{ "data": { "a": { "slug": "apodex-1-1-mini", "name": "Apodex-1.1-mini", "vendor": "Apodex AI", "vendorUrl": "https://www.apodex.com", "kind": "weights", "category": "open-weights", "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini", "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json", "repo": "https://huggingface.co/apodex/Apodex-1.1-mini", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-FP8" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-NVFP4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GPTQ-Int4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (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. Apodex's hosted API bills by account (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "gptq", "self-hosted" ], "lastRelease": "2026-08-25", "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": 29, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "strengths": [ "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls", "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE", "Apache 2.0 through the chain, with the base's licence file carried and no gate", "Apodex's own API serves the same model", "Safetensors and GGUF only, with checksums on every weight file" ], "weaknesses": [ "No safety results, and agent guidance only from the harness", "Apodex answered none of the four discussions", "The card states no parameters, architecture, limitations or knowledge cut-off", "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default", "Structured output isn't documented" ], "agentNotes": [ "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed", "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file", "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks", "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768", "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block", "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n --tool-call-parser qwen3_coder --reasoning-parser qwen3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apodex-1-1-mini" }, "weights": { "trainedBy": "Apodex AI", "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Apodex-1.1-mini-NVFP4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 24.3, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang" }, { "name": "Apodex-1.1-mini-GPTQ-Int4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4", "who": "publisher", "precision": "int4", "sizeGB": 24.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "The card serves it with SGLang" }, { "name": "apodex1.1-35b-Q4_K_M-MTP", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server" }, { "name": "Apodex-1.1-mini-FP8", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Apodex-1.1-mini", "url": "https://huggingface.co/apodex/Apodex-1.1-mini", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3", "runBuild": "Apodex-1.1-mini", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Apodex AI (apodex)", "family": "Apodex-1", "url": "https://www.anchorterminal.com/open-weights/#apodex", "others": [ { "name": "Apodex-1.0-mini", "url": "https://huggingface.co/apodex/Apodex-1.0-mini", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-4B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-2B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-0.8B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT", "params": "0.8B (873,438,784 by the Hub's safetensors count)" } ], "unrestrictedCopies": 2 }, "area": "models", "provenance": { "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)", "domain": "apodex.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.", "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.", "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.", "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.", "unchecked: the domain's registration date." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json" }, "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 0 of 4 scored categories. Apodex-1.1-mini leads on reach, risk 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": "Apodex AI", "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": "Apache-2.0", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-25", "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": "3.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 0 of 4 scored categories. Apodex-1.1-mini leads on reach, risk and documentation and upkeep.", "question": "Which is better for AI agents, Apodex-1.1-mini or GLM-4.7-Flash?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 80", "Risk, 65 against 55", "Documentation and upkeep, 38 against 28" ], "also": [ "Rated higher by the reviewer agents, 3.3 against 2.8 out of 5" ], "goodFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "slug": "apodex-1-1-mini", "watchFor": "No safety results, and agent guidance only from the harness" }, { "aheadOn": null, "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": 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}, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.json", "title": "GLM-4.7-Flash vs MiniMax-M2.7", "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": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apodex-1-1-mini": 85, "by": 5, "edge": "apodex-1-1-mini", "glm-4-7-flash": 80, "key": "reach", "name": "Reach", "weight": 25 }, { "apodex-1-1-mini": 65, "by": 10, "edge": "apodex-1-1-mini", "glm-4-7-flash": 55, "key": "risk", "name": "Risk", "weight": 20 }, { "apodex-1-1-mini": 85, "by": 0, "edge": "", "glm-4-7-flash": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "apodex-1-1-mini": 38, "by": 10, "edge": "apodex-1-1-mini", "glm-4-7-flash": 28, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 0 of 4 scored categories. Apodex-1.1-mini leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "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/apodex-1-1-mini-vs-glm-4-7-flash", "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.min.md" }, "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 0 of 4 scored categories. Apodex-1.1-mini leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Apodex-1.1-mini: grade C, 61/100, rank #36 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.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### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nAhead on:\n- Reach, 85 against 80\n- Risk, 65 against 55\n- Documentation and upkeep, 38 against 28\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.8 out of 5\n\nWatch for: No safety results, and agent guidance only from the harness\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\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 | Apodex-1.1-mini | GLM-4.7-Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 80 | Apodex-1.1-mini +5 |\n| Risk | 20% (20 this run) | 65 | 55 | Apodex-1.1-mini +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 38 | 28 | Apodex-1.1-mini +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **63 · B** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | GLM-4.7-Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | 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 | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-25 | 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 | 3.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\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### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\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, Apodex-1.1-mini or GLM-4.7-Flash?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 0 of 4 scored categories. Apodex-1.1-mini leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"glm-4-7-flash\"}`. From a terminal: `anchor compare apodex-1-1-mini glm-4-7-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json and https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json\n\n## Other comparisons with Apodex-1.1-mini or GLM-4.7-Flash\n\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.md)\n- [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md)\n- [Apertus v1.5 8B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.md)\n- [Apodex-1.1-mini vs BigBang-v1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.md)\n- [Apodex-1.1-mini vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.md)\n- [Apodex-1.1-mini vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus.md)\n- [Apodex-1.1-mini vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.md)\n- [Apodex-1.1-mini vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.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 DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it.md)\n- [Apodex-1.1-mini vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.md)\n- [Apodex-1.1-mini vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.md)\n- [Apodex-1.1-mini vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.md)\n- [Apodex-1.1-mini vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it.md)\n- [Apodex-1.1-mini vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it.md)\n- [Apodex-1.1-mini vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.md)\n- [Apodex-1.1-mini vs GLM-5.3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.md)\n- [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- [Apodex-1.1-mini vs gpt-oss-120b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b.md)\n- [Apodex-1.1-mini vs gpt-oss-20b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b.md)\n- [Apodex-1.1-mini vs Granite 4.2 3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.md)\n- [Apodex-1.1-mini vs Hy3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.md)\n- [Apodex-1.1-mini vs Inkling](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.md)\n- [Apodex-1.1-mini vs Inkling-Small](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-small.md)\n- [Apodex-1.1-mini vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.md)\n- [Apodex-1.1-mini vs Kolibri-1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.md)\n- [Apodex-1.1-mini vs Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.md)\n- [Apodex-1.1-mini vs Laguna S 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md)\n- [Apodex-1.1-mini vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.md)\n- [Apodex-1.1-mini vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [Apodex-1.1-mini vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.md)\n- [Apodex-1.1-mini vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Apodex-1.1-mini vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [Apodex-1.1-mini vs MiniCPM5-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm5-2b.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.md)\n- [Apodex-1.1-mini vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b.md)\n- [Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Apodex-1.1-mini vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-ultra-550b-a55b.md)\n- [Apodex-1.1-mini vs occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [Apodex-1.1-mini vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.md)\n- [Apodex-1.1-mini vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.md)\n- [Apodex-1.1-mini vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- [Apodex-1.1-mini vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.md)\n- [Apodex-1.1-mini vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Apodex-1.1-mini vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-4b-instruct-2507.md)\n- [Apodex-1.1-mini vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-0-8b.md)\n- [Apodex-1.1-mini vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.md)\n- [Apodex-1.1-mini vs Qwen3.5-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-2b.md)\n- [Apodex-1.1-mini vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-397b-a17b.md)\n- [Apodex-1.1-mini vs Qwen3.5-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-4b.md)\n- [Apodex-1.1-mini vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.md)\n- [Apodex-1.1-mini vs Qwen3.6-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-27b.md)\n- [Apodex-1.1-mini vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b.md)\n- [Apodex-1.1-mini vs Qwen3.8-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-27b.md)\n- [Apodex-1.1-mini vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-flash-next.md)\n- [Apodex-1.1-mini vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-next.md)\n- [Apodex-1.1-mini vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-2b-instruct.md)\n- [Apodex-1.1-mini vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-32b-instruct.md)\n- [Apodex-1.1-mini vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-4b-instruct.md)\n- [Apodex-1.1-mini vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-8b-instruct.md)\n- [Apodex-1.1-mini vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2.md)\n- [Apodex-1.1-mini vs SmolLM3-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.md)\n- [Apodex-1.1-mini vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b.md)\n- [Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b.md)\n- [Apodex-1.1-mini vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Apodex-1.1-mini vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-thinkingcap-qwen3-8-27b.md)\n- [BigBang-v1 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.md)\n- [Cosmos-Reason2-2B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.md)\n- [Darwin-36B-Opus vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.md)\n- [DeepSeek-V3.2 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.md)\n- [DeepSeek V4.1 Flash vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.md)\n- [DiffusionGemma 26B A4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 12B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 26B A4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 31B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E2B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-7-flash.md)\n- [GLM-4.5-Air vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.md)\n- [GLM-4.7-Flash vs GLM-5.3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.md)\n- [GLM-4.7-Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.md)\n- [GLM-4.7-Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-120b.md)\n- [GLM-4.7-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-20b.md)\n- [GLM-4.7-Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.md)\n- [GLM-4.7-Flash vs Hy3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.md)\n- [GLM-4.7-Flash vs Inkling](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling.md)\n- [GLM-4.7-Flash vs Inkling-Small](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small.md)\n- [GLM-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-4.7-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.md)\n- [GLM-4.7-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1.md)\n- [GLM-4.7-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-m-1.md)\n- [GLM-4.7-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.md)\n- [GLM-4.7-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1.md)\n- [GLM-4.7-Flash vs 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