{
  "data": {
    "a": {
      "slug": "qwen-agentworld-35b-a3b",
      "name": "Qwen-AgentWorld-35B-A3B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json",
      "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen-AgentWorld-35B-A3B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit GGUF (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/Qwen/Qwen-AgentWorld-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "world-model",
        "agent-simulation",
        "reasoning",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-06-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.1,
        "grade": "D",
        "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": 104,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 50,
          "reach": 55,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.",
        "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine",
        "strengths": [
          "Simulates seven kinds of agent environment in one model, with the system prompts for each published",
          "Apache 2.0, ungated, safetensors only",
          "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases",
          "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF",
          "A 262,144-token context for long interaction histories"
        ],
        "weaknesses": [
          "Not a general assistant, and the card doesn't describe using it to call tools",
          "No quantised build from Alibaba, so the GGUF route depends on a third party",
          "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026",
          "No limitations, safety results or security contact on the card",
          "Its results are on Alibaba's own benchmark"
        ],
        "agentNotes": [
          "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation",
          "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights",
          "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends",
          "Keep at least 128K of context, since the simulation depends on the history",
          "Thinking is on by default and comes back through the qwen3 reasoning parser",
          "A simulated observation is a prediction. Check anything that matters against the real environment"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.1
          }
        ],
        "editorialScores": {
          "interface": 50,
          "reach": 55,
          "risk": 55,
          "upkeep": 41
        },
        "provenanceScore": 66,
        "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": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n  --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen-agentworld-35b-a3b"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen)",
        "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata",
        "params": "34.7B",
        "active": "3B",
        "context": 262144,
        "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 22.13,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF",
            "who": "unsloth",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 69.32,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "The only build Alibaba publishes. Language weights only"
          }
        ],
        "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja",
        "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen-AgentWorld",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "olderCount": 109
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (named in the licence's copyright line)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.",
          "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November."
        ],
        "score": 66
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json"
    },
    "answer": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk.",
    "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": 35,
        "category": "interface",
        "leads": "step-3-7-flash",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.",
          "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": 50,
          "b": 85
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "step-3-7-flash",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.",
          "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": 55,
          "b": 70
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "qwen-agentworld-35b-a3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.",
          "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": 55,
          "b": 45
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Alibaba (Qwen team)",
        "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",
        "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": "2026-06-25",
        "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"
      }
    ],
    "faq": [
      {
        "answer": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk.",
        "question": "Which is better for AI agents, Qwen-AgentWorld-35B-A3B or Step 3.7 Flash?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 55 against 45"
        ],
        "also": null,
        "goodFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine",
        "slug": "qwen-agentworld-35b-a3b",
        "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools"
      },
      {
        "aheadOn": [
          "Reach, 70 against 55",
          "Agent interface, 85 against 50"
        ],
        "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": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Agents-A1-4B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.json",
        "title": "Agents-A1-4B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.json",
        "title": "Apertus v1.5 8B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.json",
        "title": "Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash.json",
        "title": "Apodex-1.1-mini vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Arete-27B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.json",
        "title": "Arete-27B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b.json",
        "title": "BigBang-v1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash.json",
        "title": "BigBang-v1 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.json",
        "title": "Carbon-X vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash.json",
        "title": "Carbon-X vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b.json",
        "title": "Cola2 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash.json",
        "title": "Cola2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Cosmos-Reason2-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash.json",
        "title": "Cosmos-Reason2-2B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.json",
        "title": "Darwin-36B-Opus vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash.json",
        "title": "Darwin-36B-Opus vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b.json",
        "title": "DeepSeek-V3.2 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-step-3-7-flash"
      },
      {
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        "title": "DeepSeek V4.1 Flash vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash"
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      {
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen-agentworld-35b-a3b"
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      {
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash"
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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-step-3-7-flash.json",
        "title": "gpt-oss-120b vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b.json",
        "title": "gpt-oss-20b vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-step-3-7-flash.json",
        "title": "gpt-oss-20b vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash.json",
        "title": "Granite 4.2 30B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash.json",
        "title": "Granite 4.2 3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.json",
        "title": "Hy-Memory-30B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy3 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-step-3-7-flash.json",
        "title": "Hy3 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b.json",
        "title": "Inkling-Small vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json",
        "title": "Inkling vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-step-3-7-flash.json",
        "title": "Inkling vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.json",
        "title": "Kimi K3.1 Pro vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash"
      },
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.json",
        "title": "Kimi K3 vs Step 3.7 Flash",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kronos vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-step-3-7-flash.json",
        "title": "Laguna M.1 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-1.7B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash.json",
        "title": "LFM3-1.7B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-step-3-7-flash"
      },
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-step-3-7-flash"
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      {
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.json",
        "title": "LongCat-2.5 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-step-3-7-flash"
      },
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen-agentworld-35b-a3b.json",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "step-3-7-flash",
        "key": "reach",
        "name": "Reach",
        "qwen-agentworld-35b-a3b": 55,
        "step-3-7-flash": 70,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "qwen-agentworld-35b-a3b",
        "key": "risk",
        "name": "Risk",
        "qwen-agentworld-35b-a3b": 55,
        "step-3-7-flash": 45,
        "weight": 20
      },
      {
        "by": 35,
        "edge": "step-3-7-flash",
        "key": "interface",
        "name": "Agent interface",
        "qwen-agentworld-35b-a3b": 50,
        "step-3-7-flash": 85,
        "weight": 10
      },
      {
        "by": 2,
        "edge": "qwen-agentworld-35b-a3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen-agentworld-35b-a3b": 41,
        "step-3-7-flash": 39,
        "weight": 10
      }
    ],
    "summary": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk. Both do open-weight models you run yourself.",
    "tasks": null,
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    "verdicts": {
      "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.",
      "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."
    }
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  "markdown": "Step 3.7 Flash scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk. Both do open-weight models you run yourself.\n\n- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.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\n## Which one, for what\n\n### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nAhead on:\n- Risk, 55 against 45\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\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- Reach, 70 against 55\n- Agent interface, 85 against 50\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 +35\n\n- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\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### Reach: Step 3.7 Flash +15\n\n- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\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: Qwen-AgentWorld-35B-A3B +10\n\n- Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.\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 | Qwen-AgentWorld-35B-A3B | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 70 | Step 3.7 Flash +15 |\n| Risk | 20% (20 this run) | 55 | 45 | Qwen-AgentWorld-35B-A3B +10 |\n| Agent interface | 10% (10 this run) | 50 | 85 | Step 3.7 Flash +35 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 39 | Qwen-AgentWorld-35B-A3B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.1 · D** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen-AgentWorld-35B-A3B | Step 3.7 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | 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 | Apache-2.0 (metadata and card; no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-06-25 | 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\n## Verdicts\n\n**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\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### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\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, Qwen-AgentWorld-35B-A3B or Step 3.7 Flash?\n\nStep 3.7 Flash scores 59.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen-agentworld-35b-a3b\", \"b\": \"step-3-7-flash\"}`. 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