{
  "data": {
    "a": {
      "slug": "bigbang-v1",
      "name": "BigBang-v1",
      "vendor": "The Endless Frontier (endless-frontier)",
      "vendorUrl": "https://huggingface.co/endless-frontier",
      "kind": "weights",
      "category": "open-weights",
      "summary": "BigBang-v1 is The Endless Frontier's general-purpose fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active), post-trained on about 10,000 synthetic research and technical problems, published on Hugging Face in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/bigbang-v1",
      "markdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json",
      "repo": "https://huggingface.co/endless-frontier/BigBang-v1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "endless-frontier/BigBang-v1"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 71.9 GB for the BF16 files, the only build from the publisher (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/endless-frontier/BigBang-v1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-08-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.4,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 67,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 55,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.",
        "bestFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.",
        "strengths": [
          "Apache 2.0 like its base, not gated",
          "Same architecture, config and template as Qwen3.6-35B-A3B, so released llama.cpp, vLLM, SGLang and Transformers run it",
          "Safetensors only with a SHA-256 for every weight file",
          "262,144 tokens of context and several tool calls a turn"
        ],
        "weaknesses": [
          "BF16 is the only build from the publisher, needing about 86 GB",
          "No report, safety results, agent guidance or security contact",
          "Training data described only as about 10,000 synthetic examples",
          "No reply from the publisher to any of 7 discussions",
          "Every benchmark figure is the publisher's own, with no set-up stated"
        ],
        "agentNotes": [
          "On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`",
          "The card says to keep at least 128K of context so its thinking isn't cut short",
          "Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output",
          "For a smaller build you'll need someone else's quantisation, the publisher has none",
          "Validate JSON output yourself or use the runtime's guided decoding"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.4
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 55,
          "risk": 55,
          "upkeep": 46
        },
        "provenanceScore": 13,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "vllm serve endless-frontier/BigBang-v1 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"endless-frontier/BigBang-v1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/bigbang-v1"
      },
      "weights": {
        "trainedBy": "The Endless Frontier (endless-frontier)",
        "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text. Identical config.json, parameter count and chat template, and the repository's LICENSE is Qwen's Apache 2.0 file. BigBang is also Apache 2.0",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "General-purpose mixture-of-experts model tuned for scientific research, search and coding tasks with thinking on, text and images in.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "BigBang-v1",
            "url": "https://huggingface.co/endless-frontier/BigBang-v1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "BigBang-v1",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "The Endless Frontier (endless-frontier)",
        "family": "BigBang",
        "url": "https://www.anchorterminal.com/open-weights/#endless-frontier",
        "olderCount": 2,
        "unrestrictedCopies": 3
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity, website, paper or GitHub repository is named on the card. The Hugging Face organisation endless-frontier (fullname The Endless Frontier, 17 members, three models) isn't verified.",
          "Nothing is hosted by the publisher, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The commit messages are uploads and benchmark-table fixes.",
          "No security.txt, since there is no domain."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/bigbang-v1.json"
    },
    "answer": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.",
    "b": {
      "slug": "minimax-m2-5",
      "name": "MiniMax-M2.5",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
      "license": "MiniMax Model License (model) and modified MIT (code)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.5"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-02-12",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.5,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 57,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and 196,608 tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by two inference providers on Hugging Face"
        ],
        "weaknesses": [
          "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach",
          "No safety evaluation, agent guidance or security contact",
          "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache",
          "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends",
          "Budget output tokens for thinking on every turn, since the template always opens a think block",
          "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy",
          "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.5
          }
        ],
        "editorialScores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "provenanceScore": 55,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-5"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-7"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 196608,
        "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.1,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8; the only build MiniMax publishes"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.5",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-7"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax AI (named in the licences; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.",
          "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.",
          "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "The Endless Frontier (endless-frontier)",
        "b": "MiniMax",
        "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": "MiniMax Model License (model) and modified MIT (code)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-06",
        "b": "2026-02-12",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.3/5 (4)",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.",
        "question": "Which is better for AI agents, BigBang-v1 or MiniMax-M2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 55 against 30",
          "Documentation and upkeep, 46 against 22"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.3 against 1.8 out of 5"
        ],
        "goodFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.",
        "slug": "bigbang-v1",
        "watchFor": "BF16 is the only build from the publisher, needing about 86 GB"
      },
      {
        "aheadOn": [
          "Reach, 65 against 55",
          "Agent interface, 75 against 60"
        ],
        "also": null,
        "goodFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "slug": "minimax-m2-5",
        "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json",
        "title": "Agents-A1-4B vs BigBang-v1",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "bigbang-v1": 55,
        "by": 10,
        "edge": "minimax-m2-5",
        "key": "reach",
        "minimax-m2-5": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "bigbang-v1": 55,
        "by": 25,
        "edge": "bigbang-v1",
        "key": "risk",
        "minimax-m2-5": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "bigbang-v1": 60,
        "by": 15,
        "edge": "minimax-m2-5",
        "key": "interface",
        "minimax-m2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "bigbang-v1": 46,
        "by": 24,
        "edge": "bigbang-v1",
        "key": "upkeep",
        "minimax-m2-5": 22,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "bigbang-v1": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.",
      "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5",
    "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.min.md"
  },
  "markdown": "MiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- BigBang-v1: grade C, 54.4/100, rank #83 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json\n- MiniMax-M2.5: grade C, 57.5/100, rank #71 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### BigBang-v1 (C)\n\nGood for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.\n\nAhead on:\n- Risk, 55 against 30\n- Documentation and upkeep, 46 against 22\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.3 against 1.8 out of 5\n\nWatch for: BF16 is the only build from the publisher, needing about 86 GB\n\n### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nAhead on:\n- Reach, 65 against 55\n- Agent interface, 75 against 60\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | BigBang-v1 | MiniMax-M2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 65 | MiniMax-M2.5 +10 |\n| Risk | 20% (20 this run) | 55 | 30 | BigBang-v1 +25 |\n| Agent interface | 10% (10 this run) | 60 | 75 | MiniMax-M2.5 +15 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 22 | BigBang-v1 +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.4 · C** | **57.5 · C** | |\n\n## Facts side by side\n\n| Fact | BigBang-v1 | MiniMax-M2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | The Endless Frontier (endless-frontier) | MiniMax |\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 | MiniMax Model License (model) and modified MIT (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-06 | 2026-02-12 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.3/5 (4) | 1.8/5 (4) |\n\n## Verdicts\n\n**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.\n\n**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\n\n## Before you call either\n\n### BigBang-v1\n\n1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n2. The card says to keep at least 128K of context so its thinking isn't cut short\n3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output\n4. For a smaller build you'll need someone else's quantisation, the publisher has none\n5. Validate JSON output yourself or use the runtime's guided decoding\n\n### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, BigBang-v1 or MiniMax-M2.5?\n\nMiniMax-M2.5 scores 57.5 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bigbang-v1\", \"b\": \"minimax-m2-5\"}`. From a terminal: `anchor compare bigbang-v1 minimax-m2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n\n## Other comparisons with BigBang-v1 or MiniMax-M2.5\n\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs BigBang-v1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs BigBang-v1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [BigBang-v1 vs 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