{ "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": "occamy-1.0 scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 3 of 4 scored categories.", "b": { "slug": "occamy-1-0", "name": "occamy-1.0", "vendor": "Accio (Accio-Lab)", "vendorUrl": "https://accio-lab.github.io/occamy/", "kind": "weights", "category": "open-weights", "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/occamy-1-0", "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json", "repo": "https://huggingface.co/Accio-Lab/occamy-1.0", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-FP8" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-NVFP4" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-APEX-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-4bit" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-mxfp4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are 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, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (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/Accio-Lab/occamy-1.0", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agent", "coding", "derivative", "apache-2.0", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-02", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 44, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 85, "risk": 55, "upkeep": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.", "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "strengths": [ "Apache 2.0 like its base, with nothing gated", "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB", "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, vLLM, SGLang and Transformers run it", "A technical report, the supervised data mix with token counts and a published data subset", "MD5 and SHA-256 checksums for every file" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "No hosted API or inference provider", "Tool-calling format and structured output aren't documented beyond the template and parser name", "Languages and a knowledge cut-off aren't stated", "The MLX builds await Apple Metal acceptance, per Accio's own cards" ], "agentNotes": [ "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later", "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism", "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says", "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5", "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata", "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 85, "risk": 55, "upkeep": 74 }, "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 Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/occamy-1-0" }, "weights": { "trainedBy": "Accio (Accio-Lab)", "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "occamy-1.0-MLX-mxfp4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 18.4, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-MLX-4bit", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 19.5, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-IQ4_XS", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 18.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card" }, { "name": "occamy-1.0-APEX-Compact", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf", "who": "publisher", "precision": "mixed-q3-q4", "sizeGB": 16.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line" }, { "name": "occamy-1.0-NVFP4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors", "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card" }, { "name": "occamy-1.0-Q4_K_M", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0-FP8", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 36.6, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "occamy-1.0-Q8_0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0", "who": "publisher", "precision": "bf16", "sizeGB": 70.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "occamy-1.0", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Accio (Accio-Lab)", "family": "occamy", "url": "https://www.anchorterminal.com/open-weights/#accio-lab", "others": [ { "name": "occamy-1.0-MTP", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP", "params": "not stated (draft head only)" } ], "olderCount": 4, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "accio-lab.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.", "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "The Endless Frontier (endless-frontier)", "b": "Accio (Accio-Lab)", "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-06", "b": "2026-10-02", "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": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "occamy-1.0 scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, BigBang-v1 or occamy-1.0?" } ], "goodFor": [ { "aheadOn": null, "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, 85 against 55", "Agent interface, 70 against 60", "Documentation and upkeep, 74 against 46" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.3 out of 5" ], "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "slug": "occamy-1-0", "watchFor": "No safety results, prompt-injection guidance or security contact" } ], "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", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.json", "title": "Agents-A1-4B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json", "title": "Apertus v1.5 8B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json", "title": "Apertus v1.5 8B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.json", "title": "Apodex-1.1-mini vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.json", "title": "Apodex-1.1-mini vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b.json", "title": "BigBang-v1 vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b" }, { "json": 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"title": "GLM-4.5-Air vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.json", "title": "GLM-4.7-Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.json", "title": "GLM-5.3-Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.json", "title": "GLM-5.3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0.json", "title": "gpt-oss-120b vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.json", "title": "gpt-oss-20b vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.json", "title": "Granite 4.2 3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.json", "title": "Hy3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0.json", "title": "Inkling-Small vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.json", "title": "Inkling vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json", "title": "Kimi K3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.json", "title": "Kolibri-1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json", "title": "Laguna M.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.json", "title": "Laguna S 2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0" }, { "json": 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"https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-occamy-1-0.json", "title": "MiniCPM5-2B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-occamy-1-0.json", "title": "MiniMax-M2.5 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.json", "title": "MiniMax-M2.7 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0.json", "title": "Muse Glimmer 30B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0.json", "title": "Nemotron 3.5 Lightning 30B A3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-occamy-1-0.json", "title": "Nemotron 3 Nano 30B A3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0.json", "title": "Nemotron 3 Nano 4B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-occamy-1-0.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-occamy-1-0.json", "title": "Nemotron 3 Super 120B A12B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-occamy-1-0.json", "title": "Nemotron 3 Ultra 550B A55B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-olmo-3-7b-think.json", "title": "occamy-1.0 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b.json", "title": "occamy-1.0 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.json", "title": "occamy-1.0 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b.json", "title": "occamy-1.0 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-30b-a3b-instruct-2507.json", "title": "occamy-1.0 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-4b-instruct-2507.json", "title": "occamy-1.0 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b.json", "title": "occamy-1.0 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b.json", "title": "occamy-1.0 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b.json", "title": "occamy-1.0 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b.json", "title": "occamy-1.0 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b.json", "title": "occamy-1.0 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json", "title": "occamy-1.0 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b.json", "title": "occamy-1.0 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.json", "title": "occamy-1.0 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.json", "title": "occamy-1.0 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json", "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.json", "title": "occamy-1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.json", "title": "occamy-1.0 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "bigbang-v1": 55, "by": 30, "edge": "occamy-1-0", "key": "reach", "name": "Reach", "occamy-1-0": 85, "weight": 25 }, { "bigbang-v1": 55, "by": 0, "edge": "", "key": "risk", "name": "Risk", "occamy-1-0": 55, "weight": 20 }, { "bigbang-v1": 60, "by": 10, "edge": "occamy-1-0", "key": "interface", "name": "Agent interface", "occamy-1-0": 70, "weight": 10 }, { "bigbang-v1": 46, "by": 28, "edge": "occamy-1-0", "key": "upkeep", "name": "Documentation and upkeep", "occamy-1-0": 74, "weight": 10 } ], "summary": "occamy-1.0 scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 3 of 4 scored categories. 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.", "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0", "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.min.md" }, "markdown": "occamy-1.0 scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 3 of 4 scored categories. 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- occamy-1.0: grade C, 61/100, rank #51 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.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\nWatch for: BF16 is the only build from the publisher, needing about 86 GB\n\n### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Reach, 85 against 55\n- Agent interface, 70 against 60\n- Documentation and upkeep, 74 against 46\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Score by category\n\n| Category | Weight | BigBang-v1 | occamy-1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 85 | occamy-1.0 +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 70 | occamy-1.0 +10 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 74 | occamy-1.0 +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.4 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | BigBang-v1 | occamy-1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | The Endless Frontier (endless-frontier) | Accio (Accio-Lab) |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-06 | 2026-10-02 |\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) | 3/5 (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**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\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### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n\n## Questions\n\n### Which is better for AI agents, BigBang-v1 or occamy-1.0?\n\noccamy-1.0 scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bigbang-v1\", \"b\": \"occamy-1-0\"}`. From a terminal: `anchor compare bigbang-v1 occamy-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n\n## Other comparisons with BigBang-v1 or occamy-1.0\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 occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [BigBang-v1 vs 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Spark-X2.6-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.md)\n- [occamy-1.0 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", 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