{ "data": { "a": { "slug": "deepseek-v4-flash-0731", "name": "DeepSeek-V4-Flash-0731", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V4-Flash-0731 is the 31 July 2026 release of DeepSeek's V4 Flash, a mixture-of-experts text model with 284B parameters, 13B active and a 1M-token context, under MIT. DeepSeek's API retired V4 Flash on 10 September 2026.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4-Flash-0731" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's API retired V4 Flash on 10 September 2026 and routes its old names to V4.1 Flash, so no hosted DeepSeek endpoint serves these weights. Running them yourself costs the hardware, 166.9 GB of files (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/deepseek-ai/DeepSeek-V4-Flash-0731", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "long-context", "mit", "safetensors", "fp8", "fp4", "self-hosted", "new" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 23, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "bestFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context", "strengths": [ "MIT licence with nothing added, downloadable with no account", "1M tokens of context trained in", "Runs in vLLM, SGLang, llama.cpp, Transformers and LM Studio from released versions", "Parallel tool calls in a documented DSML format, with reasoning off or at three effort levels", "Six live inference providers on Hugging Face for agents without the hardware", "Safetensors only, with a SHA-256 on every shard" ], "weaknesses": [ "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine", "No Jinja template, so prompts come from running DeepSeek's encoding code or a runtime's own encoder", "No safety evaluation or agent-use guidance published, and only a company-wide security address", "DeepSeek hasn't replied to any of the 61 discussions, including reports of tool-call errors", "The card lacks intended use, limitations, languages and a knowledge cut-off" ], "agentNotes": [ "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)", "Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template", "Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"", "Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max", "Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash", "LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "provenanceScore": 73, "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": "# From DeepSeek's card (one 4x GB300 node). Add vLLM's --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 for tool calls\nvllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \\\n --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \\\n --data-parallel-size 4 --enable-expert-parallel \\\n --moe-backend deep_gemm_mega_moe \\\n --attention-config '{\"use_fp4_indexer_cache\": true}' \\\n --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-flash-0731" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v3-2" ], "weights": { "trainedBy": "DeepSeek", "params": "284B", "active": "13B", "context": 1048576, "goodFor": "Text model for agents and long documents, with thinking or chat mode, reasoning effort and tool calls in DSML tags.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "DeepSeek-V4-Flash-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 166.9, "memoryGB": 256, "format": "safetensors", "note": "MoE experts in FP4, most other weights FP8; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4", "runBuild": "DeepSeek-V4-Flash-0731", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V4", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V4-Flash-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-Vision-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", "params": "not stated on card" }, { "name": "DeepSeek-V4-Pro-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-0813", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813", "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)" }, { "name": "DeepSeek-V4.1-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "params": "552B backbone, 8B activated in prefill / 16B in decode", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" }, { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "params": "552B backbone, 8B / 16B activated (V4.1-Flash figures; not restated)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" } ], "olderCount": 72, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, four commits from 31 July to 1 August 2026 (the release and a card edit adding the SGLang cookbook).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.json" }, "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 0 of 4 scored categories. Laguna XS 2.1 leads on reach, risk and agent interface.", "b": { "slug": "laguna-xs-2-1", "name": "Laguna XS 2.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna XS 2.1 is Poolside's open-weight Mixture-of-Experts coding model, 33B total and 3B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/laguna-xs-2-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json", "repo": "https://huggingface.co/poolside/Laguna-XS-2.1", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-INT4" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-NVFP4" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-NVFP4-mlx" }, { "registry": "huggingface", "name": "poolside/Laguna-XS-2.1-GGUF" }, { "registry": "ollama", "name": "laguna-xs-2.1" } ], "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 every build, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 66.9 GB for the BF16 files and 20.3 GB for Poolside's Q4_K_M GGUF, which is also Ollama's default build. Poolside serves it on OpenRouter with a free and a paid endpoint, both marked to expire on 31 October 2026 (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/poolside/Laguna-XS-2.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "openmdw-1.1", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-19", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 33, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.", "bestFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm", "Poolside's own Q4_K_M, FP8, INT4, NVFP4 and MLX builds, the Q4_K_M at 20.3 GB", "Permissive OpenMDW-1.1 licence with no gate, account or use policy", "Tool calling and the poolside_v1 parser documented on the card, several calls a turn", "Thinking on or off per request, with the reasoning returned in reasoning_content" ], "weaknesses": [ "No reply from Poolside to any discussion, including reports about the tokenizer and remote code", "No safety results and only general cautions for agent use", "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code", "The template defaults thinking off while generation_config.json turns it on", "No structured output documentation, and no limitations, languages or cut-off on the card" ], "agentNotes": [ "Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF", "On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)", "With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds", "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding", "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped", "Sampling per the card is temperature 1.0 and top_k 20", "The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "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": 85, "reach": 85, "risk": 55, "upkeep": 46 }, "provenanceScore": 71, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run laguna-xs-2.1\n# or, with vLLM 0.21.0 or later (a build with vllm-project/vllm#47311):\nvllm serve poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 \\\n --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"laguna-xs-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-xs-2-1" }, "sameCompany": [ "laguna-m-1", "laguna-s-2-1" ], "weights": { "trainedBy": "Poolside", "params": "33.4B total, 3B active", "context": 262144, "goodFor": "Agentic coding and long-horizon terminal work on one GPU or a 32 GB machine, with tool calling and reasoning that turns on or off per request.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Laguna-XS-2.1-INT4", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-INT4", "who": "publisher", "precision": "int4 (w4a16)", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors" }, { "name": "Laguna-XS-2.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.6, "memoryGB": 32, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-XS-2.1-NVFP4-mlx", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4-mlx", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.6, "memoryGB": 32, "format": "mlx", "runsOn": [ "MLX" ] }, { "name": "Laguna-XS-2.1-GGUF Q4_K_M", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 20.3, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "Laguna-XS-2.1-Q4_K_M.gguf, 20,274,300,032 bytes. The repository also holds a BF16 GGUF of 66.9 GB (96 GB of memory)" }, { "name": "ollama laguna-xs-2.1:latest", "url": "https://ollama.com/library/laguna-xs-2.1:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20.3, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama shows 20 GB. Its model blob (sha256 1ac7079101fc) is Poolside's Q4_K_M GGUF, so the exact size is Poolside's. The library also has q8_0 (36 GB), bf16 (67 GB) and MLX tags (nvfp4 shown as 20 GB, exact size unpublished, mxfp8 40 GB, mlx-bf16 68 GB). The card warns that ollama run may return empty output on Apple Silicon until llama.cpp#25389 ships" }, { "name": "Laguna-XS-2.1-FP8", "url": "https://huggingface.co/poolside/Laguna-XS-2.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.3, "memoryGB": 48, "format": "safetensors" }, { "name": "Laguna-XS-2.1", "url": "https://huggingface.co/poolside/Laguna-XS-2.1", "who": "publisher", "precision": "bf16", "sizeGB": 66.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072 --port 8000", "runBuild": "Laguna-XS-2.1-GGUF Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Poolside", "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-S-2.1", "url": "https://huggingface.co/poolside/Laguna-S-2.1", "params": "117.56B (Hub safetensors count; the name gives only the size class S)", "listing": "https://www.anchorterminal.com/tools/laguna-s-2-1" }, { "name": "Laguna-S-2.1-base", "url": "https://huggingface.co/poolside/Laguna-S-2.1-base", "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)" }, { "name": "Laguna-XS.2", "url": "https://huggingface.co/poolside/Laguna-XS.2", "params": "33.44B (Hub safetensors count; the name gives only the size class XS)" } ], "unrestrictedCopies": 1 }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)", "domain": "poolside.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://poolside.ai/legal/eula", "privacy": "https://poolside.ai/legal/privacy", "statusPage": "", "changelog": "https://docs.poolside.ai/release-notes/models", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation poolside is verified, and Poolside's blog post for this release links its collection there.", "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes. The June 2026 entry calls XS 2.1 an incremental update to Laguna XS.2.", "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Poolside", "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": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-31", "b": "2026-08-19", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "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": "2.5/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 0 of 4 scored categories. 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"https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna XS 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.json", "title": "Laguna XS 2.1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct" }, { "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/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.json", "title": "Laguna XS 2.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.json", "title": "Laguna XS 2.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "deepseek-v4-flash-0731": 70, "edge": "laguna-xs-2-1", "key": "reach", "laguna-xs-2-1": 85, "name": "Reach", "weight": 25 }, { "by": 5, "deepseek-v4-flash-0731": 50, "edge": "laguna-xs-2-1", "key": "risk", "laguna-xs-2-1": 55, "name": "Risk", "weight": 20 }, { "by": 10, "deepseek-v4-flash-0731": 75, "edge": "laguna-xs-2-1", "key": "interface", "laguna-xs-2-1": 85, "name": "Agent interface", "weight": 10 }, { "by": 3, "deepseek-v4-flash-0731": 43, "edge": "laguna-xs-2-1", "key": "upkeep", "laguna-xs-2-1": 46, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 0 of 4 scored categories. Laguna XS 2.1 leads on reach, risk and agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-flash-0731": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "laguna-xs-2-1": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1", "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.min.md" }, "markdown": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 0 of 4 scored categories. Laguna XS 2.1 leads on reach, risk and agent interface. Both do open-weight models you run yourself.\n\n- DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json\n- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### DeepSeek-V4-Flash-0731 (B)\n\nGood for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context\n\nWatch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine\n\n### Laguna XS 2.1 (C)\n\nGood for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.\n\nAhead on:\n- Reach, 85 against 70\n- Risk, 55 against 50\n- Agent interface, 85 against 75\n\nWatch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V4-Flash-0731 | Laguna XS 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 85 | Laguna XS 2.1 +15 |\n| Risk | 20% (20 this run) | 50 | 55 | Laguna XS 2.1 +5 |\n| Agent interface | 10% (10 this run) | 75 | 85 | Laguna XS 2.1 +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 46 | Laguna XS 2.1 +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | Laguna XS 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Poolside |\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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-08-19 |\n| Terms last updated | no document linked | |\n| Privacy policy last updated | 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 | 2.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.\n\n**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.\n\n## Before you call either\n\n### DeepSeek-V4-Flash-0731\n\n1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)\n2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template\n3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"\n4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max\n5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash\n6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory\n\n### Laguna XS 2.1\n\n1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF\n2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)\n3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds\n4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n6. Sampling per the card is temperature 1.0 and top_k 20\n7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Laguna XS 2.1?\n\nDeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Laguna XS 2.1's 61 (C), and leads in 0 of 4 scored categories. Laguna XS 2.1 leads on reach, risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"laguna-xs-2-1\"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 laguna-xs-2-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json\n\n## Other comparisons with DeepSeek-V4-Flash-0731 or Laguna XS 2.1\n\n- [Agents-A1-4B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.md)\n- [Agents-A1-4B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.md)\n- [Apertus v1.5 8B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.md)\n- [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.md)\n- [Apodex-1.1-mini vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731.md)\n- [Apodex-1.1-mini vs Laguna XS 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"https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "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": "DeepSeek-V4-Flash-0731 vs Laguna XS 2.1", "url": "" } ], "description": "DeepSeek-V4-Flash-0731 scores 62.4 (B) to Laguna XS 2.1's 61 (C) for open-weight models you run yourself. 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