{ "data": { "a": { "slug": "gemma-4-e4b-it", "name": "Gemma 4 E4B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 E4B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 4.5B effective parameters (8B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-e4b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json", "repo": "https://huggingface.co/google/gemma-4-E4B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-E4B-it" }, { "registry": "huggingface", "name": "google/gemma-4-E4B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-E4B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:e4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 17.9 GB at BF16, 8.9 GB at SFP8, 4.5 GB at Q4_0, 2.5 GB for the mobile build and 2.2 GB for the mobile build text only to load the weights per Google (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://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "on-device", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 63.2, "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": 19, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 27, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.", "bestFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning", "strengths": [ "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file", "Google's own 4-bit QAT GGUF needs about 7.4 GB with working space", "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers", "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser", "131,072 tokens of native context and thinking that turns on or off per request", "Takes audio as well as images and video frames" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "No hosted version, since the Gemini API serves only the two largest sizes", "Training data described only by type and cut-off", "Google's own card numbers put it far below the 12B, 26B A4B and 31B on agent and coding tasks", "Chat-template reports since June wait without a reply" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio", "With Ollama run `ollama run gemma4:e4b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K", "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there", "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns", "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.2 } ], "editorialScores": { "capability": 27, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "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 gemma4:e4b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:e4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-e4b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-E4B", "params": "8B", "context": 131072, "goodFor": "Small on-device assistant and agent model, 4.5B effective, with tool calling and optional thinking, taking text, images, video frames and short audio clips for transcription and translation.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-E4B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-q4_0-gguf/blob/main/gemma-4-E4B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 6.15, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 5.15 GB model and the 0.99 GB projector (gemma-4-E4B-it-mmproj.gguf) for image and audio input" }, { "name": "gemma-4-E4B-it-qat-mobile-ct", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-ct", "who": "publisher", "precision": "wNa8o8", "sizeGB": 3.77, "memoryGB": 8, "format": "safetensors", "note": "Google's mobile scheme with 2-bit decoding layers, so below 4-bit in places and not the build Reach's memory line reads. Compressed-tensors format" }, { "name": "gemma-4-E4B-it-qat-mobile-transformers", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-transformers", "who": "publisher", "precision": "wNa8o8", "sizeGB": 3.56, "memoryGB": 8, "format": "safetensors", "note": "The same mobile scheme as a Transformers reference, which Google says other formats are built from. Google's mobile memory figures are for LiteRT-LM" }, { "name": "ollama gemma4:e4b", "url": "https://ollama.com/library/gemma4:e4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (5.5 GB) with a BF16 vision projector (992 MB) and a Q8_0 draft model (99 MB) (digest dc35e8d9c606), the llama.cpp variant. gemma4:e4b-it-qat (6.1 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-e4b", "url": "https://lmstudio.ai/models/google/gemma-4-e4b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 5.9, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E4B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio" }, { "name": "gemma-4-E4B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 11.55, "memoryGB": 16, "format": "safetensors", "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang. The per-layer embedding tables stay large, so it's bigger than the GGUF" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "who": "publisher", "precision": "bf16", "sizeGB": 16.02, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072", "runBuild": "gemma-4-E4B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai keeps a dated releases page for the Gemma family (this size on 31 March 2026). The Hugging Face commit history says what each change fixed.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.json" }, "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep.", "b": { "slug": "glm-5-3", "name": "GLM-5.3", "vendor": "Z.ai (Zhipu AI)", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-5.3 is Z.ai's open-weight mixture-of-experts model for coding and long-horizon agent work, 744B parameters with 40B active and a 1M-token context, published on Hugging Face in FP8 and BF16 under its own licence.", "url": "https://www.anchorterminal.com/tools/glm-5-3", "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3.json", "repo": "https://huggingface.co/zai-org/GLM-5.3", "license": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-5.3" }, { "registry": "huggingface", "name": "zai-org/GLM-5.3-BF16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the GLM-5.3 License. A licensee running a model-as-a-service business with more than US$10 billion of revenue in 12 months must pass Z.AI's security review before commercial use. Running it yourself costs the hardware, 755.7 GB of FP8 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/zai-org/GLM-5.3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "coding", "long-context", "custom-licence", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-10-14", "graded": true, "anchor": { "graded": true, "score": 64.6, "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": 14, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 80, "interface": 70, "reach": 55, "risk": 50, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.", "bestFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider", "strengths": [ "Model code in released llama.cpp b11768, vLLM 0.28.0, SGLang 0.5.22 and Transformers 5.12.0", "1,048,576 tokens of native context and three reasoning budgets per request", "Template renders several tool calls per turn, parsed by glm47 in vLLM and SGLang", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard", "Downloadable with no account, and hosted by Z.ai and five live providers on Hugging Face" ], "weaknesses": [ "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build", "No safety evaluation, agent-use guidance or security contact", "Thinking is always on, and reasoning_effort only lowers it", "Commercial use by model-as-a-service operators above US$10 billion revenue needs Z.AI's security review", "The card leaves out limitations, architecture, parameters and cut-off, and zai-org answered 2 of 16 discussions, none since 8 September" ], "agentNotes": [ "Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)", "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text", "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max", "Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default", "Don't send `enable_thinking: false`; the template ignores it and always opens a think block", "Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API", "No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe" ], "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": 64.6 } ], "editorialScores": { "capability": 80, "interface": 70, "reach": 55, "risk": 50, "upkeep": 58 }, "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": "# vLLM's recipe, FP8 on one 8xH200 node\nuv pip install \"vllm\u003e=0.28.0\" --torch-backend=auto\nvllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 \\\n --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 \\\n --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice \\\n --served-model-name glm-5.3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"glm-5.3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"reasoning_effort\": \"low\", \"clear_thinking\": true}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-5-3" }, "sameCompany": [ "glm-4-5-air", "glm-4-7-flash", "glm-5-3-flash" ], "weights": { "trainedBy": "Z.ai", "builtOn": "GLM-5.2's base model (same base, new post-training)", "params": "744B", "active": "40B", "context": 1048576, "goodFor": "Large model for coding and long-horizon agent work with tool calling and always-on reasoning, text only.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-5.3", "url": "https://huggingface.co/zai-org/GLM-5.3", "who": "publisher", "precision": "fp8", "sizeGB": 755.6, "memoryGB": 1024, "format": "safetensors" }, { "name": "GLM-5.3-BF16", "url": "https://huggingface.co/zai-org/GLM-5.3-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 1506.7, "memoryGB": 2048, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3", "runBuild": "GLM-5.3", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-5", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-5", "url": "https://huggingface.co/zai-org/GLM-5", "params": "744B-A40B" }, { "name": "GLM-5.1", "url": "https://huggingface.co/zai-org/GLM-5.1", "params": "744B-A40B" }, { "name": "GLM-5.2", "url": "https://huggingface.co/zai-org/GLM-5.2", "params": "744B-A40B" }, { "name": "GLM-5.3-Flash", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash", "params": "320B-A18B", "listing": "https://www.anchorterminal.com/tools/glm-5-3-flash" } ], "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). The licence names 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-5.3/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' LICENSE names Z.AI as the copyright holder.", "Nothing is hosted for this listing, so the GLM-5.3 License stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 11 commits from 27 August to 14 October 2026, whose messages say what changed (two chat template fixes, on 4 September and 14 October, and evaluation results).", "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-5-3.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "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": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-10-14", "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": "3.5/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Gemma 4 E4B IT or GLM-5.3?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 55", "Risk, 70 against 50", "Agent interface, 85 against 70", "Documentation and upkeep, 75 against 58" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning", "slug": "gemma-4-e4b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": [ "Capability, 80 against 27" ], "also": null, "goodFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider", "slug": "glm-5-3", "watchFor": "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.json", "title": "Agents-A1-4B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.json", "title": "Agents-A1-4B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json", "title": "Apertus v1.5 8B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3" }, { "json": 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"https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b.json", "title": "GLM-5.3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b.json", "title": "GLM-5.3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-30b-a3b-instruct-2507.json", "title": "GLM-5.3 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-4b-instruct-2507.json", "title": "GLM-5.3 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-0-8b.json", "title": "GLM-5.3 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-122b-a10b.json", "title": "GLM-5.3 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-2b.json", "title": "GLM-5.3 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-397b-a17b.json", "title": "GLM-5.3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-4b.json", "title": "GLM-5.3 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b.json", "title": "GLM-5.3 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-27b.json", "title": "GLM-5.3 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-35b-a3b.json", "title": "GLM-5.3 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-27b.json", "title": "GLM-5.3 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-flash-next.json", "title": "GLM-5.3 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-30b-a3b-instruct.json", "title": "GLM-5.3 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-next.json", "title": "GLM-5.3 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "GLM-5.3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-2b-instruct.json", "title": "GLM-5.3 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-30b-a3b-instruct.json", "title": "GLM-5.3 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-32b-instruct.json", "title": "GLM-5.3 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-4b-instruct.json", "title": "GLM-5.3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-8b-instruct.json", "title": "GLM-5.3 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2.json", "title": "GLM-5.3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-smollm3-3b.json", "title": "GLM-5.3 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b.json", "title": "GLM-5.3 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b.json", "title": "GLM-5.3 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next.json", "title": "GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-thinkingcap-qwen3-8-27b.json", "title": "GLM-5.3 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 53, "edge": "glm-5-3", "gemma-4-e4b-it": 27, "glm-5-3": 80, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 40, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 95, "glm-5-3": 55, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 20, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 70, "glm-5-3": 50, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 15, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 85, "glm-5-3": 70, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 17, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 75, "glm-5-3": 58, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-e4b-it": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.", "glm-5-3": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3", "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.min.md" }, "markdown": "GLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Gemma 4 E4B IT: grade B, 63.2/100, rank #24 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json\n- GLM-5.3: grade B, 64.6/100, rank #17 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.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### Gemma 4 E4B IT (B)\n\nGood for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning\n\nAhead on:\n- Reach, 95 against 55\n- Risk, 70 against 50\n- Agent interface, 85 against 70\n- Documentation and upkeep, 75 against 58\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: No safety evaluation figures published\n\n### GLM-5.3 (B)\n\nGood for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider\n\nAhead on:\n- Capability, 80 against 27\n\nWatch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 E4B IT | GLM-5.3 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 27 | 80 | GLM-5.3 +53 |\n| Reach | 25% (25 this run) | 95 | 55 | Gemma 4 E4B IT +40 |\n| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 E4B IT +20 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E4B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 58 | Gemma 4 E4B IT +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **64.6 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E4B IT | GLM-5.3 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | 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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-10-14 |\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 | 3.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 E4B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.\n\n**GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.\n\n## Before you call either\n\n### Gemma 4 E4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e4b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n\n### GLM-5.3\n\n1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default\n5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block\n6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API\n7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 E4B IT or GLM-5.3?\n\nGLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e4b-it\", \"b\": \"glm-5-3\"}`. From a terminal: `anchor compare gemma-4-e4b-it glm-5-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/glm-5-3.json\n\n## Other comparisons with Gemma 4 E4B IT or GLM-5.3\n\n- [Agents-A1-4B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.md)\n- [Agents-A1-4B vs GLM-5.3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.md)\n- [Apertus v1.5 8B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.md)\n- [Apertus v1.5 8B vs GLM-5.3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.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-5.3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.md)\n- [BigBang-v1 vs Gemma 4 E4B 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research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemma 4 E4B IT vs GLM-5.3", "url": "" } ], "description": "GLM-5.3 scores 64.6 (B) to Gemma 4 E4B IT's 63.2 (B) for open-weight models you run yourself. 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