{ "data": { "a": { "slug": "glm-5-3-flash", "name": "GLM-5.3-Flash", "vendor": "Z.ai", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-5.3-Flash is Z.ai's open-weight multimodal mixture-of-experts model, 320B parameters with 18B active and a 1,048,576-token context, published on Hugging Face in FP8 and BF16 under the MIT licence.", "url": "https://www.anchorterminal.com/tools/glm-5-3-flash", "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json", "dns": "glm-5-3-flash.grade.anchorterminal.com", "repo": "https://huggingface.co/zai-org/GLM-5.3-Flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash" }, { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash-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 MIT licence. Z.ai also sells hosted access as glm-5.3-flash on its own API, which isn't graded here. Running the weights yourself costs the hardware, 328.4 GB for the FP8 files and 642.7 GB for BF16 (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": "2026-11-02" }, "docsUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-26", "graded": true, "anchor": { "graded": true, "score": 65.1, "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": 1350, "categoryRank": 13, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "bestFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "strengths": [ "MIT weights with no use policy, downloadable with no account", "Model code in released llama.cpp b11768, vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.16.1", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard", "1,048,576 tokens of native context and several tool calls per turn in the chat template", "Chat template changed as recently as 15 October 2026, and hosted by Z.ai and six other providers for agents without the hardware" ], "weaknesses": [ "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists", "No safety evaluation, agent-use guidance or security contact", "Thinking is always on, and reasoning_effort only lowers it to low", "The tool-call format lives only in the chat template, with the parser named by the runtimes", "The card leaves out intended use, limitations, context length and knowledge cut-off, and no zai-org reply on discussions since 28 September" ], "agentNotes": [ "Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)", "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs", "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max", "Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block", "Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt", "On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 65.1 } ], "editorialScores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "provenanceScore": 61, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM 0.32.0 or later, per vLLM's recipe (FP8, about 386 GB of GPU memory)\nvllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 \\\n --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"zai-org/GLM-5.3-Flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-5-3-flash" }, "sameCompany": [ "glm-4-5-air", "glm-4-7-flash", "glm-5-3" ], "weights": { "trainedBy": "Z.ai", "params": "320B", "active": "18B", "context": 1048576, "goodFor": "Multimodal agent and coding model: text, images and video in, text out, with tool calling and always-on reasoning.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-5.3-Flash-FP4-Local", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-FP4-Local", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 177.5, "memoryGB": 256, "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. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "GLM-5.3-Flash-NVFP4", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 204.4, "memoryGB": 256, "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. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "GLM-5.3-Flash", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash", "who": "publisher", "precision": "fp8", "sizeGB": 328.3, "memoryGB": 512, "format": "safetensors" }, { "name": "GLM-5.3-Flash-BF16", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 642.7, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47", "runBuild": "GLM-5.3-Flash", "checked": "2026-10-21", "minMemoryGB": 256 }, "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", "url": "https://huggingface.co/zai-org/GLM-5.3", "params": "744B-A40B", "listing": "https://www.anchorterminal.com/tools/glm-5-3" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Z.AI Co., Ltd (named in the licence's copyright line)", "domain": "z.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright (c) 2026 Z.AI Co., Ltd. The registration date of z.ai couldn't be read, since the RDAP service answered 429 on 21 October 2026.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Early messages say only \"update\"; later ones say what changed (a chat template fix on 4 September, evaluation results on 7 September and 12 October, a chat template speed-up on 15 October).", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json", "live": { "slug": "glm-5-3-flash", "securityTxt": { "url": "https://z.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-04T15:54:10.804513976Z" }, "domain": { "domain": "z.ai", "registered": "2017-12-15", "source": "https://rdap.identitydigital.services/rdap/domain/z.ai", "checkedAt": "2026-11-01T14:26:13.734331232Z" }, "pages": [ { "url": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-04T08:27:04.056101778Z", "changedAt": "2026-11-04T08:27:04.056101778Z", "fingerprint": "f27f4107c336" } ], "updatedAt": "2026-11-04T15:54:10.804513976Z" } }, "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.", "b": { "slug": "ripple-65b-preview", "name": "Ripple-65B Preview", "vendor": "Ricursive Intelligence", "vendorUrl": "https://www.ricursive.com/ripple", "kind": "weights", "category": "open-weights", "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.", "url": "https://www.anchorterminal.com/tools/ripple-65b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json", "dns": "ripple-65b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview" }, { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).", "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 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "remote-code", "self-hosted", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 53.3, "grade": "D", "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": 1350, "categoryRank": 78, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.", "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "strengths": [ "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to", "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately", "Trained for long context at 2M-token sequences, with 1M stated", "Base model and a speculative-decoding draft published beside the instruct model", "Safetensors only, with a checksum on every file" ], "weaknesses": [ "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code", "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF", "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run", "Thinking can't be turned off or budgeted", "No safety guidance for agent use, no security contact and no knowledge cut-off" ], "agentNotes": [ "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default", "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags", "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields", "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on", "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.3 } ], "editorialScores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "provenanceScore": 36, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n --model-path ../.. \\\n --speculative-draft-model-path ../../../ripple-dflash2", "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ripple-65b-preview" }, "weights": { "trainedBy": "Ricursive Intelligence", "params": "65B", "context": 1048576, "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.", "runtimes": [], "builds": [ { "name": "Ripple-65B-Preview-NVFP4", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 57.2, "memoryGB": 69, "format": "safetensors", "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day" }, { "name": "Ripple-65B-Preview", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 129.6, "memoryGB": 160, "format": "safetensors", "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs" } ], "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2", "runBuild": "Ripple-65B-Preview", "checked": "2026-11-04", "minMemoryGB": 69 }, "area": "models", "provenance": { "legalEntity": "Ricursive Intelligence, Inc.", "domain": "ricursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.", "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.", "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.", "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.", "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json" }, "edges": [ { "by": 15, "category": "reach", "leads": "glm-5-3-flash", "name": "Reach", "reasons": { "a": "Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.", "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read." }, "scores": { "a": 60, "b": 45 } }, { "by": 9, "category": "upkeep", "leads": "glm-5-3-flash", "name": "Documentation and upkeep", "reasons": { "a": "The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.", "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M." }, "scores": { "a": 58, "b": 49 } }, { "by": 5, "category": "interface", "leads": "ripple-65b-preview", "name": "Agent interface", "reasons": { "a": "A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M." }, "scores": { "a": 70, "b": 75 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai", "b": "Ricursive Intelligence", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-26", "b": "2026-11-04", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.", "question": "Which is better for AI agents, GLM-5.3-Flash or Ripple-65B Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 60 against 45", "Documentation and upkeep, 58 against 49" ], "also": null, "goodFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "slug": "glm-5-3-flash", "watchFor": "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists" }, { "aheadOn": [ "Agent interface, 75 against 70" ], "also": null, "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "slug": "ripple-65b-preview", "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code" } ], "history": null, "job": { 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"https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview.json", "title": "Qwen3-4B-Instruct-2507 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview.json", "title": "Qwen3.5-0.8B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.json", "title": "Qwen3.5-122B-A10B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.json", "title": "Qwen3.5-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview.json", "title": "Qwen3.5-397B-A17B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview.json", "title": "Qwen3.5-4B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json", "title": "Qwen3.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.json", "title": "Qwen3.6-27B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview.json", "title": "Qwen3.6-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview.json", "title": "Qwen3.8-27B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.json", "title": "Qwen3.8-Flash-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview.json", "title": "Qwen3-Coder-Next vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-2B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-32B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json", "title": "Recursive Flint vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json", "title": "Ripple-65B Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json", "title": "Ripple-65B Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json", "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "glm-5-3-flash", "glm-5-3-flash": 60, "key": "reach", "name": "Reach", "ripple-65b-preview": 45, "weight": 25 }, { "by": 0, "edge": "", "glm-5-3-flash": 55, "key": "risk", "name": "Risk", "ripple-65b-preview": 55, "weight": 20 }, { "by": 5, "edge": "ripple-65b-preview", "glm-5-3-flash": 70, "key": "interface", "name": "Agent interface", "ripple-65b-preview": 75, "weight": 10 }, { "by": 9, "edge": "glm-5-3-flash", "glm-5-3-flash": 58, "key": "upkeep", "name": "Documentation and upkeep", "ripple-65b-preview": 49, "weight": 10 } ], "summary": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "glm-5-3-flash": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview", "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.min.md" }, "markdown": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface. Both do open-weight models you run yourself.\n\n- GLM-5.3-Flash: grade B, 65.1/100, rank #18 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json\n- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.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 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-5.3-Flash (B)\n\nGood for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider\n\nAhead on:\n- Reach, 60 against 45\n- Documentation and upkeep, 58 against 49\n\nWatch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists\n\n### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Reach: GLM-5.3-Flash +15\n\n- GLM-5.3-Flash (60): Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.\n- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n### Documentation and upkeep: GLM-5.3-Flash +9\n\n- GLM-5.3-Flash (58): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.\n- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n### Agent interface: Ripple-65B Preview +5\n\n- GLM-5.3-Flash (70): A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.\n- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n## Score by category\n\n| Category | Weight | GLM-5.3-Flash | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 45 | GLM-5.3-Flash +15 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 75 | Ripple-65B Preview +5 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 49 | GLM-5.3-Flash +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.1 · B** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3-Flash | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai | Ricursive Intelligence |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-26 | 2026-11-04 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.\n\n**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\n\n## Before you call either\n\n### GLM-5.3-Flash\n\n1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block\n5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt\n6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai\n\n### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3-Flash or Ripple-65B Preview?\n\nGLM-5.3-Flash scores 65.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3-flash\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare glm-5-3-flash ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with GLM-5.3-Flash or Ripple-65B Preview\n\n- [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md)\n- [Agents-A1-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.md)\n- [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md)\n- [Apertus v1.5 8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.md)\n- [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- [Apodex-1.1-mini vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview.md)\n- [BigBang-v1 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.md)\n- [BigBang-v1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview.md)\n- [Carbon-X vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash.md)\n- [Carbon-X vs Ripple-65B Preview](https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview.md)\n- [Cosmos-Reason2-2B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.md)\n- [Cosmos-Reason2-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.md)\n- [Darwin-36B-Opus vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.md)\n- [Darwin-36B-Opus vs Ripple-65B Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview.md)\n- [DeepSeek-V3.2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.md)\n- [DeepSeek-V3.2 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.md)\n- [DeepSeek V4.1 Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.md)\n- [DeepSeek-V4-Flash-0731 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ripple-65b-preview.md)\n- [DiffusionGemma 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash.md)\n- [DiffusionGemma 26B A4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 12B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 12B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 26B A4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 E2B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E2B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview.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 Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ripple-65b-preview.md)\n- [GLM-4.5-Air vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.md)\n- [GLM-4.5-Air vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ripple-65b-preview.md)\n- [GLM-4.7-Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.md)\n- [GLM-4.7-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ripple-65b-preview.md)\n- [GLM-5.3-Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-120b.md)\n- [GLM-5.3-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b.md)\n- [GLM-5.3-Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.md)\n- [GLM-5.3-Flash vs Hy3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.md)\n- [GLM-5.3-Flash vs Inkling](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling.md)\n- [GLM-5.3-Flash vs Inkling-Small](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.md)\n- [GLM-5.3-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-5.3-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.md)\n- [GLM-5.3-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.md)\n- [GLM-5.3-Flash vs Kronos](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kronos.md)\n- [GLM-5.3-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.md)\n- [GLM-5.3-Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [GLM-5.3-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-o-4-5.md)\n- [GLM-5.3-Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm5-2b.md)\n- [GLM-5.3-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.md)\n- [GLM-5.3-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3-Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m3-1-flash.md)\n- [GLM-5.3-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4.md)\n- [GLM-5.3-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-5.3-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-5.3-Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-5.3-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.md)\n- [GLM-5.3-Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-olmo-3-7b-think.md)\n- [GLM-5.3-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3-Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [GLM-5.3-Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-4b-instruct-2507.md)\n- [GLM-5.3-Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b.md)\n- [GLM-5.3-Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-122b-a10b.md)\n- [GLM-5.3-Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-2b.md)\n- [GLM-5.3-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b.md)\n- [GLM-5.3-Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-4b.md)\n- [GLM-5.3-Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.md)\n- [GLM-5.3-Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b.md)\n- [GLM-5.3-Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b.md)\n- [GLM-5.3-Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b.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-Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next.md)\n- [GLM-5.3-Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct.md)\n- [GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2.md)\n- [GLM-5.3-Flash vs Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.md)\n- [GLM-5.3-Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.md)\n- [GLM-5.3-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2.md)\n- [GLM-5.3 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash.md)\n- [GLM-5.3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-ripple-65b-preview.md)\n- [gpt-oss-120b vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ripple-65b-preview.md)\n- [gpt-oss-20b vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ripple-65b-preview.md)\n- [Granite 4.2 3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.md)\n- [Hy3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/hy3-vs-ripple-65b-preview.md)\n- [Inkling-Small vs Ripple-65B Preview](https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.md)\n- [Inkling vs Ripple-65B Preview](https://www.anchorterminal.com/compare/inkling-vs-ripple-65b-preview.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.md)\n- [Kimi K3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-ripple-65b-preview.md)\n- [Kolibri-1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview.md)\n- [Kronos vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview.md)\n- [Laguna M.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview.md)\n- [Laguna S 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.md)\n- [Laguna XS 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ripple-65b-preview.md)\n- [LBS-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.md)\n- [LFM2.5-2.6B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ripple-65b-preview.md)\n- [Ling-3.0-tiny vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ripple-65b-preview.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ripple-65b-preview.md)\n- [MiniCPM-o 4.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview.md)\n- [MiniCPM5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ripple-65b-preview.md)\n- [MiniMax-M2.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview.md)\n- [MiniMax-M2.7 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.md)\n- [MiniMax-M3.1-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ripple-65b-preview.md)\n- [Mistral Large 4 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mistral-large-4-vs-ripple-65b-preview.md)\n- [Muse Glimmer 30B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ripple-65b-preview.md)\n- [Nanbeige4.3-33B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ripple-65b-preview.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B 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[Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.md)\n- [Ripple-65B Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.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": "2026-11-05", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": 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