{ "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": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Qwen3.8-Flash-Next's 61.8 (C), and leads in 4 of 5 scored categories. Qwen3.8-Flash-Next leads on capability.", "b": { "slug": "qwen3-8-flash-next", "name": "Qwen3.8-Flash-Next", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.8-Flash-Next is Alibaba's open-weight multimodal mixture-of-experts model and an experimental preview of the Qwen4 architecture, 125B parameters with 6B active plus a 51B n-gram embedding table, published on Hugging Face in BF16 and FP8.", "url": "https://www.anchorterminal.com/tools/qwen3-8-flash-next", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json", "repo": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "license": "Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.8-Flash-Next" }, { "registry": "huggingface", "name": "Qwen/Qwen3.8-Flash-Next-FP8" }, { "registry": "ollama", "name": "qwen3.8-flash-next" } ], "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 under the Qwen Community License 1.0. A licensee whose group runs a Model as a Service or AI Work Assistant business (AI coding or office productivity products) needs a separate licence from Qwen, with no published price, before any commercial use beyond internal use. Running it yourself costs the hardware, 360.0 GB for the BF16 files, 185.5 GB for FP8 and 105 GB for Ollama's MLX build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "source-available", "safetensors", "fp8", "self-hosted", "preview" ], "lastRelease": "2026-08-31", "graded": true, "anchor": { "graded": true, "score": 61.8, "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": 25, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 74, "interface": 70, "reach": 65, "risk": 45, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.", "bestFor": "Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "262,144 tokens of native context, several tool calls per turn, and thinking that turns off per request with three effort levels", "Safetensors only, a chat template in the repository and a SHA-256 for every weight file", "No gate on the download, and Alibaba's Qwen Cloud serves it for agents without the hardware" ], "weaknesses": [ "No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB", "The Qwen Community License requires a separate licence before commercial use by a model-as-a-service or coding or office assistant business", "No safety results, agent guidance, training data, limitations, languages or knowledge cut-off published", "Labelled an experimental preview, and Qwen has answered none of the 40 most recent discussions", "The tool-calling format is only in the template, and structured output isn't documented" ], "agentNotes": [ "On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share", "On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off", "`reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error", "Send at most one system message and put it first, since the template raises an error otherwise", "Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without", "vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented", "Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.8 } ], "editorialScores": { "capability": 74, "interface": 70, "reach": 65, "risk": 45, "upkeep": 36 }, "provenanceScore": 49, "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 qwen3.8-flash-next:125b-mlx\n# or, as Qwen's GitHub README serves it (4 GPUs):\nvllm serve Qwen/Qwen3.8-Flash-Next --port 8000 --tensor-parallel-size 4 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Qwen/Qwen3.8-Flash-Next\",\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/qwen3-8-flash-next" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "125B", "active": "6B", "context": 262144, "goodFor": "Experimental preview of the Qwen4 architecture: a multimodal mixture-of-experts model with tool calling and adjustable reasoning effort.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.8-Flash-Next-FP4-Local", "url": "https://huggingface.co/nvidia/Qwen3.8-Flash-Next-FP4-Local", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 102.2, "memoryGB": 128, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. 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Cloud (Singapore) Private Limited, the registrant of qwen.ai. 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The BF16 repository's commit messages are an upload and README updates; the FP8 repository's say what changed.", "qwen.ai redirects /.well-known/security.txt to its home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-08-31", "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": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Qwen3.8-Flash-Next's 61.8 (C), and leads in 4 of 5 scored categories. Qwen3.8-Flash-Next leads on capability.", "question": "Which is better for AI agents, Gemma 4 E4B IT or Qwen3.8-Flash-Next?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 65", "Risk, 70 against 45", "Agent interface, 85 against 70", "Documentation and upkeep, 75 against 36" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.0 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, 74 against 27" ], "also": null, "goodFor": "Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.", "slug": "qwen3-8-flash-next", "watchFor": "No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB" } ], "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-qwen3-8-flash-next.json", "title": "Agents-A1-4B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-flash-next" }, { "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-qwen3-8-flash-next.json", "title": "Apertus v1.5 8B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-flash-next.json", "title": "Apodex-1.1-mini vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it.json", "title": "BigBang-v1 vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next.json", "title": "BigBang-v1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it.json", "title": 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"title": "Qwen3.5-9B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.json", "title": "Qwen3.6-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next.json", "title": "Qwen3.8-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next.json", "title": "Qwen3.8-Flash-Next vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.json", "title": "Qwen3.8-Flash-Next vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.json", "title": "Qwen3.8-Flash-Next vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 47, "edge": "qwen3-8-flash-next", "gemma-4-e4b-it": 27, "key": "capability", "name": "Capability", "qwen3-8-flash-next": 74, "weight": 35 }, { "by": 30, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 95, "key": "reach", "name": "Reach", "qwen3-8-flash-next": 65, "weight": 25 }, { "by": 25, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 70, "key": "risk", "name": "Risk", "qwen3-8-flash-next": 45, "weight": 20 }, { "by": 15, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 85, "key": "interface", "name": "Agent interface", "qwen3-8-flash-next": 70, "weight": 10 }, { "by": 39, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-8-flash-next": 36, "weight": 10 } ], "summary": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Qwen3.8-Flash-Next's 61.8 (C), and leads in 4 of 5 scored categories. Qwen3.8-Flash-Next leads on capability. 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.", "qwen3-8-flash-next": "A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next", "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.min.md" }, "markdown": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Qwen3.8-Flash-Next's 61.8 (C), and leads in 4 of 5 scored categories. Qwen3.8-Flash-Next leads on capability. 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- Qwen3.8-Flash-Next: grade C, 61.8/100, rank #31 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 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 65\n- Risk, 70 against 45\n- Agent interface, 85 against 70\n- Documentation and upkeep, 75 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.0 out of 5\n\nWatch for: No safety evaluation figures published\n\n### Qwen3.8-Flash-Next (C)\n\nGood for: Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.\n\nAhead on:\n- Capability, 74 against 27\n\nWatch for: No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 E4B IT | Qwen3.8-Flash-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 27 | 74 | Qwen3.8-Flash-Next +47 |\n| Reach | 25% (25 this run) | 95 | 65 | Gemma 4 E4B IT +30 |\n| Risk | 20% (20 this run) | 70 | 45 | Gemma 4 E4B IT +25 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E4B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 36 | Gemma 4 E4B IT +39 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **61.8 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E4B IT | Qwen3.8-Flash-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-08-31 |\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) | 2/5 (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**Qwen3.8-Flash-Next.** A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.\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### Qwen3.8-Flash-Next\n\n1. On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share\n2. On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off\n5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Send at most one system message and put it first, since the template raises an error otherwise\n7. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without\n8. vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe\n9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n10. Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 E4B IT or Qwen3.8-Flash-Next?\n\nGemma 4 E4B IT scores 63.2 (B) on agent readiness against Qwen3.8-Flash-Next's 61.8 (C), and leads in 4 of 5 scored categories. Qwen3.8-Flash-Next leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e4b-it\", \"b\": \"qwen3-8-flash-next\"}`. From a terminal: `anchor compare gemma-4-e4b-it qwen3-8-flash-next`\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/qwen3-8-flash-next.json\n\n## Other comparisons with Gemma 4 E4B IT or Qwen3.8-Flash-Next\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 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-flash-next.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 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-flash-next.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 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GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air.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- [Gemma 4 E4B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.md)\n- [Gemma 4 E4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E4B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 E4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 E4B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.md)\n- [Gemma 4 E4B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling.md)\n- [Gemma 4 E4B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling-small.md)\n- [Gemma 4 E4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 E4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.md)\n- [Gemma 4 E4B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1.md)\n- [Gemma 4 E4B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1.md)\n- [Gemma 4 E4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E4B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 E4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 E4B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm5-2b.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 E4B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 E4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.md)\n- [Gemma 4 E4B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E4B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 E4B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 E4B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 E4B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 E4B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 E4B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 E4B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 E4B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 E4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-smollm3-3b.md)\n- [Gemma 4 E4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 E4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-4.5-Air vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-8-flash-next.md)\n- [GLM-4.7-Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next.md)\n- [GLM-5.3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-flash-next.md)\n- [gpt-oss-120b vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-8-flash-next.md)\n- [gpt-oss-20b vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-flash-next.md)\n- [Granite 4.2 3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next.md)\n- [Hy3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-flash-next.md)\n- [Inkling-Small vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-flash-next.md)\n- [Inkling vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-flash-next.md)\n- [Kimi K3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next.md)\n- [Kolibri-1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next.md)\n- [Laguna M.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.md)\n- [Laguna S 2.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next.md)\n- [Laguna XS 2.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.md)\n- [LFM2.5-2.6B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.md)\n- [Ling-3.0-tiny vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-8-flash-next.md)\n- [MiniCPM-o 4.5 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next.md)\n- [MiniCPM5-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-flash-next.md)\n- [MiniMax-M2.5 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-flash-next.md)\n- [MiniMax-M2.7 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.md)\n- [Muse Glimmer 30B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next.md)\n- [Nemotron 3 Nano 30B A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next.md)\n- [Nemotron 3 Nano 4B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-flash-next.md)\n- [Nemotron 3 Super 120B A12B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next.md)\n- [Nemotron 3 Ultra 550B A55B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next.md)\n- [occamy-1.0 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.md)\n- [Olmo 3 7B Think vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.md)\n- [Ornith-1.5-35B-A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next.md)\n- [Ornith-1.5-397B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.md)\n- [Ornith-1.5-9B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-flash-next.md)\n- [Qwen3-4B-Instruct-2507 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-flash-next.md)\n- [Qwen3.5-0.8B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next.md)\n- [Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next.md)\n- [Qwen3.5-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-flash-next.md)\n- [Qwen3.5-397B-A17B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-flash-next.md)\n- [Qwen3.5-4B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.md)\n- [Qwen3.5-9B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.md)\n- [Qwen3.6-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.md)\n- [Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.md)\n- [Qwen3.8-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next.md)\n- [Qwen3.8-Flash-Next vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next.md)\n- [Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct.md)\n- [Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.md)\n- [Qwen3.8-Flash-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.md)\n- [Qwen3.8-Flash-Next vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.md)\n- [Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemma 4 E4B IT vs Qwen3.8-Flash-Next", "url": "" } ], "description": "Gemma 4 E4B IT scores 63.2 (B) to Qwen3.8-Flash-Next's 61.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemma 4 E4B IT B 63.2", "Qwen3.8-Flash-Next C 61.8", "scores" ], "h1": "Gemma 4 E4B IT vs Qwen3.8-Flash-Next", "image": "https://www.anchorterminal.com/assets/og/compare-gemma-4-e4b-it-vs-qwen3-8-flash-next.png", "path": "/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next", "published": "2026-10-01", "section": "tools", "title": "Gemma 4 E4B IT vs Qwen3.8-Flash-Next for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next" }, "tokens": { "markdown": 6550, "slim": 580 }, "version": 1 }