{ "data": { "a": { "slug": "agents-a1-4b", "name": "Agents-A1-4B", "vendor": "InternScience (Shanghai AI Laboratory)", "vendorUrl": "https://internscience.github.io/Agents-A1/", "kind": "weights", "category": "open-weights", "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/agents-a1-4b", "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json", "repo": "https://huggingface.co/InternScience/Agents-A1-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "InternScience/Agents-A1-4B" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-FP8" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q8_0-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-F16-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (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/InternScience/Agents-A1-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-07-14", "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": 27, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "strengths": [ "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model", "FP8 and three GGUF precisions from the publisher", "Apache 2.0 through the chain, with no gate", "Trained for tool calling, several calls a turn, and 262,144 tokens of context", "An evaluation framework for its agent benchmarks is published on GitHub" ], "weaknesses": [ "The base isn't declared, and the technical report doesn't cover the 4B", "No safety results, agent guidance or security contact", "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14", "Tool calling and structured output aren't documented beyond a parser name", "InternScience answered one of three discussions, and the history was squashed" ], "agentNotes": [ "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector", "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work", "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14", "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding", "Validate JSON output yourself, since structured output isn't documented" ], "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": 90, "risk": 55, "upkeep": 43 }, "provenanceScore": 40, "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": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --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\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/agents-a1-4b" }, "weights": { "trainedBy": "InternScience (Shanghai AI Laboratory)", "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B", "params": "4.5B", "context": 262144, "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Agents-A1-4B-Q8_0", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 5.2, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" }, { "name": "Agents-A1-4B-Q4_K_M", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.4, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.7 GB of weights and the 0.7 GB vision projector" }, { "name": "Agents-A1-4B", "url": "https://huggingface.co/InternScience/Agents-A1-4B", "who": "publisher", "precision": "bf16", "sizeGB": 9.1, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Agents-A1-4B-FP8", "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 7.8, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Agents-A1-4B-F16", "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF", "who": "publisher", "precision": "f16", "sizeGB": 9.1, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" } ], "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Agents-A1-4B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "InternScience (Shanghai AI Laboratory)", "family": "Agents-A1", "url": "https://www.anchorterminal.com/open-weights/#internscience", "others": [ { "name": "Agents-A1", "url": "https://huggingface.co/InternScience/Agents-A1", "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template", "domain": "internscience.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.", "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.", "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json" }, "answer": "Agents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "InternScience (Shanghai AI Laboratory)", "b": "The Endless Frontier (endless-frontier)", "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": "2026-07-14", "b": "2026-08-06", "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": "3/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Agents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories.", "question": "Which is better for AI agents, Agents-A1-4B or BigBang-v1?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 55", "Agent interface, 70 against 60" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.3 out of 5" ], "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "slug": "agents-a1-4b", "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B" }, { "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json", "title": "Agents-A1-4B vs Apertus v1.5 8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json", "title": "Agents-A1-4B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini" }, { "json": 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"json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b.json", "title": "BigBang-v1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b.json", "title": "BigBang-v1 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.json", "title": "BigBang-v1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0" }, { "json": 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"https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-4b-instruct-2507.json", "title": "BigBang-v1 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-0-8b.json", "title": "BigBang-v1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b.json", "title": "BigBang-v1 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b.json", "title": "BigBang-v1 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-397b-a17b.json", "title": "BigBang-v1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-4b.json", "title": "BigBang-v1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.json", "title": "BigBang-v1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-27b.json", "title": "BigBang-v1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b.json", "title": "BigBang-v1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b.json", "title": "BigBang-v1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next.json", "title": "BigBang-v1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "BigBang-v1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next.json", "title": "BigBang-v1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct.json", "title": 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"https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b.json", "title": "BigBang-v1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "BigBang-v1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.json", "title": "BigBang-v1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "agents-a1-4b": 90, "bigbang-v1": 55, "by": 35, "edge": "agents-a1-4b", "key": "reach", "name": "Reach", "weight": 25 }, { "agents-a1-4b": 55, "bigbang-v1": 55, "by": 0, "edge": "", "key": "risk", "name": "Risk", "weight": 20 }, { "agents-a1-4b": 70, "bigbang-v1": 60, "by": 10, "edge": "agents-a1-4b", "key": "interface", "name": "Agent interface", "weight": 10 }, { "agents-a1-4b": 43, "bigbang-v1": 46, "by": 3, "edge": "bigbang-v1", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Agents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1", "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.min.md" }, "markdown": "Agents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json\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- 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### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Reach, 90 against 55\n- Agent interface, 70 against 60\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\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\n## Score by category\n\n| Category | Weight | Agents-A1-4B | BigBang-v1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 55 | Agents-A1-4B +35 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 60 | Agents-A1-4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 46 | BigBang-v1 +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **54.4 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | BigBang-v1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | The Endless Frontier (endless-frontier) |\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 | 2026-07-14 | 2026-08-06 |\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 | 3/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\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## Before you call either\n\n### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Agents-A1-4B or BigBang-v1?\n\nAgents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"bigbang-v1\"}`. 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