{ "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", "dns": "bigbang-v1.grade.anchorterminal.com", "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": 56.3, "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": 87, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 60, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "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, Ollama 0.40.14, 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, and no change to the files or card since 6 August 2026", "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 Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only", "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.3 } ], "editorialScores": { "interface": 60, "reach": 60, "risk": 55, "upkeep": 46 }, "provenanceScore": 19, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "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\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/endless-frontier_BigBang-v1-GGUF:Q4_K_M --jinja", "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", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "bartowski endless-frontier_BigBang-v1-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 22.76, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Endless Frontier's. 21.86 GB of Q4_K_M weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "bartowski endless-frontier_BigBang-v1-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 38.71, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Endless Frontier's. 37.81 GB of Q8_0 weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "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": "2026-11-04", "minMemoryGB": 32 }, "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": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/bigbang-v1.json" }, "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk.", "b": { "slug": "mimo-v2-6-flash-rl", "name": "MiMo-V2.6-Flash-RL", "vendor": "Xiaomi (MiMo team)", "vendorUrl": "https://mimo.xiaomi.com", "kind": "weights", "category": "open-weights", "summary": "MiMo-V2.6-Flash-RL is Xiaomi's open-weight agent model, a mixture of experts of 309B parameters with 15B active, taking text, images, video and audio with a 1M context. MIT. The weights are 178 GB at 4 bits, so it needs a server.", "url": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl", "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json", "dns": "mimo-v2-6-flash-rl.grade.anchorterminal.com", "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "license": "MIT (metadata only, no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "XiaomiMiMo/MiMo-V2.6-Flash-RL" } ], "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 under MIT, per the card's metadata. Running it yourself needs a multi-GPU server: the files are 177.7 GB with most weights at 4 bits. Xiaomi's API platform and Novita serve it for those without the hardware (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/XiaomiMiMo/MiMo-V2.6-Flash-RL", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "audio", "video", "reasoning", "tool-calling", "agentic", "long-context", "mit", "safetensors", "fp4", "custom-code", "self-hosted" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 59, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 45, "upkeep": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.", "bestFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context", "strengths": [ "MIT, ungated, with hosted options", "Four released runtimes carry it, with tool and reasoning parsers in vLLM and SGLang", "A 1M context and four input types", "Trained with reinforcement learning across agent harnesses", "A GGUF from the llama.cpp project's own organisation" ], "weaknesses": [ "178 GB at 4 bits, so it needs about 200 GB of memory", "The card's serve commands pass trust-remote-code", "A pickle file sits in the draft model's folder", "No safety results, limitations or data sources on the card", "Xiaomi replied in a few of the discussions only" ], "agentNotes": [ "Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita", "On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`", "Use temperature 1.0 and top_p 0.95, as the card recommends", "The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in", "Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle", "The Use Restrictions section asks for human oversight of high-risk actions" ], "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": 61 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 45, "upkeep": 59 }, "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 --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo\n# the card's full command adds data-parallel attention and speculative decoding flags", "http": "# SGLang and vLLM serve an OpenAI-compatible API. The card recommends temperature 1.0 and top_p 0.95,\n# and names the mimo tool-call and reasoning parsers for both" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/mimo-v2-6-flash-rl" }, "sameCompany": [ "mimo-v2-6-distill-qwen-9b" ], "weights": { "trainedBy": "Xiaomi (MiMo team)", "params": "309B", "active": "15B", "context": 1048576, "goodFor": "Long agent runs over code, tools, screens and audio on a multi-GPU server, or through a provider.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiMo-V2.6-Flash-RL", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "who": "publisher", "precision": "fp4", "sizeGB": 177.74, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build Xiaomi publishes, with most weights at 4 bits" }, { "name": "ggml-org MiMo-V2.6-Flash-RL-GGUF (MXFP4)", "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF", "who": "llama.cpp", "precision": "mxfp4", "sizeGB": 168.93, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 167.37 GB of MXFP4 weights plus the 1.56 GB Q8_0 projector, from the Hub's model information on 10 November 2026" } ], "run": "sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo", "runBuild": "MiMo-V2.6-Flash-RL", "checked": "2026-11-10", "minMemoryGB": 256 }, "family": { "lab": "Xiaomi (MiMo)", "family": "MiMo-V2 (V2, V2.5, V2.6)", "url": "https://www.anchorterminal.com/open-weights/#xiaomi", "others": [ { "name": "MiMo-V2.6-Distill-Qwen-9B", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B", "params": "9B (Hub safetensors count 9.41B)", "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b" }, { "name": "MiMo-V2.6-Pro-MOPD", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD", "params": "1024.22B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.6-Flash-MOPD", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.6-Pro-RL", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL", "params": "1024.22B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Pro-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base", "params": "1023.18B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Pro", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro", "params": "1023.18B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-ASR", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR", "params": "7.62B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2-Flash-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base", "params": "309.77B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2-Flash", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash", "params": "309.77B (Hub safetensors count; the name states none)" } ], "olderCount": 12 }, "area": "models", "provenance": { "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)", "domain": "mimo.xiaomi.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Xiaomi's legal entity, domain and security facts are carried from our MiMo-V2.6 Distill listing.", "Nothing is hosted under this listing, so MIT stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 13 commits from 21 September to 28 October 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.json" }, "edges": [ { "by": 25, "category": "interface", "leads": "mimo-v2-6-flash-rl", "name": "Agent interface", "reasons": { "a": "Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 60, "b": 85 } }, { "by": 13, "category": "upkeep", "leads": "mimo-v2-6-flash-rl", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.", "b": "Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59." }, "scores": { "a": 46, "b": 59 } }, { "by": 10, "category": "risk", "leads": "bigbang-v1", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45." }, "scores": { "a": 55, "b": 45 } }, { "by": 5, "category": "reach", "leads": "mimo-v2-6-flash-rl", "name": "Reach", "reasons": { "a": "Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.", "b": "Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65." }, "scores": { "a": 60, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "The Endless Frontier (endless-frontier)", "b": "Xiaomi (MiMo team)", "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": "MIT (metadata only, no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-06", "b": "2026-10-28", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk.", "question": "Which is better for AI agents, BigBang-v1 or MiMo-V2.6-Flash-RL?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 45" ], "also": null, "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 60", "Agent interface, 85 against 60", "Documentation and upkeep, 59 against 46" ], "also": null, "goodFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context", "slug": "mimo-v2-6-flash-rl", "watchFor": "178 GB at 4 bits, so it needs about 200 GB of memory" } ], "history": { "bigbang-v1": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 1.9, "from": 54.4, "fromGrade": "C", "to": 56.3, "toGrade": "C" } ], "mimo-v2-6-flash-rl": null }, "job": { 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"https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2.json", "title": "MiMo-V2.6-Flash-RL vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.json", "title": "MiMo-V2.6-Flash-RL vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview.json", "title": "MiMo-V2.6-Flash-RL vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.json", "title": "MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b.json", "title": "MiMo-V2.6-Flash-RL vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b.json", "title": "MiMo-V2.6-Flash-RL vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.json", "title": "MiMo-V2.6-Flash-RL vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b.json", "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b.json", "title": "MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.json", "title": "MiMo-V2.6-Flash-RL vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.json", "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json", "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b.json", "title": "MiMo-V2.6-Flash-RL vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "bigbang-v1": 60, "by": 5, "edge": "mimo-v2-6-flash-rl", "key": "reach", "mimo-v2-6-flash-rl": 65, "name": "Reach", "weight": 25 }, { "bigbang-v1": 55, "by": 10, "edge": "bigbang-v1", "key": "risk", "mimo-v2-6-flash-rl": 45, "name": "Risk", "weight": 20 }, { "bigbang-v1": 60, "by": 25, "edge": "mimo-v2-6-flash-rl", "key": "interface", "mimo-v2-6-flash-rl": 85, "name": "Agent interface", "weight": 10 }, { "bigbang-v1": 46, "by": 13, "edge": "mimo-v2-6-flash-rl", "key": "upkeep", "mimo-v2-6-flash-rl": 59, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk. Both do open-weight models you run yourself.", "tasks": null, "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.", "mimo-v2-6-flash-rl": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl", "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.min.md" }, "markdown": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk. Both do open-weight models you run yourself.\n\n- BigBang-v1: grade C, 56.3/100, rank #116 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json\n- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.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### 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 45\n\nWatch for: BF16 is the only build from the publisher, needing about 86 GB\n\n### MiMo-V2.6-Flash-RL (C)\n\nGood for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context\n\nAhead on:\n- Reach, 65 against 60\n- Agent interface, 85 against 60\n- Documentation and upkeep, 59 against 46\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n\n## Why the edge\n\n### Agent interface: MiMo-V2.6-Flash-RL +25\n\n- BigBang-v1 (60): Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +13\n\n- BigBang-v1 (46): Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.\n- MiMo-V2.6-Flash-RL (59): Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.\n\n### Risk: BigBang-v1 +10\n\n- BigBang-v1 (55): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.\n\n### Reach: MiMo-V2.6-Flash-RL +5\n\n- BigBang-v1 (60): Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.\n- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.\n\n## Score by category\n\n| Category | Weight | BigBang-v1 | MiMo-V2.6-Flash-RL | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 65 | MiMo-V2.6-Flash-RL +5 |\n| Risk | 20% (20 this run) | 55 | 45 | BigBang-v1 +10 |\n| Agent interface | 10% (10 this run) | 60 | 85 | MiMo-V2.6-Flash-RL +25 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 59 | MiMo-V2.6-Flash-RL +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.3 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | BigBang-v1 | MiMo-V2.6-Flash-RL |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | The Endless Frontier (endless-frontier) | Xiaomi (MiMo team) |\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 | MIT (metadata only, no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-06 | 2026-10-28 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## How the scores have moved\n\n### BigBang-v1\n\n- +1.9 on 4 November 2026: correction\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\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**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.\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 Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only\n5. Validate JSON output yourself or use the runtime's guided decoding\n\n### MiMo-V2.6-Flash-RL\n\n1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita\n2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`\n3. Use temperature 1.0 and top_p 0.95, as the card recommends\n4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in\n5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle\n6. The Use Restrictions section asks for human oversight of high-risk actions\n\n## Questions\n\n### Which is better for AI agents, BigBang-v1 or MiMo-V2.6-Flash-RL?\n\nMiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 3 of 4 scored categories. BigBang-v1 leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bigbang-v1\", \"b\": \"mimo-v2-6-flash-rl\"}`. From a terminal: `anchor compare bigbang-v1 mimo-v2-6-flash-rl`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n\n## Other comparisons with BigBang-v1 or MiMo-V2.6-Flash-RL\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 MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.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 MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.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 MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md)\n- [Arete-27B vs 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