)\n- [Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.md)\n- [Ornith-1.5-35B-A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.md)\n- [Ornith-1.5-35B-A3B vs Swift 1.5 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