{
  "data": {
    "a": {
      "slug": "minicpm-o-4-5",
      "name": "MiniCPM-o 4.5",
      "vendor": "OpenBMB",
      "vendorUrl": "https://openbmb.cn",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json",
      "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5",
      "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-gguf"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-awq"
        },
        {
          "registry": "huggingface",
          "name": "openbmb/MiniCPM-o-4_5-GPTQ"
        },
        {
          "registry": "ollama",
          "name": "openbmb/minicpm-o4.5"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (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://github.com/OpenSQZ/MiniCPM-V-CookBook",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "omni",
        "vision",
        "speech",
        "voice-cloning",
        "full-duplex",
        "reasoning",
        "apache-2.0",
        "safetensors",
        "gguf",
        "awq",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-10-16",
      "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": 35,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.",
        "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.",
        "strengths": [
          "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB",
          "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation",
          "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too",
          "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds",
          "Repository changed 5 days ago and earlier versions stay in their own repositories"
        ],
        "weaknesses": [
          "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni",
          "Two pickle files (flow.pt and hift.pt) in the speech stage",
          "No safety results or guidance, though it clones voices from a short clip",
          "Tool calling, structured output and where the reasoning comes back aren't documented",
          "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions"
        ],
        "agentNotes": [
          "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB",
          "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does",
          "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni",
          "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off",
          "Keep prompts within 40,960 tokens",
          "Don't rely on tool calls or JSON output, neither is documented; validate any you get"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "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": 35,
          "reach": 90,
          "risk": 40,
          "upkeep": 66
        },
        "provenanceScore": 53,
        "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 openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n  --max-model-len 4096 --max-num-batched-tokens 2048",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minicpm-o-4-5"
      },
      "sameCompany": [
        "minicpm5-2b"
      ],
      "weights": {
        "trainedBy": "OpenBMB",
        "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)",
        "params": "9.4B",
        "context": 40960,
        "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.",
        "runtimes": [
          "vLLM",
          "llama.cpp",
          "Ollama"
        ],
        "builds": [
          {
            "name": "MiniCPM-o-4_5-gguf Q4_K_M",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.12,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni"
          },
          {
            "name": "MiniCPM-o-4_5-gguf Q4_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 5.87,
            "memoryGB": 8,
            "format": "gguf",
            "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair"
          },
          {
            "name": "ollama openbmb/minicpm-o4.5:latest",
            "url": "https://ollama.com/openbmb/minicpm-o4.5/tags",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.1,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it"
          },
          {
            "name": "MiniCPM-o-4_5-gguf Q8_0",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.8,
            "memoryGB": 12,
            "format": "gguf",
            "note": "8.71 GB language file plus the 1.10 GB vision file"
          },
          {
            "name": "MiniCPM-o-4_5-GPTQ",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ",
            "who": "publisher",
            "precision": "gptq-int4",
            "sizeGB": 9.77,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs"
          },
          {
            "name": "MiniCPM-o-4_5-awq",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq",
            "who": "publisher",
            "precision": "awq-int4",
            "sizeGB": 13.07,
            "memoryGB": 16,
            "format": "safetensors",
            "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code"
          },
          {
            "name": "MiniCPM-o-4_5-gguf F16",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 17.48,
            "memoryGB": 24,
            "format": "gguf",
            "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input"
          },
          {
            "name": "MiniCPM-o-4_5",
            "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 20.05,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers (remote code)"
            ],
            "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0"
          }
        ],
        "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048",
        "runBuild": "MiniCPM-o-4_5",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "OpenBMB (MiniCPM)",
        "family": "MiniCPM-o 4",
        "url": "https://www.anchorterminal.com/open-weights/#openbmb",
        "olderCount": 22
      },
      "alsoIn": [
        "voice-agents"
      ],
      "area": "models",
      "provenance": {
        "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)",
        "domain": "openbmb.cn",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.",
          "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.",
          "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).",
          "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json"
    },
    "answer": "MiniCPM-o 4.5 and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk and agent interface.",
    "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": "OpenBMB",
        "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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards",
        "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": "2026-10-16",
        "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": "2.8/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniCPM-o 4.5 and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk and agent interface.",
        "question": "Which is better for AI agents, MiniCPM-o 4.5 or Swift 1.5 Qwen3.8-27B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 80",
          "Documentation and upkeep, 66 against 43"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5"
        ],
        "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.",
        "slug": "minicpm-o-4-5",
        "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni"
      },
      {
        "aheadOn": [
          "Risk, 50 against 40",
          "Agent interface, 60 against 35"
        ],
        "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-minicpm-o-4-5.json",
        "title": "Agents-A1-4B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Apertus v1.5 8B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Apodex-1.1-mini vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5.json",
        "title": "BigBang-v1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Cosmos-Reason2-2B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Darwin-36B-Opus vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "DeepSeek-V3.2 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b.json",
        "title": "DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B",
        "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-minicpm-o-4-5.json",
        "title": "DeepSeek V4.1 Flash vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "DeepSeek-V4-Flash-0731 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-27b.json",
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        "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-minicpm-o-4-5.json",
        "title": "DiffusionGemma 26B A4B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Gemma 4 12B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Gemma 4 26B A4B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Gemma 4 31B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "Gemma 4 E2B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "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-minicpm-o-4-5.json",
        "title": "Gemma 4 E4B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-27B",
        "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-minicpm-o-4-5.json",
        "title": "GLM-4.5-Air vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B",
        "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-minicpm-o-4-5.json",
        "title": "GLM-4.7-Flash vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B",
        "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-minicpm-o-4-5.json",
        "title": "GLM-5.3-Flash vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-o-4-5"
      },
      {
        "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-minicpm-o-4-5.json",
        "title": "GLM-5.3 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-5.3 vs Swift 1.5 Qwen3.8-27B",
        "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-minicpm-o-4-5.json",
        "title": "gpt-oss-120b vs MiniCPM-o 4.5",
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        "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"
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        "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-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Inkling vs Swift 1.5 Qwen3.8-27B",
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        "title": "Kimi K3 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json",
        "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "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-1-5-397b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "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-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-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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "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-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b"
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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-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-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-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"
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      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-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": 10,
        "edge": "minicpm-o-4-5",
        "key": "reach",
        "minicpm-o-4-5": 90,
        "name": "Reach",
        "swift-1-5-qwen3-8-27b": 80,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "swift-1-5-qwen3-8-27b",
        "key": "risk",
        "minicpm-o-4-5": 40,
        "name": "Risk",
        "swift-1-5-qwen3-8-27b": 50,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "swift-1-5-qwen3-8-27b",
        "key": "interface",
        "minicpm-o-4-5": 35,
        "name": "Agent interface",
        "swift-1-5-qwen3-8-27b": 60,
        "weight": 10
      },
      {
        "by": 23,
        "edge": "minicpm-o-4-5",
        "key": "upkeep",
        "minicpm-o-4-5": 66,
        "name": "Documentation and upkeep",
        "swift-1-5-qwen3-8-27b": 43,
        "weight": 10
      }
    ],
    "summary": "MiniCPM-o 4.5 and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.",
      "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": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b",
    "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.min.md"
  },
  "markdown": "MiniCPM-o 4.5 and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.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- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nAhead on:\n- Reach, 90 against 80\n- Documentation and upkeep, 66 against 43\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\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\nAhead on:\n- Risk, 50 against 40\n- Agent interface, 60 against 35\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 | MiniCPM-o 4.5 | 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) | 90 | 80 | MiniCPM-o 4.5 +10 |\n| Risk | 20% (20 this run) | 40 | 50 | Swift 1.5 Qwen3.8-27B +10 |\n| Agent interface | 10% (10 this run) | 35 | 60 | Swift 1.5 Qwen3.8-27B +25 |\n| Documentation and upkeep | 10% (10 this run) | 66 | 43 | MiniCPM-o 4.5 +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-o 4.5 | Swift 1.5 Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | 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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | 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 | 2026-10-16 | 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 | 2.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\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### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\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, MiniCPM-o 4.5 or Swift 1.5 Qwen3.8-27B?\n\nMiniCPM-o 4.5 and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-o-4-5\", \"b\": \"swift-1-5-qwen3-8-27b\"}`. From a terminal: `anchor compare minicpm-o-4-5 swift-1-5-qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json\n\n## Other comparisons with MiniCPM-o 4.5 or Swift 1.5 Qwen3.8-27B\n\n- [Agents-A1-4B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [Apodex-1.1-mini vs Swift 1.5 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