{ "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", "dns": "minicpm-o-4-5.grade.anchorterminal.com", "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": "2026-11-03" }, "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": "2026-10-24", "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": 1518, "categoryRank": 38, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "oldest": "2026-11-03", "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": [ "minicpm-v-4-7-1b", "minicpm-v-4-7-35b-a3b", "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": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json", "live": { "slug": "minicpm-o-4-5", "securityTxt": { "url": "https://openbmb.cn/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:10.170465559Z" }, "domain": { "domain": "openbmb.cn", "checkedAt": "2026-11-08T14:50:03.275690576Z" }, "pages": [ { "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:38.685365772Z", "changedAt": "2026-11-10T16:23:38.685365772Z", "fingerprint": "0600a4aee37d" } ], "updatedAt": "2026-11-10T16:23:38.685365772Z" } }, "answer": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on risk and agent interface.", "b": { "slug": "step-3-7-flash", "name": "Step 3.7 Flash", "vendor": "StepFun", "vendorUrl": "https://platform.stepfun.ai", "kind": "weights", "category": "open-weights", "summary": "Step 3.7 Flash is StepFun's open-weight vision-language model, 198B parameters with about 11B active, with tool calling and a 256K context. Apache 2.0. Its 4-bit GGUF is 105 GB, just over what a 128 GB machine holds.", "url": "https://www.anchorterminal.com/tools/step-3-7-flash", "markdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json", "dns": "step-3-7-flash.grade.anchorterminal.com", "repo": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "license": "Apache-2.0 (metadata and card; no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-GGUF" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-FP8" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 109 GB at IQ4_XS with its projector, 129 GB in NVFP4 or 403 GB in BF16. StepFun's API serves it at $0.20 per million input tokens and $1.15 per million output, per the card, and DeepInfra and Featherless serve it too (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "custom-code", "self-hosted" ], "lastRelease": "2026-06-03", "graded": true, "anchor": { "graded": true, "score": 59.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 73, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 70, "risk": 45, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.", "bestFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision", "strengths": [ "Apache 2.0, ungated", "GGUF, FP8 and NVFP4 builds from StepFun", "Four released runtimes carry it, with Step parsers in vLLM and SGLang", "A 256K context with images in", "Hosted by StepFun and two providers" ], "weaknesses": [ "About 131 GB to run at 4 bits with images, over a 128 GB machine's share", "Every serve command on the card runs the repository's code", "No reply from StepFun on the repository's discussions", "Not changed since 3 June 2026", "No limitations, safety results or training data on the card" ], "agentNotes": [ "On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server", "On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`", "The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code", "For llama.cpp load the mmproj file from StepFun's GGUF repository for image input", "StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`", "The same model is on our StepFun API listing, with prices and terms" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.8 } ], "editorialScores": { "interface": 85, "reach": 70, "risk": 45, "upkeep": 39 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --model-path stepfun-ai/Step-3.7-Flash-FP8 --tp 8 --ep 4 --reasoning-parser step3p5 --tool-call-parser step3p5 --enable-multimodal --trust-remote-code", "http": "# SGLang and vLLM serve an OpenAI-compatible API. StepFun's own API is at https://api.stepfun.ai/v1\n# (https://api.stepfun.com/v1 for keys issued in China)" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/step-3-7-flash" }, "sameModel": [ "stepfun-api" ], "weights": { "trainedBy": "StepFun", "params": "198B", "active": "11B", "context": 262144, "goodFor": "Agent work that mixes images, search and code, on a GPU server or through StepFun's API. It's a few GB too big for a 128 GB machine at 4 bits.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Step-3.7-Flash-GGUF IQ4_XS", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "iq4_xs", "sizeGB": 108.96, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "104.99 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash-GGUF Q4_K_S", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "q4_k_s", "sizeGB": 115.47, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "111.50 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash-NVFP4", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 129.25, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "StepFun's 4-bit build for NVIDIA GPUs" }, { "name": "Step-3.7-Flash-FP8", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 212.53, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "StepFun's FP8 build" }, { "name": "Step-3.7-Flash-GGUF Q8_0", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 213.39, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "209.42 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash (BF16 safetensors)", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "who": "publisher", "precision": "bf16", "sizeGB": 402.73, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "26 files" } ], "run": "llama-server -hf stepfun-ai/Step-3.7-Flash-GGUF:IQ4_XS --jinja", "runBuild": "Step-3.7-Flash-GGUF IQ4_XS", "checked": "2026-11-10", "minMemoryGB": 192 }, "family": { "lab": "StepFun", "family": "Step 3", "url": "https://www.anchorterminal.com/open-weights/#stepfun", "others": [ { "name": "Step-3.5-Flash", "url": "https://huggingface.co/stepfun-ai/Step-3.5-Flash" }, { "name": "Step3-VL-10B", "url": "https://huggingface.co/stepfun-ai/Step3-VL-10B" } ] }, "area": "models", "provenance": { "legalEntity": "STEPFUN TECHNOLOGY (HK) LIMITED", "domain": "stepfun.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/stepfun-ai/Step-3.7-Flash/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "StepFun's legal entity, domain and security.txt are carried from our StepFun API listing.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The StepFun API that serves this model has its own listing.", "The changelog is the commit history, 23 commits from 23 May to 3 June 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.json" }, "edges": [ { "by": 50, "category": "interface", "leads": "step-3-7-flash", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code.", "b": "Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 35, "b": 85 } }, { "by": 27, "category": "upkeep", "leads": "minicpm-o-4-5", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies.", "b": "Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39." }, "scores": { "a": 66, "b": 39 } }, { "by": 20, "category": "reach", "leads": "minicpm-o-4-5", "name": "Reach", "reasons": { "a": "Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it.", "b": "Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70." }, "scores": { "a": 90, "b": 70 } }, { "by": 5, "category": "risk", "leads": "step-3-7-flash", "name": "Risk", "reasons": { "a": "File safety 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code. Provenance 15, the files are in the openbmb organisation OpenBMB's GitHub links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only, nothing about voice cloning misuse, prompt injection or refusals. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt, policy or advisories.", "b": "File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45." }, "scores": { "a": 40, "b": 45 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenBMB", "b": "StepFun", "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": "Apache-2.0 (metadata and card; no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-24", "b": "2026-06-03", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on risk and agent interface.", "question": "Which is better for AI agents, MiniCPM-o 4.5 or Step 3.7 Flash?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 70", "Documentation and upkeep, 66 against 39" ], "also": null, "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, 45 against 40", "Agent interface, 85 against 35" ], "also": null, "goodFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision", "slug": "step-3-7-flash", "watchFor": "About 131 GB to run at 4 bits with images, over a 128 GB machine's share" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-32B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-4B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-8B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash.json", "title": "Qwopus3.8-27B-Flash-V2 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash.json", "title": "Recursive Flint vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.json", "title": "Ripple-65B Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json", "title": "SenseNova-Loop-9B-Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.json", "title": "SmolLM3-3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json", "title": "Spark-X2.6-4B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.json", "title": "Step 3.7 Flash vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json", "title": "Step 3.7 Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "minicpm-o-4-5", "key": "reach", "minicpm-o-4-5": 90, "name": "Reach", "step-3-7-flash": 70, "weight": 25 }, { "by": 5, "edge": "step-3-7-flash", "key": "risk", "minicpm-o-4-5": 40, "name": "Risk", "step-3-7-flash": 45, "weight": 20 }, { "by": 50, "edge": "step-3-7-flash", "key": "interface", "minicpm-o-4-5": 35, "name": "Agent interface", "step-3-7-flash": 85, "weight": 10 }, { "by": 27, "edge": "minicpm-o-4-5", "key": "upkeep", "minicpm-o-4-5": 66, "name": "Documentation and upkeep", "step-3-7-flash": 39, "weight": 10 } ], "summary": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on risk and agent interface. Both do open-weight models you run yourself.", "tasks": null, "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.", "step-3-7-flash": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash", "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.min.md" }, "markdown": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash 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 #48 of 160 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- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.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 6359 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 581 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 70\n- Documentation and upkeep, 66 against 39\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### Step 3.7 Flash (C)\n\nGood for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision\n\nAhead on:\n- Risk, 45 against 40\n- Agent interface, 85 against 35\n\nWatch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share\n\n\n## Why the edge\n\n### Agent interface: Step 3.7 Flash +50\n\n- MiniCPM-o 4.5 (35): Chat template 20, a Jinja template in the tokenizer config. Tool-calling format 10 of 25, only the inherited Qwen3 template says how tools work. Trained tool calling 0, OpenBMB never mentions tool use. Structured output 0, not documented. Reasoning control 0, thinking turns on and off per request but nobody says where the reasoning comes back. Context 5, 40,960 tokens. Read as an omni model, it takes images, video and audio and returns text and streaming speech, which the card documents with code.\n- Step 3.7 Flash (85): Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Documentation and upkeep: MiniCPM-o 4.5 +27\n\n- MiniCPM-o 4.5 (66): Model card 11 of 20, limitations, context length and a knowledge cut-off are missing. Training data 5, described by kind without sources, sizes or a cut-off. Last change 20, the card changed on 24 October 2026. Responsiveness 10, OpenBMB answered half of the 12 questions. Version policy 10, earlier models stay in their own repositories and releases are dated. File health 10, the files agree and the two bug reports have replies.\n- Step 3.7 Flash (39): Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39.\n\n### Reach: MiniCPM-o 4.5 +20\n\n- MiniCPM-o 4.5 (90): Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a fifth added. Runtimes 15 of 25, released vLLM, llama.cpp and Ollama run it, while Transformers 5.22.1 still needs OpenBMB's remote code and SGLang 0.5.23 supports only the older 2.6. Builds 15, GGUF at several precisions plus AWQ and GPTQ. Terms 15, plain Apache 2.0. Download 10, nothing gated. Hosted 5, OpenBMB's own API serves it.\n- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.\n\n### Risk: Step 3.7 Flash +5\n\n- MiniCPM-o 4.5 (40): File safety 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code. Provenance 15, the files are in the openbmb organisation OpenBMB's GitHub links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only, nothing about voice cloning misuse, prompt injection or refusals. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt, policy or advisories.\n- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n\n## Score by category\n\n| Category | Weight | MiniCPM-o 4.5 | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 70 | MiniCPM-o 4.5 +20 |\n| Risk | 20% (20 this run) | 40 | 45 | Step 3.7 Flash +5 |\n| Agent interface | 10% (10 this run) | 35 | 85 | Step 3.7 Flash +50 |\n| Documentation and upkeep | 10% (10 this run) | 66 | 39 | MiniCPM-o 4.5 +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-o 4.5 | Step 3.7 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | StepFun |\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 | Apache-2.0 (metadata and card; no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-24 | 2026-06-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\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**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.\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### Step 3.7 Flash\n\n1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server\n2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`\n3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code\n4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input\n5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`\n6. The same model is on our StepFun API listing, with prices and terms\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-o 4.5 or Step 3.7 Flash?\n\nMiniCPM-o 4.5 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash 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-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-o-4-5\", \"b\": \"step-3-7-flash\"}`. From a terminal: `anchor compare minicpm-o-4-5 step-3-7-flash`\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/step-3-7-flash.json\n\n## Other comparisons with MiniCPM-o 4.5 or Step 3.7 Flash\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 Step 3.7 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash.md)\n- [Arete-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5.md)\n- [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md)\n- [BigBang-v1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5.md)\n- [BigBang-v1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash.md)\n- [Carbon-X vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-o-4-5.md)\n- [Carbon-X vs Step 3.7 Flash](https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash.md)\n- [Cola2 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/cola2-vs-minicpm-o-4-5.md)\n- [Cola2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash.md)\n- [Cosmos-Reason2-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-o-4-5.md)\n- [Cosmos-Reason2-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash.md)\n- [Darwin-36B-Opus vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-o-4-5.md)\n- [Darwin-36B-Opus vs Step 3.7 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash.md)\n- [DeepSeek-V3.2 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-o-4-5.md)\n- [DeepSeek-V3.2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-step-3-7-flash.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.md)\n- [DeepSeek V4.1 Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash.md)\n- [DeepSeek-V4-Flash-0731 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-o-4-5.md)\n- [DeepSeek-V4-Flash-0731 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-step-3-7-flash.md)\n- [Devstral Small 2 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-o-4-5.md)\n- [Devstral Small 2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.md)\n- [DiffusionGemma 26B A4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-o-4-5.md)\n- [DiffusionGemma 26B A4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash.md)\n- [EXAONE 4.5 33B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.md)\n- [EXAONE 4.5 33B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash.md)\n- [Gemma 4 12B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 12B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-step-3-7-flash.md)\n- [Gemma 4 26B A4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 26B A4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-step-3-7-flash.md)\n- [Gemma 4 31B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 31B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash.md)\n- [Gemma 4 E2B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 E2B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash.md)\n- [Gemma 4 E4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 E4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-step-3-7-flash.md)\n- [GLM-4.5-Air vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.md)\n- [GLM-4.5-Air vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-step-3-7-flash.md)\n- [GLM-4.7-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.md)\n- [GLM-4.7-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-step-3-7-flash.md)\n- [GLM-5.3-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-o-4-5.md)\n- [GLM-5.3-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-step-3-7-flash.md)\n- [GLM-5.3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5.md)\n- [GLM-5.3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-step-3-7-flash.md)\n- [gpt-oss-120b vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-o-4-5.md)\n- [gpt-oss-120b vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-step-3-7-flash.md)\n- [gpt-oss-20b vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5.md)\n- [gpt-oss-20b vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-step-3-7-flash.md)\n- [Granite 4.2 30B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-o-4-5.md)\n- [Granite 4.2 30B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash.md)\n- [Granite 4.2 3B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5.md)\n- [Granite 4.2 3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash.md)\n- [Granite 4.2 8B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-o-4-5.md)\n- [Granite 4.2 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash.md)\n- [Hy-Memory-30B-A3B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-o-4-5.md)\n- [Hy-Memory-30B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.md)\n- [Hy3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.md)\n- [Hy3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy3-vs-step-3-7-flash.md)\n- [Inkling-Small vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.md)\n- [Inkling-Small vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-small-vs-step-3-7-flash.md)\n- [Inkling vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.md)\n- [Inkling vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-vs-step-3-7-flash.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash.md)\n- [Kimi K3.1 Pro vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-o-4-5.md)\n- [Kimi K3.1 Pro vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.md)\n- [Kimi K3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.md)\n- [Kimi K3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.md)\n- [Kolibri-1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.md)\n- [Kolibri-1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.md)\n- [Kronos vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5.md)\n- [Kronos vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.md)\n- [Laguna M.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.md)\n- [Laguna M.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-m-1-vs-step-3-7-flash.md)\n- [Laguna S 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.md)\n- [Laguna S 2.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-step-3-7-flash.md)\n- [Laguna XS 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5.md)\n- [Laguna XS 2.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-step-3-7-flash.md)\n- [LBS-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.md)\n- [LBS-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash.md)\n- [LFM2.5-2.6B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md)\n- [LFM2.5-2.6B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-step-3-7-flash.md)\n- [LFM3-1.7B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5.md)\n- [LFM3-1.7B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash.md)\n- [LFM3-Audio-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-o-4-5.md)\n- [LFM3-Audio-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash.md)\n- [LFM3-Omni-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-o-4-5.md)\n- [LFM3-Omni-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash.md)\n- [LFM3-VL-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-o-4-5.md)\n- [LFM3-VL-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.md)\n- [Ling-3.0-tiny vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5.md)\n- [Ling-3.0-tiny vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-step-3-7-flash.md)\n- [Ling-3.1-flash-1019 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-o-4-5.md)\n- [Ling-3.1-flash-1019 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-step-3-7-flash.md)\n- [LongCat-2.5 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-o-4-5.md)\n- [LongCat-2.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5.md)\n- [MiMo-V2.6-Flash-RL vs Step 3.7 Flash](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.md)\n- [MiniCPM-o 4.5 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm-v-4-7-1b.md)\n- [MiniCPM-o 4.5 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiniCPM-o 4.5 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm5-2b.md)\n- [MiniCPM-o 4.5 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5.md)\n- [MiniCPM-o 4.5 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.md)\n- [MiniCPM-o 4.5 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m3-1-flash.md)\n- [MiniCPM-o 4.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.md)\n- [MiniCPM-o 4.5 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.md)\n- [MiniCPM-o 4.5 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nanbeige-4-2-3b.md)\n- [MiniCPM-o 4.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nanbeige-4-3-33b.md)\n- [MiniCPM-o 4.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-super-120b-a12b.md)\n- [MiniCPM-o 4.5 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiniCPM-o 4.5 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.md)\n- [MiniCPM-o 4.5 vs occamy-1.0](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0.md)\n- [MiniCPM-o 4.5 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-olmo-3-7b-think.md)\n- [MiniCPM-o 4.5 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b.md)\n- [MiniCPM-o 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"name": "MiniCPM-o 4.5 vs Step 3.7 Flash", "url": "" } ], "description": "MiniCPM-o 4.5 scores 61 (C) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. 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