{ "data": { "a": { "slug": "north-mini-code-1-0", "name": "North Mini Code 1.0", "vendor": "Cohere", "vendorUrl": "https://cohere.com", "kind": "weights", "category": "open-weights", "summary": "North Mini Code 1.0 is Cohere's open-weight coding agent model, a mixture of experts of 30B parameters with 3B active, trained for tool use, with interleaved thinking and a 256K context. Apache 2.0. Cohere's 4-bit build is 19 GB.", "url": "https://www.anchorterminal.com/tools/north-mini-code-1-0", "markdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json", "dns": "north-mini-code-1-0.grade.anchorterminal.com", "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "CohereLabs/North-Mini-Code-1.0" }, { "registry": "huggingface", "name": "CohereLabs/North-Mini-Code-1.0-fp8" }, { "registry": "huggingface", "name": "CohereLabs/North-Mini-Code-1.0-w4a16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. The card calls it a research release. Running it yourself takes 19.4 GB at 4 bits (w4a16), 32.1 GB in FP8 or 61.0 GB in BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "agentic", "reasoning", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "4-bit", "self-hosted" ], "lastRelease": "2026-07-29", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 63, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 60, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.", "bestFor": "Developers who want a coding agent on their own GPU with a permissive licence", "strengths": [ "Trained for tool use in coding agents, with parsers in released vLLM", "19 GB at 4 bits from Cohere, and a 256K context", "Apache 2.0, ungated", "Four released runtimes carry the architecture", "Tool use documented with a worked example" ], "weaknesses": [ "No limitations, safety results, languages or data description on the card", "No reply from Cohere on the repository's discussions", "The card's instructions still point at unreleased vLLM and Transformers", "No GGUF from Cohere", "Text only", "No hosted API named" ], "agentNotes": [ "Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed", "Pass the model's thinking back with its tool calls on every turn. The card says results drop without it", "Describe tools in JSON schema and append tool results as a dictionary with the tool role", "Use temperature 1.0 and top_p 0.95", "For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's", "It's text only. Don't send images" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 85, "risk": 60, "upkeep": 36 }, "provenanceScore": 75, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"vllm\u003e=0.33.0\" \"cohere_melody\u003e=0.9.0\"\nvllm serve CohereLabs/North-Mini-Code-1.0 -tp 2 --max-model-len 320000 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Use temperature 1.0 and top_p 0.95,\n# and send the model's earlier thinking back with each turn" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/north-mini-code-1-0" }, "sameCompany": [ "cohere-chat", "cohere-embed", "cohere-transcribe-03-2026", "cohere-north" ], "weights": { "trainedBy": "Cohere and Cohere Labs", "params": "30B", "active": "3B", "context": 256000, "goodFor": "A local coding agent: repository work, terminal tasks and long tool-calling sessions, on one 24 GB GPU at 4 bits.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "North-Mini-Code-1.0-w4a16", "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-w4a16", "who": "publisher", "precision": "w4a16", "sizeGB": 19.38, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "Cohere's 4-bit weight build" }, { "name": "unsloth North-Mini-Code-1.0-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 19.2, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Cohere's. 19.20 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "North-Mini-Code-1.0-fp8", "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 32.06, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Cohere's FP8 build" }, { "name": "unsloth North-Mini-Code-1.0-GGUF (Q8_0)", "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF", "who": "unsloth", "precision": "q8_0", "sizeGB": 32.44, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Cohere's. 32.44 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "North-Mini-Code-1.0 (BF16 safetensors)", "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", "who": "publisher", "precision": "bf16", "sizeGB": 60.97, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "49 files" } ], "run": "vllm serve CohereLabs/North-Mini-Code-1.0-w4a16 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice", "runBuild": "North-Mini-Code-1.0-w4a16", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Cohere (Cohere Labs)", "family": "North Mini Code", "url": "https://www.anchorterminal.com/open-weights/#cohere", "olderCount": 29 }, "area": "models", "provenance": { "legalEntity": "Cohere Inc.", "domain": "cohere.com", "domainRegistered": "2000-03-07", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card says Developed by Cohere and Cohere Labs. Legal entity, domain and security facts are carried from our other Cohere listings.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 8 commits from 5 to 15 June 2026." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.json" }, "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on documentation and upkeep.", "b": { "slug": "qwen3-coder-next", "name": "Qwen3-Coder-Next", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-Coder-Next is Alibaba's open-weight coding-agent model, an 80B mixture-of-experts with 3B active and no thinking mode, trained for tool calling across many agent scaffolds with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-coder-next", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-next.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-next.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json", "dns": "qwen3-coder-next.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-Next" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-Next-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-Next-GGUF" }, { "registry": "ollama", "name": "qwen3-coder-next" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence the card's metadata names, though no LICENSE file is in the repositories. Featherless AI and Parasail serve it per token. Running it yourself costs the hardware, 159.4 GB for the BF16 files, 80.4 GB in FP8 and 48.4 GB for Alibaba's own 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/Qwen/Qwen3-Coder-Next", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-02-03", "graded": true, "anchor": { "graded": true, "score": 57.6, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 81, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 42, "interface": 75, "reach": 85, "risk": 50, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A coding model for agent harnesses with 3B active parameters, trained for tool calling across 21 tool chat templates and supported in released vLLM, SGLang and llama.cpp. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine. Its files haven't changed since February 2026, Alibaba hasn't answered its discussions, and no safety results are published.", "bestFor": "Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.", "strengths": [ "Tool-calling format documented by Alibaba, with the parser named and shipped", "Trained on 21 tool chat templates, several calls per turn and 262,144 tokens native", "Alibaba's own FP8 and GGUF builds, the Q4_K_M about 58 GB in use", "Released support in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers", "A technical report with data sources, a cut-off and filtering", "No account or gate, safetensors and GGUF only" ], "weaknesses": [ "No LICENSE file anywhere and a broken licence link on the card", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Alibaba to any discussion, including a broken shipped parser", "No change to the files since 3 February 2026", "No thinking mode and no documented structured output" ], "agentNotes": [ "To start with Ollama, run `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files per the 21 October reading)", "On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`", "Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36", "The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start", "There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content", "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40", "The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.6 } ], "editorialScores": { "capability": 42, "interface": 75, "reach": 85, "risk": 50, "upkeep": 41 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-coder-next\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3-Coder-Next-FP8 --port 8000 --tensor-parallel-size 2 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-coder-next\", \"messages\": [{\"role\": \"user\", \"content\": \"Write a quick sort in Go.\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-coder-next" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen-agentworld-35b-a3b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen3-Next-80B-A3B-Base (named in github.com/QwenLM/Qwen3-Coder, not public on the Hub)", "params": "80B total, 3B active", "context": 262144, "goodFor": "Fast coding-agent model for local development, text only, trained for tool calling across many agent harness formats, no thinking mode.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Coder-Next-GGUF Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 48.4, "memoryGB": 64, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Four split files in the Qwen3-Coder-Next-Q4_K_M folder, summed from the Hub's file list. The same repository has Q5_0 (55.0 GB), Q5_K_M (56.7 GB), Q6_K (65.5 GB) and F16 (159.5 GB)" }, { "name": "ollama qwen3-coder-next:latest", "url": "https://ollama.com/library/qwen3-coder-next:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 52, "memoryGB": 64, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (digest ca06e9e4087c), also the q4_K_M tag. The q8_0 tag is 85 GB. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "lmstudio-community Qwen3-Coder-Next-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/Qwen3-Coder-Next-MLX-4bit", "who": "lmstudio", "precision": "mlx-4bit", "sizeGB": 44.9, "memoryGB": 64, "format": "mlx", "runsOn": [ "LM Studio (Apple silicon)" ], "note": "The smallest build at 4 bits or better from the publisher or a runtime's library, summed from the Hub's file list. LM Studio's catalogue entry states a 42 GB minimum. lmstudio-community's GGUF Q4_K_M is 48.5 GB" }, { "name": "Qwen3-Coder-Next-FP8", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 80.4, "memoryGB": 128, "format": "safetensors" }, { "name": "Qwen3-Coder-Next-GGUF Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 84.8, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Four split files in the Qwen3-Coder-Next-Q8_0 folder. 84.8 GB × 1.2 = 101.8 GB, within the reference machine's 107.4 GB" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "who": "publisher", "precision": "bf16", "sizeGB": 159.4, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "hf download Qwen/Qwen3-Coder-Next-GGUF --include \"Qwen3-Coder-Next-Q4_K_M/*\" --local-dir . \u0026\u0026 llama-server -m Qwen3-Coder-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072 --temp 1.0 --top-k 40 --top-p 0.95 --min-p 0", "runBuild": "Qwen3-Coder-Next-GGUF Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 64 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (the Qwen team's employer; no LICENSE file names a copyright holder for this model)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The repositories hold no LICENSE file, so no copyright line names the entity. The other Qwen repositories' licences read Copyright 2026 Alibaba Cloud. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence named in the metadata stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commits are uploads and README updates, the last on 3 February 2026 (4 February in the GGUF repository). github.com/QwenLM/Qwen3-Coder records the release on 3 February 2026 and a tech report update on 4 February.", "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-coder-next.json", "live": { "slug": "qwen3-coder-next", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 10, "category": "risk", "leads": "north-mini-code-1-0", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "b": "Safetensors and GGUF only, with the two shipped parsers outside the documented loading path, gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Licence risk loses 5 because the card's licence link is broken and no LICENSE file exists anywhere. Safety evaluation, agent guidance and a security programme score 0, since the report's cybersecurity appendix measures skills rather than safe behaviour, nothing guides agent use and no reporting route was found." }, "scores": { "a": 60, "b": 50 } }, { "by": 10, "category": "interface", "leads": "north-mini-code-1-0", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.", "b": "The template ships in the repository (20). Tool calling is the model's strength, and Alibaba documents how tools are declared, names and ships the qwen3_coder parser and describes the format in its report (25), with several calls per turn in a model trained on 21 harness formats (20). Context is 262,144 tokens native (10). Structured output scores 0, as nothing documents JSON output, and reasoning control scores 0 because the model has no thinking mode to control." }, "scores": { "a": 85, "b": 75 } }, { "by": 5, "category": "upkeep", "leads": "qwen3-coder-next", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.", "b": "The card covers four of the seven items and misses limitations, languages and a cut-off (11). The technical report names the data sources, the cut-off and the filtering with some sizes (15). The files last changed on 3 February 2026 (4 February in the GGUF repository), 274 days ago, within a year (5). Alibaba's staff answered none of the 40 discussions; the one reply from a Qwen organisation member is not the publisher's (0), and GitHub issues couldn't be counted. It's a first release (5), and reports of a broken shipped parser and the missing licence wait unanswered (5). 11+15+5+0+5+5 = 41." }, "scores": { "a": 36, "b": 41 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Cohere", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-29", "b": "2026-02-03", "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": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on documentation and upkeep.", "question": "Which is better for AI agents, North Mini Code 1.0 or Qwen3-Coder-Next?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 50", "Agent interface, 85 against 75" ], "also": null, "goodFor": "Developers who want a coding agent on their own GPU with a permissive licence", "slug": "north-mini-code-1-0", "watchFor": "No limitations, safety results, languages or data description on the card" }, { "aheadOn": [ "Documentation and upkeep, 41 against 36" ], "also": null, "goodFor": "Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.", "slug": "qwen3-coder-next", "watchFor": "No LICENSE file anywhere and a broken licence link on the card" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0.json", "title": "Agents-A1-4B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-next.json", "title": "Agents-A1-4B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.json", "title": "Apertus v1.5 8B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next.json", "title": "Apertus v1.5 8B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0.json", "title": "Apodex-1.1-mini vs North Mini Code 1.0", "url": 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"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/carbon-x-vs-north-mini-code-1-0.json", "title": "Carbon-X vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-next.json", "title": "Carbon-X vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.json", "title": "Cola2 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-next.json", "title": "Cola2 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0.json", "title": "Cosmos-Reason2-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-next.json", "title": "Cosmos-Reason2-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0.json", "title": "Darwin-36B-Opus vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next.json", "title": "Darwin-36B-Opus vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0.json", "title": "DeepSeek-V3.2 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-next.json", "title": "DeepSeek-V3.2 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0.json", "title": "DeepSeek V4.1 Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-Next", "url": 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"https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 12B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 12B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 26B A4B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 26B A4B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 31B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 31B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 E2B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 E2B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0.json", "title": "Gemma 4 E4B IT vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 E4B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.json", "title": "GLM-4.5-Air vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-next.json", "title": "GLM-4.5-Air vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0.json", "title": "GLM-4.7-Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-next.json", "title": "GLM-4.7-Flash vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-north-mini-code-1-0.json", "title": "GLM-5.3-Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next.json", "title": "GLM-5.3-Flash vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-north-mini-code-1-0.json", "title": "GLM-5.3 vs North Mini Code 1.0", "url": 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"https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next.json", "title": "gpt-oss-20b vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 30B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-next.json", "title": "Granite 4.2 30B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-next.json", "title": "Granite 4.2 3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0.json", "title": "Granite 4.2 8B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-coder-next.json", "title": "Granite 4.2 8B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.json", "title": "Hy-Memory-30B-A3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next.json", "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-Next", "url": 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"https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.json", "title": "Inkling vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-next.json", "title": "Inkling vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-coder-next.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.json", "title": "Kimi K3.1 Pro vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next.json", "title": "Kimi K3.1 Pro vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0.json", "title": "Kimi K3 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.json", "title": "Kimi K3 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.json", "title": "Kolibri-1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-next.json", "title": "Kolibri-1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0.json", "title": "Kronos vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-next.json", "title": "Kronos vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0.json", "title": "Laguna M.1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next.json", "title": "Laguna M.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0.json", "title": "Laguna S 2.1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next.json", "title": "Laguna S 2.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0.json", "title": "Laguna XS 2.1 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next.json", "title": "Laguna XS 2.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.json", "title": "LBS-27B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next.json", "title": "LBS-27B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0.json", "title": "LFM2.5-2.6B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.json", "title": "LFM3-1.7B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-next.json", "title": "LFM3-1.7B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0.json", "title": "LFM3-Audio-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-next.json", "title": "LFM3-Audio-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0.json", "title": "LFM3-Omni-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-next.json", "title": "LFM3-Omni-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.json", "title": "LFM3-VL-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next.json", "title": "LFM3-VL-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0.json", "title": "Ling-3.0-tiny vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.json", "title": "Ling-3.0-tiny vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0.json", "title": "Ling-3.1-flash-1019 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-coder-next.json", "title": "Ling-3.1-flash-1019 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0.json", "title": "LongCat-2.5 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0" }, { "json": 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"https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0.json", "title": "MiniCPM-o 4.5 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next.json", "title": "MiniCPM-o 4.5 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0.json", "title": "MiniCPM-V 4.7 1B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0.json", "title": "MiniCPM-V 4.7 35B-A3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-next.json", "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0.json", "title": "MiniCPM5-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-north-mini-code-1-0" }, { "json": 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Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0.json", "title": "MiniMax-M3.1-Flash vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.json", "title": "MiniMax-M3.1-Flash vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0.json", "title": "Mistral Large 4 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next.json", "title": "Mistral Large 4 vs Qwen3-Coder-Next", "url": 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1.0 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.json", "title": "North Mini Code 1.0 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.json", "title": "North Mini Code 1.0 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b.json", "title": "North Mini Code 1.0 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.json", "title": "occamy-1.0 vs Qwen3-Coder-Next", "url": 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"https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-Coder-Next vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.json", "title": "Qwen3-Coder-Next vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b.json", "title": "Qwen3-Coder-Next vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-step-3-7-flash.json", "title": "Qwen3-Coder-Next vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b.json", "title": "Qwen3-Coder-Next vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "key": "reach", "name": "Reach", "north-mini-code-1-0": 85, "qwen3-coder-next": 85, "weight": 25 }, { "by": 10, "edge": "north-mini-code-1-0", "key": "risk", "name": "Risk", "north-mini-code-1-0": 60, "qwen3-coder-next": 50, "weight": 20 }, { "by": 10, "edge": "north-mini-code-1-0", "key": "interface", "name": "Agent interface", "north-mini-code-1-0": 85, "qwen3-coder-next": 75, "weight": 10 }, { "by": 5, "edge": "qwen3-coder-next", "key": "upkeep", "name": "Documentation and upkeep", "north-mini-code-1-0": 36, "qwen3-coder-next": 41, "weight": 10 } ], "summary": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "north-mini-code-1-0": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.", "qwen3-coder-next": "A coding model for agent harnesses with 3B active parameters, trained for tool calling across 21 tool chat templates and supported in released vLLM, SGLang and llama.cpp. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine. Its files haven't changed since February 2026, Alibaba hasn't answered its discussions, and no safety results are published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next", "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.min.md" }, "markdown": "North Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- North Mini Code 1.0: grade C, 61/100, rank #80 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/north-mini-code-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json\n- Qwen3-Coder-Next: grade C, 57.6/100, rank #107 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### North Mini Code 1.0 (C)\n\nGood for: Developers who want a coding agent on their own GPU with a permissive licence\n\nAhead on:\n- Risk, 60 against 50\n- Agent interface, 85 against 75\n\nWatch for: No limitations, safety results, languages or data description on the card\n\n### Qwen3-Coder-Next (C)\n\nGood for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.\n\nAhead on:\n- Documentation and upkeep, 41 against 36\n\nWatch for: No LICENSE file anywhere and a broken licence link on the card\n\n\n## Why the edge\n\n### Risk: North Mini Code 1.0 +10\n\n- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n- Qwen3-Coder-Next (50): Safetensors and GGUF only, with the two shipped parsers outside the documented loading path, gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Licence risk loses 5 because the card's licence link is broken and no LICENSE file exists anywhere. Safety evaluation, agent guidance and a security programme score 0, since the report's cybersecurity appendix measures skills rather than safe behaviour, nothing guides agent use and no reporting route was found.\n\n### Agent interface: North Mini Code 1.0 +10\n\n- North Mini Code 1.0 (85): Chat template 20, chat_template.jinja in the repository (12,397 characters). Tool-calling format 25, the card documents how tools are declared (JSON schema through the chat template), how the call and its thinking go back into the history and how results are appended, and names the cohere_command4 tool-call parser for vLLM, which released vLLM has. Trained tool calling 20, the card says the model was specifically trained with tool-use capabilities for agentic coding, with reinforcement learning on verifiable tasks, and the template renders every call in a turn with thinking between them. Structured output 0, JSON schema is for describing tools. Reasoning control 10, the template has a reasoning_effort setting, and the card names the cohere_command4 reasoning parser, which returns thinking apart from the answer. Context 10, 256K tokens per the card, with 64K of output, and 500,000 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Qwen3-Coder-Next (75): The template ships in the repository (20). Tool calling is the model's strength, and Alibaba documents how tools are declared, names and ships the qwen3_coder parser and describes the format in its report (25), with several calls per turn in a model trained on 21 harness formats (20). Context is 262,144 tokens native (10). Structured output scores 0, as nothing documents JSON output, and reasoning control scores 0 because the model has no thinking mode to control.\n\n### Documentation and upkeep: Qwen3-Coder-Next +5\n\n- North Mini Code 1.0 (36): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, the card gives the training method and nothing about the data. Last change 10, the main repository last changed on 15 June 2026 (outdated chat templates removed), 148 days before this check, and the draft model's repository on 29 July, 104 days. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (7 June to 6 November 2026) has a reply from a CohereLabs member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, with each build in its own repository. File health 10, Config, template and card agree on the architecture, and the June commit that removed broken templates says so. 11 + 0 + 10 + 0 + 5 + 10 = 36.\n- Qwen3-Coder-Next (41): The card covers four of the seven items and misses limitations, languages and a cut-off (11). The technical report names the data sources, the cut-off and the filtering with some sizes (15). The files last changed on 3 February 2026 (4 February in the GGUF repository), 274 days ago, within a year (5). Alibaba's staff answered none of the 40 discussions; the one reply from a Qwen organisation member is not the publisher's (0), and GitHub issues couldn't be counted. It's a first release (5), and reports of a broken shipped parser and the missing licence wait unanswered (5). 11+15+5+0+5+5 = 41.\n\n## Score by category\n\n| Category | Weight | North Mini Code 1.0 | Qwen3-Coder-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 85 | even |\n| Risk | 20% (20 this run) | 60 | 50 | North Mini Code 1.0 +10 |\n| Agent interface | 10% (10 this run) | 85 | 75 | North Mini Code 1.0 +10 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 41 | Qwen3-Coder-Next +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | North Mini Code 1.0 | Qwen3-Coder-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-29 | 2026-02-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**North Mini Code 1.0.** A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.\n\n**Qwen3-Coder-Next.** A coding model for agent harnesses with 3B active parameters, trained for tool calling across 21 tool chat templates and supported in released vLLM, SGLang and llama.cpp. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine. Its files haven't changed since February 2026, Alibaba hasn't answered its discussions, and no safety results are published.\n\n## Before you call either\n\n### North Mini Code 1.0\n\n1. Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed\n2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it\n3. Describe tools in JSON schema and append tool results as a dictionary with the tool role\n4. Use temperature 1.0 and top_p 0.95\n5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's\n6. It's text only. Don't send images\n\n### Qwen3-Coder-Next\n\n1. To start with Ollama, run `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files per the 21 October reading)\n2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`\n3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36\n4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start\n5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content\n6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40\n7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf\n8. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, North Mini Code 1.0 or Qwen3-Coder-Next?\n\nNorth Mini Code 1.0 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-coder-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"north-mini-code-1-0\", \"b\": \"qwen3-coder-next\"}`. From a terminal: `anchor compare north-mini-code-1-0 qwen3-coder-next`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json and https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json\n\n## Other comparisons with North Mini Code 1.0 or Qwen3-Coder-Next\n\n- [Agents-A1-4B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0.md)\n- [Agents-A1-4B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-next.md)\n- [Apertus v1.5 8B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.md)\n- [Apertus v1.5 8B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next.md)\n- [Apodex-1.1-mini vs North Mini Code 1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0.md)\n- [Apodex-1.1-mini vs 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Qwen3-Coder-Next](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-next.md)\n- [Gemma 4 12B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 12B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 26B A4B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 26B A4B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 31B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 31B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 E2B IT vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0.md)\n- [Gemma 4 E2B IT vs 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WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.md)\n- [Qwen3-Coder-Next vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": 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