{ "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": "GLM-4.7-Flash and DeepSeek-V4-Flash-0731 score within a point of each other on agent readiness, 63 (B) and 62.4 (B). DeepSeek-V4-Flash-0731 leads on capability and documentation and upkeep.", "b": { "slug": "glm-4-7-flash", "name": "GLM-4.7-Flash", "vendor": "Z.ai (Zhipu AI)", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-4.7-Flash is Z.ai's open-weight mixture-of-experts reasoning model, 30B parameters with about 3B active per token, for English and Chinese, published on Hugging Face in BF16 under the MIT licence.", "url": "https://www.anchorterminal.com/tools/glm-4-7-flash", "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json", "repo": "https://huggingface.co/zai-org/GLM-4.7-Flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-4.7-Flash" }, { "registry": "ollama", "name": "glm-4.7-flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named on the card. Running it yourself costs the hardware, 62.5 GB for the BF16 files and 19 GB for Ollama's default build. Z.ai's own API also serves GLM-4.7-Flash, which its docs describe as completely free (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/zai-org/GLM-4.7-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "chinese", "mit", "safetensors", "self-hosted" ], "lastRelease": "2026-01-29", "graded": true, "anchor": { "graded": true, "score": 63, "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": 21, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 59, "interface": 85, "reach": 80, "risk": 55, "upkeep": 28 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.", "bestFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime", "strengths": [ "MIT weights with no use policy, downloadable with no account", "Model code in released vLLM, SGLang, Transformers and llama.cpp, and a local Ollama build", "Ollama's 19 GB build fits a 24 GB GPU, and BF16 fits our reference machine", "Z.ai documents the glm47 tool parser, OpenAI-style tool declarations and turning thinking off per request", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard" ], "weaknesses": [ "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January", "No quantised build from Z.ai and no LICENSE file in the repository", "No safety evaluation, agent-use guidance or security contact", "The card leaves out intended use, limitations, context length, knowledge cut-off and training data", "config.json and tokenizer_config.json disagree on context length" ], "agentNotes": [ "On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`", "Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build", "Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text", "Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026", "Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default", "Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench", "Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63 } ], "editorialScores": { "capability": 59, "interface": 85, "reach": 80, "risk": 55, "upkeep": 28 }, "provenanceScore": 68, "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": "ollama run glm-4.7-flash\n# or, per Z.ai's card:\nvllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 \\\n --enable-auto-tool-choice --served-model-name glm-4.7-flash", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"glm-4.7-flash\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-4-7-flash" }, "sameCompany": [ "glm-4-5-air", "glm-5-3", "glm-5-3-flash" ], "weights": { "trainedBy": "Z.ai", "params": "30B", "active": "3B", "context": 202752, "goodFor": "Small mixture-of-experts reasoning model for English and Chinese with tool calling, a local coding and agent model.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama glm-4.7-flash:latest", "url": "https://ollama.com/library/glm-4.7-flash:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "GLM-4.7-Flash", "url": "https://huggingface.co/zai-org/GLM-4.7-Flash", "who": "publisher", "precision": "bf16", "sizeGB": 62.4, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash", "runBuild": "GLM-4.7-Flash", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-4", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-4-9B", "url": "https://huggingface.co/zai-org/glm-4-9b", "params": "9B" }, { "name": "GLM-4-9B-Chat", "url": "https://huggingface.co/zai-org/glm-4-9b-chat", "params": "9B" }, { "name": "GLM-4-9B-Chat-1M", "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m", "params": "9B" }, { "name": "GLM-4V-9B", "url": "https://huggingface.co/zai-org/glm-4v-9b", "params": "9B" }, { "name": "GLM-4-9B-0414", "url": "https://huggingface.co/zai-org/GLM-4-9B-0414", "params": "9B" }, { "name": "GLM-4-32B-Base-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414", "params": "32B" }, { "name": "GLM-4-32B-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-0414", "params": "32B" }, { "name": "GLM-Z1-9B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414", "params": "9B" }, { "name": "GLM-Z1-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414", "params": "32B" }, { "name": "GLM-Z1-Rumination-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414", "params": "32B" }, { "name": "GLM-4.1V-9B-Base", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base", "params": "9B" }, { "name": "GLM-4.1V-9B-Thinking", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking", "params": "9B" }, { "name": "GLM-4.5-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Base", "params": "355B-A32B" }, { "name": "GLM-4.5", "url": "https://huggingface.co/zai-org/GLM-4.5", "params": "355B-A32B" }, { "name": "GLM-4.5-Air-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base", "params": "106B-A12B" }, { "name": "GLM-4.5-Air", "url": "https://huggingface.co/zai-org/GLM-4.5-Air", "params": "106B-A12B", "listing": "https://www.anchorterminal.com/tools/glm-4-5-air" }, { "name": "GLM-4.5V", "url": "https://huggingface.co/zai-org/GLM-4.5V", "params": "106B-A12B" }, { "name": "GLM-4.6", "url": "https://huggingface.co/zai-org/GLM-4.6", "params": "355B-A32B" }, { "name": "GLM-4.6V", "url": "https://huggingface.co/zai-org/GLM-4.6V", "params": "106B" }, { "name": "GLM-4.6V-Flash", "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash", "params": "9B" }, { "name": "GLM-4.7", "url": "https://huggingface.co/zai-org/GLM-4.7", "params": "355B-A32B" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai)", "domain": "z.ai", "domainRegistered": "2017-12-15", "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-4.7-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Z.ai's terms of use name JINGSHENG HENGXING TECHNOLOGY PTE.LTD as the operator of Z.ai, and RDAP names the same company as the registrant of z.ai. The weights' card names no legal entity and the repository has no LICENSE file.", "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 12 commits from 19 to 29 January 2026, mostly card and benchmark updates.", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Z.ai (Zhipu AI)", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-31", "b": "2026-01-29", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-4.7-Flash and DeepSeek-V4-Flash-0731 score within a point of each other on agent readiness, 63 (B) and 62.4 (B). DeepSeek-V4-Flash-0731 leads on capability and documentation and upkeep.", "question": "Which is better for AI agents, DeepSeek-V4-Flash-0731 or GLM-4.7-Flash?" } ], "goodFor": [ { "aheadOn": [ "Capability, 66 against 59", "Documentation and upkeep, 43 against 28" ], "also": null, "goodFor": "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", "slug": "deepseek-v4-flash-0731", "watchFor": "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine" }, { "aheadOn": [ "Reach, 80 against 70", "Risk, 55 against 50", "Agent interface, 85 against 75" ], "also": null, "goodFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime", "slug": "glm-4-7-flash", "watchFor": "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.json", "title": "Agents-A1-4B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.json", "title": "Agents-A1-4B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json", "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json", "title": "Apertus v1.5 8B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash" }, { 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"url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b.json", "title": "GLM-4.7-Flash vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-30b-a3b.json", "title": "GLM-4.7-Flash vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.json", "title": "GLM-4.7-Flash vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "GLM-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-super-120b-a12b.json", "title": "GLM-4.7-Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-ultra-550b-a55b.json", "title": "GLM-4.7-Flash vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.json", "title": "GLM-4.7-Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-olmo-3-7b-think.json", "title": "GLM-4.7-Flash vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.json", "title": "GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507.json", "title": "GLM-4.7-Flash vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b.json", "title": "GLM-4.7-Flash vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b.json", "title": "GLM-4.7-Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.json", "title": "GLM-4.7-Flash vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b.json", "title": "GLM-4.7-Flash vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-4b.json", "title": "GLM-4.7-Flash vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.json", "title": "GLM-4.7-Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b.json", "title": "GLM-4.7-Flash vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b.json", "title": "GLM-4.7-Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-27b.json", "title": "GLM-4.7-Flash vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next.json", "title": "GLM-4.7-Flash vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "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-4-7-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-2b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2.json", "title": "GLM-4.7-Flash vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b.json", "title": "GLM-4.7-Flash vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.json", "title": "GLM-4.7-Flash vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 7, "deepseek-v4-flash-0731": 66, "edge": "deepseek-v4-flash-0731", "glm-4-7-flash": 59, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 10, "deepseek-v4-flash-0731": 70, "edge": "glm-4-7-flash", "glm-4-7-flash": 80, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "deepseek-v4-flash-0731": 50, "edge": "glm-4-7-flash", "glm-4-7-flash": 55, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 10, "deepseek-v4-flash-0731": 75, "edge": "glm-4-7-flash", "glm-4-7-flash": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 15, "deepseek-v4-flash-0731": 43, "edge": "deepseek-v4-flash-0731", "glm-4-7-flash": 28, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.7-Flash and DeepSeek-V4-Flash-0731 score within a point of each other on agent readiness, 63 (B) and 62.4 (B). DeepSeek-V4-Flash-0731 leads on capability and documentation and upkeep. 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.", "glm-4-7-flash": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash", "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.min.md" }, "markdown": "GLM-4.7-Flash and DeepSeek-V4-Flash-0731 score within a point of each other on agent readiness, 63 (B) and 62.4 (B). DeepSeek-V4-Flash-0731 leads on capability and documentation and upkeep. 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- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.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\nAhead on:\n- Capability, 66 against 59\n- Documentation and upkeep, 43 against 28\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### GLM-4.7-Flash (B)\n\nGood for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime\n\nAhead on:\n- Reach, 80 against 70\n- Risk, 55 against 50\n- Agent interface, 85 against 75\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V4-Flash-0731 | GLM-4.7-Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 66 | 59 | DeepSeek-V4-Flash-0731 +7 |\n| Reach | 25% (25 this run) | 70 | 80 | GLM-4.7-Flash +10 |\n| Risk | 20% (20 this run) | 50 | 55 | GLM-4.7-Flash +5 |\n| Agent interface | 10% (10 this run) | 75 | 85 | GLM-4.7-Flash +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 28 | DeepSeek-V4-Flash-0731 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **63 · B** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | GLM-4.7-Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Z.ai (Zhipu AI) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-01-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (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**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.\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### GLM-4.7-Flash\n\n1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`\n2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build\n3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text\n4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026\n5. Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default\n6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench\n7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V4-Flash-0731 or GLM-4.7-Flash?\n\nGLM-4.7-Flash and DeepSeek-V4-Flash-0731 score within a point of each other on agent readiness, 63 (B) and 62.4 (B). DeepSeek-V4-Flash-0731 leads on capability and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"glm-4-7-flash\"}`. 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