{ "data": { "a": { "slug": "ling-3-0-tiny", "name": "Ling-3.0-tiny", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "Ling-3.0-tiny is inclusionAI's open-weight hybrid reasoning mixture of experts with 7.9B parameters and 1.3B active, mixing Kimi Delta Attention and MLA layers 3 to 1, with switchable thinking and a 131,072-token context, in BF16, FP8, INT4 and GGUF under MIT.", "url": "https://www.anchorterminal.com/tools/ling-3-0-tiny", "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json", "repo": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-fp8" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-int4" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repositories hold no LICENSE file). Running it yourself costs the hardware, 15.8 GB for the BF16 files, 8.4 GB for the FP8 build, 5.8 GB for INT4 and 4.8 GB for the Q4_K_M GGUF (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/inclusionAI/Ling-3.0-tiny", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "hybrid-attention", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "int4", "gguf", "self-hosted" ], "lastRelease": "2026-08-31", "graded": true, "anchor": { "graded": true, "score": 55, "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": 1201, "categoryRank": 67, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 31, "interface": 60, "reach": 90, "risk": 50, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.", "bestFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT", "strengths": [ "About 6 GB of memory for inclusionAI's own Q4_K_M GGUF, with FP8, INT4 and three more GGUF precisions", "Runs in released Transformers, vLLM, SGLang and llama.cpp", "MIT with no gate, no account and no use policy, with checksummed files in inclusionAI's own organisation", "Thinking switchable per request, several tool calls a turn and 131,072 tokens of native context", "A training-content summary with sources, size and an October 2025 cut-off, and a guardrail probe made for this model" ], "weaknesses": [ "No hosted provider today, and Ollama support is only an unmerged pull request", "The card's SGLang recipe asks for built-in MTP the files don't have, and the safetensors index misstates the total size", "The documented vLLM route needs --trust-remote-code", "No safety results for the model, no structured output and no security contact", "Card and SGLang disagree on which tool and reasoning parsers to use, and most discussions since August have no reply" ], "agentNotes": [ "Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3", "With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code", "With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks", "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20", "The template keeps earlier reasoning in the prompt, which grows long agent sessions", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented", "For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55 } ], "editorialScores": { "capability": 31, "interface": 60, "reach": 90, "risk": 50, "upkeep": 56 }, "provenanceScore": 57, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.32.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align \\\n --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"inclusionAI/Ling-3.0-tiny\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}, \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 20}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ling-3-0-tiny" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "params": "7.9B (1.3B active)", "context": 131072, "goodFor": "Small, fast hybrid reasoning model for local agents, tool calling and coding on a laptop, with thinking switchable per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ling-3.0-tiny-int4", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-int4", "who": "publisher", "precision": "int4", "sizeGB": 5.8, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors, 4-bit weights in groups of 32, with attention and dense layers kept at higher precision" }, { "name": "Ling-3.0-tiny-Q6_K", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 6.5, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-Q5_K_M", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 5.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-Q4_K_M", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 4.8, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-fp8", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 8.4, "memoryGB": 12, "format": "safetensors" }, { "name": "Ling-3.0-tiny-Q8_0", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 8.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "who": "publisher", "precision": "bf16", "sizeGB": 15.8, "memoryGB": 24, "format": "safetensors" } ], "run": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Ling-3.0-tiny-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ling 3", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ling-3.0-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-Fin", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-VL", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL", "params": "124B total, 5.5B active" }, { "name": "Ling-3.0-flash-dspark", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark", "params": "1.36B (draft)" }, { "name": "Ling-3.0-flash-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe", "params": "5.18M (probe)" }, { "name": "Ling-3.0-tiny-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe", "params": "3.22M (probe)" }, { "name": "Ling-3.1-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash", "params": "560B total, 25B active" } ], "olderCount": 54, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 17 commits from 10 August to 24 September 2026, mostly README updates, the last adding the training-content summary.", "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026, and the inclusionAI .github repository holds only a profile README." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.json", "live": { "slug": "ling-3-0-tiny", "securityTxt": { "url": "https://inclusion-ai.org/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-23T15:23:26.507720168Z" }, "pages": [ { "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-23T01:49:25.466281104Z", "changedAt": "2026-10-23T01:49:25.466281104Z", "fingerprint": "a3ab7996fbd4" } ], "updatedAt": "2026-10-23T15:23:26.507720168Z" } }, "answer": "Recursive Flint scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach.", "b": { "slug": "recursive-flint", "name": "Recursive Flint", "vendor": "Recursive", "vendorUrl": "https://www.recursive.com", "kind": "weights", "category": "open-weights", "summary": "Recursive Flint is Recursive's open-weight mixture-of-experts coding model for agents, 39.8B total and about 2.5B active parameters, with reasoning, tool calling and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/recursive-flint", "markdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/recursive-flint.json", "repo": "https://huggingface.co/RecursiveSI/flint", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "RecursiveSI/flint" }, { "registry": "huggingface", "name": "RecursiveSI/flint-int4" }, { "registry": "huggingface", "name": "RecursiveSI/flint-int4-gguf" }, { "registry": "huggingface", "name": "RecursiveSI/flint-nvfp4-gguf" }, { "registry": "ollama", "name": "flint" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and all three quantised builds (checked 2026-10-22).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 79.6 GB for the BF16 files and 22.6 GB for Recursive's smallest 4-bit build (MLX INT4). The card mentions hosted API terms separate from the weight licence, but no Recursive API or inference provider serving Flint was confirmed (checked 2026-10-22).", "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 2026-10-22).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-22" }, "docsUrl": "https://huggingface.co/RecursiveSI/flint", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "mlx", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-21", "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": 1201, "categoryRank": 41, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 90, "reach": 75, "risk": 60, "upkeep": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-22" }, "negative": 0, "verdict": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.", "bestFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Recursive's own MLX INT4, Q4_1 GGUF and NVFP4 GGUF builds, the smallest 22.6 GB", "Tool format, parser, parallel calls and JSON Schema output documented by Recursive", "262,144 tokens of context with no position scaling", "Safetensors and GGUF only, with a checksum on every file in Recursive's organisation", "llama.cpp, Ollama and LM Studio support on release day" ], "weaknesses": [ "No independent benchmark results yet, every published score is Recursive's", "No reasoning switch or budget, so hard prompts can spend tens of thousands of tokens thinking", "The BF16 route needs one 141 GB GPU and Recursive's SGLang plugin, and vLLM and released Transformers don't run it", "No safety results and no trained instruction hierarchy", "No inference provider or confirmed Recursive API for agents without the hardware" ], "agentNotes": [ "Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp", "With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on", "Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out", "Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask", "Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call", "Message content must be a string, flatten content-part arrays first", "Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "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": 90, "reach": 75, "risk": 60, "upkeep": 67 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-22", "reach": "2026-10-22", "risk": "2026-10-22", "upkeep": "2026-10-22" }, "last": "2026-10-22", "oldest": "2026-10-22", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run flint\n# or, with llama.cpp b11803 or later:\nllama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 262144 -np 1 -fa on --jinja \\\n --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"flint\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/recursive-flint" }, "weights": { "trainedBy": "Recursive", "params": "39.8B total, about 2.5B active", "context": 262144, "goodFor": "Agentic coding on a 32 to 48 GB machine, investigating codebases, fixing bugs and writing tests with multi-turn tool calls and reasoning.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio" ], "builds": [ { "name": "flint-int4", "url": "https://huggingface.co/RecursiveSI/flint-int4", "who": "publisher", "precision": "int4 (group-64 affine, INT8 embeddings)", "sizeGB": 22.6, "memoryGB": 32, "format": "mlx", "runsOn": [ "rec", "Ollama" ], "note": "For Apple Silicon. Recursive's rec engine asks for an M4 Pro or newer with 48 GB" }, { "name": "flint-int4-gguf Q4_1", "url": "https://huggingface.co/RecursiveSI/flint-int4-gguf", "who": "publisher", "precision": "q4_1", "sizeGB": 26, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "flint-int4-q4_1.gguf, 25,992,966,976 bytes, the INT4 export's trained codes stored as Q4_1. The card targets Metal and asks for a Mac with at least 48 GB at full context" }, { "name": "flint-nvfp4-gguf", "url": "https://huggingface.co/RecursiveSI/flint-nvfp4-gguf", "who": "publisher", "precision": "nvfp4", "sizeGB": 22.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "flint-nvfp4.gguf, 22,863,084,160 bytes, for NVIDIA GPUs with CUDA. The card recommends at least 48 GB at full context" }, { "name": "ollama flint:latest", "url": "https://ollama.com/library/flint:latest", "who": "ollama", "precision": "4-bit", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Digest 905c74dfd56b, shown as 23 GB (Ollama rounds to whole GB). The library also has qat-int4 (23 GB), q4_1 (26 GB), gguf-nvfp4 (23 GB) and mlx-bf16 (80 GB) tags. Ollama 0.40.8 runs Flint in its MLX runner; its bundled llama.cpp (b11738) predates Flint, so the GGUF tags on Linux weren't confirmed in a release" }, { "name": "lmstudio-community flint-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/flint-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 24.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "flint-Q4_K_M.gguf, 24,166,841,184 bytes, the build behind LM Studio's recursivesi/flint catalogue page. Also Q6_K (32.7 GB, 48 GB of memory) and Q8_0 (42.4 GB, 64 GB)" }, { "name": "flint", "url": "https://huggingface.co/RecursiveSI/flint", "who": "publisher", "precision": "bf16", "sizeGB": 79.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "SGLang 0.5.16 with Recursive's flint_sglang kit" ], "note": "Recursive's SGLang kit needs one GPU with at least 141 GB (B300, B200 or H200). Transformers support isn't in a release yet" } ], "run": "llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "runBuild": "flint-int4-gguf Q4_1", "checked": "2026-10-22", "minMemoryGB": 32 }, "family": { "lab": "Recursive", "family": "Flint", "url": "https://www.anchorterminal.com/open-weights/#recursive" }, "area": "models", "provenance": { "legalEntity": "Recursive Superintelligence, Inc. (the Hugging Face organisation's full name)", "domain": "recursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "unknown", "checked": "2026-10-22", "notes": [ "The Hugging Face organisation RecursiveSI is named Recursive Superintelligence, Inc. and isn't verified by Hugging Face. Recursive's rec repository, which carries Recursive's logo and links recursive.com, downloads the INT4 build from RecursiveSI/flint-int4 at a pinned commit.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The card links www.recursive.com, docs.recursive.com and github.com/recursive-oss/rec. Neither recursive.com host could be read on 22 October 2026, so security.txt and the domain's registration weren't checked.", "The card names a security reporting address at recursive.com, and rec's SECURITY.md asks for private reports through GitHub's vulnerability reporting." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/recursive-flint.json", "live": { "slug": "recursive-flint", "securityTxt": { "url": "https://recursive.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-23T15:23:07.053004679Z" }, "updatedAt": "2026-10-23T15:23:07.053004679Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "Recursive", "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": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-31", "b": "2026-10-21", "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": "2.8/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Recursive Flint scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach.", "question": "Which is better for AI agents, Ling-3.0-tiny or Recursive Flint?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 75" ], "also": null, "goodFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT", "slug": "ling-3-0-tiny", "watchFor": "No hosted provider today, and Ollama support is only an unmerged pull request" }, { "aheadOn": [ "Risk, 60 against 50", "Agent interface, 90 against 60", "Documentation and upkeep, 67 against 56" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.", "slug": "recursive-flint", "watchFor": "No independent benchmark results yet, every published score is Recursive's" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json", "title": "Agents-A1-4B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.json", "title": "Agents-A1-4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.json", "title": "Apertus v1.5 8B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.json", "title": "Apertus v1.5 8B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.json", "title": "Apodex-1.1-mini vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.json", "title": "Apodex-1.1-mini vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.json", "title": "BigBang-v1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.json", "title": "BigBang-v1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny.json", "title": "Carbon-X vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.json", "title": "Carbon-X vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.json", "title": "Cosmos-Reason2-2B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.json", "title": "Cosmos-Reason2-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.json", "title": "Darwin-36B-Opus vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.json", "title": "Darwin-36B-Opus vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.json", "title": "DeepSeek-V3.2 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint.json", "title": "DeepSeek-V3.2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json", "title": "DeepSeek V4.1 Flash vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint.json", "title": "DeepSeek V4.1 Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.json", "title": "DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.json", "title": "DeepSeek-V4-Flash-0731 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.json", "title": "DiffusionGemma 26B A4B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.json", "title": "DiffusionGemma 26B A4B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 12B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-recursive-flint.json", "title": "Gemma 4 12B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 26B A4B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-recursive-flint.json", "title": "Gemma 4 26B A4B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 31B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint.json", "title": "Gemma 4 31B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 E2B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.json", "title": "Gemma 4 E2B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 E4B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-recursive-flint.json", "title": "Gemma 4 E4B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json", "title": "GLM-4.5-Air vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-recursive-flint.json", "title": "GLM-4.5-Air vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny.json", "title": "GLM-4.7-Flash vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.json", "title": "GLM-4.7-Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.json", "title": "GLM-5.3-Flash vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.json", "title": "GLM-5.3-Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny.json", "title": "GLM-5.3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.json", "title": "GLM-5.3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny.json", "title": "gpt-oss-120b vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint.json", "title": "gpt-oss-120b vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.json", "title": "gpt-oss-20b vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint.json", "title": "gpt-oss-20b vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.json", "title": "Granite 4.2 3B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint.json", "title": "Granite 4.2 3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json", "title": "Hy3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-recursive-flint.json", "title": "Hy3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/hy3-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.json", "title": "Inkling-Small vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint.json", "title": "Inkling-Small vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.json", "title": "Inkling vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-recursive-flint.json", "title": "Inkling vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/inkling-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json", "title": "Kimi K3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-recursive-flint.json", "title": "Kimi K3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny.json", "title": "Kolibri-1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint.json", "title": "Kolibri-1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.json", "title": "Laguna M.1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.json", "title": "Laguna M.1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny.json", "title": "Laguna S 2.1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.json", "title": "Laguna S 2.1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny.json", "title": "Laguna XS 2.1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint.json", "title": "Laguna XS 2.1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.json", "title": "LFM2.5-2.6B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.json", "title": "LFM2.5-2.6B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Ling-3.0-tiny vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5.json", "title": "Ling-3.0-tiny vs 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"https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint.json", "title": "Qwen3.6-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint.json", "title": "Qwen3.8-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint.json", "title": "Qwen3.8-Flash-Next vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint.json", "title": "Qwen3-Coder-Next vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-2B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-32B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-4B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-8B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint.json", "title": "Qwopus3.8-27B-Flash-V2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.json", "title": "Recursive Flint vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json", "title": "Recursive Flint vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b.json", "title": "Recursive Flint vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.json", "title": "Recursive Flint vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "ling-3-0-tiny", "key": "reach", "ling-3-0-tiny": 90, "name": "Reach", "recursive-flint": 75, "weight": 25 }, { "by": 10, "edge": "recursive-flint", "key": "risk", "ling-3-0-tiny": 50, "name": "Risk", "recursive-flint": 60, "weight": 20 }, { "by": 30, "edge": "recursive-flint", "key": "interface", "ling-3-0-tiny": 60, "name": "Agent interface", "recursive-flint": 90, "weight": 10 }, { "by": 11, "edge": "recursive-flint", "key": "upkeep", "ling-3-0-tiny": 56, "name": "Documentation and upkeep", "recursive-flint": 67, "weight": 10 } ], "summary": "Recursive Flint scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "ling-3-0-tiny": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.", "recursive-flint": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint", "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.min.md" }, "markdown": "Recursive Flint scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach. Both do open-weight models you run yourself.\n\n- Ling-3.0-tiny: grade C, 55/100, rank #85 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json\n- Recursive Flint: grade C, 61/100, rank #49 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.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 3023 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ling-3.0-tiny (C)\n\nGood for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT\n\nAhead on:\n- Reach, 90 against 75\n\nWatch for: No hosted provider today, and Ollama support is only an unmerged pull request\n\n### Recursive Flint (C)\n\nGood for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.\n\nAhead on:\n- Risk, 60 against 50\n- Agent interface, 90 against 60\n- Documentation and upkeep, 67 against 56\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: No independent benchmark results yet, every published score is Recursive's\n\n\n## Score by category\n\n| Category | Weight | Ling-3.0-tiny | Recursive Flint | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 75 | Ling-3.0-tiny +15 |\n| Risk | 20% (20 this run) | 50 | 60 | Recursive Flint +10 |\n| Agent interface | 10% (10 this run) | 60 | 90 | Recursive Flint +30 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 67 | Recursive Flint +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.0-tiny | Recursive Flint |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Recursive |\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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-31 | 2026-10-21 |\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 | 2.8/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.\n\n**Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.\n\n## Before you call either\n\n### Ling-3.0-tiny\n\n1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3\n2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code\n3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20\n6. The template keeps earlier reasoning in the prompt, which grows long agent sessions\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches\n\n### Recursive Flint\n\n1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp\n2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on\n3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out\n4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask\n5. Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call\n6. Message content must be a string, flatten content-part arrays first\n7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key\n\n## Questions\n\n### Which is better for AI agents, Ling-3.0-tiny or Recursive Flint?\n\nRecursive Flint scores 61 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 4 scored categories. Ling-3.0-tiny leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-0-tiny\", \"b\": \"recursive-flint\"}`. From a terminal: `anchor compare ling-3-0-tiny recursive-flint`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n\n## Other comparisons with Ling-3.0-tiny or Recursive Flint\n\n- [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.md)\n- [Agents-A1-4B vs Recursive Flint](https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.md)\n- [Apertus v1.5 8B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.md)\n- [Apertus v1.5 8B vs Recursive Flint](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.md)\n- [Apodex-1.1-mini vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.md)\n- [Apodex-1.1-mini vs Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md)\n- [BigBang-v1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.md)\n- [BigBang-v1 vs Recursive Flint](https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.md)\n- [Carbon-X vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny.md)\n- [Carbon-X vs Recursive Flint](https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.md)\n- [Cosmos-Reason2-2B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.md)\n- [Cosmos-Reason2-2B vs Recursive Flint](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.md)\n- [Darwin-36B-Opus vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.md)\n- [Darwin-36B-Opus vs Recursive Flint](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.md)\n- [DeepSeek-V3.2 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.md)\n- [DeepSeek-V3.2 vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint.md)\n- [DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.md)\n- [DeepSeek-V4-Flash-0731 vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.md)\n- [DiffusionGemma 26B A4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.md)\n- [DiffusionGemma 26B A4B IT vs Recursive Flint](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.md)\n- [Gemma 4 12B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 12B IT vs Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-recursive-flint.md)\n- [Gemma 4 26B A4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 26B A4B IT vs Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-recursive-flint.md)\n- [Gemma 4 31B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 31B IT vs Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint.md)\n- [Gemma 4 E2B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E2B IT vs Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.md)\n- [Gemma 4 E4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E4B IT vs Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-recursive-flint.md)\n- [GLM-4.5-Air vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.md)\n- [GLM-4.5-Air vs Recursive Flint](https://www.anchorterminal.com/compare/glm-4-5-air-vs-recursive-flint.md)\n- [GLM-4.7-Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny.md)\n- [GLM-4.7-Flash vs Recursive Flint](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.md)\n- [GLM-5.3-Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.md)\n- [GLM-5.3-Flash vs Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.md)\n- [GLM-5.3 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny.md)\n- [GLM-5.3 vs Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.md)\n- [gpt-oss-120b vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny.md)\n- [gpt-oss-120b vs Recursive Flint](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint.md)\n- [gpt-oss-20b vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.md)\n- [gpt-oss-20b vs Recursive Flint](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint.md)\n- [Granite 4.2 3B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.md)\n- [Granite 4.2 3B vs Recursive Flint](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint.md)\n- [Hy3 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.md)\n- [Hy3 vs Recursive Flint](https://www.anchorterminal.com/compare/hy3-vs-recursive-flint.md)\n- [Inkling-Small vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.md)\n- [Inkling-Small vs Recursive 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Flint](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.md)\n- [Qwopus3.8-27B-Flash-V2 vs Recursive Flint](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint.md)\n- [Recursive Flint vs SmolLM3-3B](https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.md)\n- [Recursive Flint vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.md)\n- [Recursive Flint vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b.md)\n- [Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Recursive Flint vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor 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