{ "data": { "a": { "slug": "cola2", "name": "Cola2", "vendor": "ByteDance (Seed)", "vendorUrl": "https://github.com/ByteDance-Seed", "kind": "weights", "category": "open-weights", "summary": "Cola2 is a research checkpoint from ByteDance Seed, a base language model of 8.8B parameters that generates text through continuous latents with a diffusion transformer and a token decoder. Not a chat model, and the weights carry no stated licence.", "url": "https://www.anchorterminal.com/tools/cola2", "markdownUrl": "https://www.anchorterminal.com/tools/cola2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cola2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cola2.json", "dns": "cola2.grade.anchorterminal.com", "repo": "https://huggingface.co/ByteDance-Seed/Cola2", "license": "None stated for the weights (the code is Apache 2.0)", "transports": [], "packages": [ { "registry": "huggingface", "name": "ByteDance-Seed/Cola2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download, but nothing says what you may do with the weights. The card says the code is Apache 2.0 and that the weights remain subject to their respective terms, without naming them. Running it yourself costs the hardware: 35.1 GB of F32 files, loaded as BF16 in the card's example (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/ByteDance-Seed/Cola2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "base-model", "research", "diffusion", "continuous-latent", "no-licence", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 33.9, "grade": "F", "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": 1518, "categoryRank": 113, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 20, "reach": 25, "risk": 40, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.", "bestFor": "Researchers working on latent or diffusion language models who want a checkpoint to study", "strengths": [ "A documented new architecture with a paper and a reproduction protocol", "Evaluation data and checksums ship with the weights", "A security policy that points to ByteDance's security centre", "The model card says plainly what it isn't for" ], "weaknesses": [ "No licence stated for the weights", "A base model: no chat, no tools, 32 new tokens by default", "Loads only with the repository's own Python, on Transformers 4.x", "35 GB of F32 files and no quantised build", "Below Llama-3-8B on the card's own eight-task table", "No stated context length or cut-off" ], "agentNotes": [ "Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions", "Don't use it commercially on the strength of this listing. The weights have no stated licence", "If you study it, read modeling_cola2.py first: trust_remote_code runs it", "Pin Transformers below 5, as the card requires", "Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding", "Check the download with `sha256sum -c SHA256SUMS`" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "F", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 33.9 } ], "editorialScores": { "interface": 20, "reach": 25, "risk": 40, "upkeep": 58 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"torch\u003e=2.2\" \"transformers\u003e=4.51,\u003c5\" \"safetensors\u003e=0.4\" accelerate\n# then AutoModel.from_pretrained(\"ByteDance-Seed/Cola2\", trust_remote_code=True, torch_dtype=torch.bfloat16)\n# trust_remote_code runs the three Python files in the repository. Read them first", "http": "# No server. The model runs in process through Transformers, and the GitHub repository has a\n# command-line chat script (scripts/run_chat.sh) and a benchmark runner" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/cola2" }, "weights": { "trainedBy": "ByteDance Seed", "builtOn": "The decoder and encoder embedding are initialised from Qwen3-4B-Base, per the GitHub model card", "params": "8.8B", "goodFor": "Research on continuous-latent text generation: short completions, cloze and multiple-choice experiments, and reproducing the paper's numbers.", "runtimes": [], "builds": [ { "name": "Cola2 (F32 safetensors)", "url": "https://huggingface.co/ByteDance-Seed/Cola2/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 35.07, "memoryGB": 48, "format": "safetensors", "note": "Eight shards in 32-bit floats. The card's example loads them as BF16" } ], "run": "python -c \"from transformers import AutoModel; AutoModel.from_pretrained('ByteDance-Seed/Cola2', trust_remote_code=True)\"", "runBuild": "Cola2 (F32 safetensors)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "ByteDance (Seed)", "family": "Cola", "url": "https://www.anchorterminal.com/open-weights/#bytedance-seed", "others": [ { "name": "Cola-DLM", "url": "https://huggingface.co/ByteDance-Seed/Cola-DLM" } ] }, "area": "models", "provenance": { "legalEntity": "ByteDance, named in the GitHub repository's security policy. The legal entity wasn't looked up", "domain": "bytedance.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ByteDance-Seed/Cola2/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "The files are in the ByteDance-Seed organisation on Hugging Face and the code is in the ByteDance-Seed organisation on GitHub; each links the other.", "No licence is stated for the weights, so there are no terms to read.", "The domain's registration date and security.txt weren't looked up. The GitHub repository's SECURITY.md points to ByteDance's security centre.", "The changelog is the commit history, six commits on 6 November 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cola2.json" }, "answer": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.", "b": { "slug": "exaone-4-5-33b", "name": "EXAONE 4.5 33B", "vendor": "LG AI Research", "vendorUrl": "https://www.lgresearch.ai", "kind": "weights", "category": "open-weights", "summary": "EXAONE 4.5 33B is LG AI Research's open-weight vision-language model, 34B parameters, with tool calling, switchable reasoning and a 256K context, strong on documents and Korean. Its licence allows research and education only, with LG's own GGUF from 18 GB.", "url": "https://www.anchorterminal.com/tools/exaone-4-5-33b", "markdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json", "dns": "exaone-4-5-33b.grade.anchorterminal.com", "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B", "license": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B" }, { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B-GGUF" }, { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B-AWQ" }, { "registry": "huggingface", "name": "LGAI-EXAONE/EXAONE-4.5-33B-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account or token. None of the four repositories is gated, though the licence says downloading is agreeing to it (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download for research and education. The EXAONE AI Model License Agreement 1.2 - NC prohibits commercial use of the model, its derivatives and its output without a separate agreement with LG. Running it yourself takes 20.6 GB at IQ4_XS with its projector, up to 68.7 GB in BF16 (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/LG-AI-EXAONE/EXAONE-4.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "documents", "korean", "reasoning", "tool-calling", "long-context", "non-commercial", "safetensors", "gguf", "awq", "fp8", "self-hosted" ], "lastRelease": "2026-07-06", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 51, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.", "bestFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights", "strengths": [ "A complete card, down to a knowledge cut-off", "GGUF, AWQ and FP8 builds from LG", "Released vLLM, Transformers and llama.cpp run it with no repository code", "Tool calling and reasoning control documented with parsers", "A 256K context, served on one large GPU" ], "weaknesses": [ "Research and education only. No commercial use of the model or its output", "LG can amend the licence at any time, and it's revocable", "No building models that compete with LG's", "No reply from LG on the repository's discussions", "Not changed since the summer", "No safety results or training data sources on the card" ], "agentNotes": [ "Don't use it, or anything it writes, commercially. The licence covers the output too", "Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`", "Set `enable_thinking` false for a direct answer. Thinking is on by default", "Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean", "For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input", "Its knowledge stops at December 2024" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 50 }, "provenanceScore": 49, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"vllm\u003e=0.33.0\" \"transformers\u003e=5.8.0\"\nvllm serve LGAI-EXAONE/EXAONE-4.5-33B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. The card recommends temperature 1.0,\n# top_p 0.95 and presence_penalty 1.5, and enable_thinking false for a direct answer" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/exaone-4-5-33b" }, "weights": { "trainedBy": "LG AI Research", "params": "34B", "context": 262144, "goodFor": "Research on document understanding, Korean and agent work with images, where non-commercial terms are acceptable.", "runtimes": [ "llama.cpp", "vLLM", "Transformers" ], "builds": [ { "name": "EXAONE-4.5-33B-GGUF IQ4_XS", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 20.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "18.03 GB with the 2.57 GB F16 projector" }, { "name": "EXAONE-4.5-33B-GGUF Q4_K_M", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.62, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "20.05 GB with the 2.57 GB F16 projector" }, { "name": "EXAONE-4.5-33B-AWQ", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-AWQ", "who": "publisher", "precision": "awq", "sizeGB": 22.75, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "LG's 4-bit AWQ build" }, { "name": "EXAONE-4.5-33B-GGUF Q8_0", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 37.71, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "35.14 GB with the 2.57 GB F16 projector" }, { "name": "EXAONE-4.5-33B-FP8", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 41.31, "memoryGB": 64, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "LG's FP8 build" }, { "name": "EXAONE-4.5-33B (BF16 safetensors)", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B", "who": "publisher", "precision": "bf16", "sizeGB": 68.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Two shards" } ], "run": "llama-server -hf LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q4_K_M --jinja", "runBuild": "EXAONE-4.5-33B-GGUF Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 32 }, "family": { "lab": "LG AI Research", "family": "EXAONE 4", "url": "https://www.anchorterminal.com/open-weights/#lg-ai-research", "others": [ { "name": "EXAONE-4.0.1-32B", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B" }, { "name": "EXAONE-4.0-1.2B", "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B" } ] }, "area": "models", "provenance": { "legalEntity": "LG Management Development Institute Co., Ltd., the licensor named in the model's licence. The card names LG AI Research", "domain": "lgresearch.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card and the Hub organisation name LG AI Research, and the licence names LG Management Development Institute Co., Ltd. as licensor. How the two relate wasn't read first-hand.", "www.lgresearch.ai's robots.txt allows reading. Its home page gave little text and its about page a 404, so no people or ownership facts were taken from it.", "The domain's registration date wasn't looked up. www.lgresearch.ai has no security.txt (404 on 10 November 2026).", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 8 commits from 9 April to 6 July 2026." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.json" }, "edges": [ { "by": 65, "category": "interface", "leads": "exaone-4-5-33b", "name": "Agent interface", "reasons": { "a": "Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.", "b": "Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 20, "b": 85 } }, { "by": 40, "category": "reach", "leads": "exaone-4-5-33b", "name": "Reach", "reasons": { "a": "Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.", "b": "Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65." }, "scores": { "a": 25, "b": 65 } }, { "by": 10, "category": "risk", "leads": "exaone-4-5-33b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.", "b": "File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50." }, "scores": { "a": 40, "b": 50 } }, { "by": 8, "category": "upkeep", "leads": "cola2", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.", "b": "Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50." }, "scores": { "a": 58, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "ByteDance (Seed)", "b": "LG AI Research", "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": "None stated for the weights (the code is Apache 2.0)", "b": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-07-06", "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" } ], "faq": [ { "answer": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.", "question": "Which is better for AI agents, Cola2 or EXAONE 4.5 33B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 58 against 50" ], "also": null, "goodFor": "Researchers working on latent or diffusion language models who want a checkpoint to study", "slug": "cola2", "watchFor": "No licence stated for the weights" }, { "aheadOn": [ "Reach, 65 against 25", "Risk, 50 against 40", "Agent interface, 85 against 20" ], "also": null, "goodFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights", "slug": "exaone-4-5-33b", "watchFor": "Research and education only. 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33B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-xs-2-1.json", "title": "EXAONE 4.5 33B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b.json", "title": "EXAONE 4.5 33B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b.json", "title": "EXAONE 4.5 33B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b.json", "title": "EXAONE 4.5 33B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-audio-2b.json", "title": "EXAONE 4.5 33B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-omni-2b.json", "title": "EXAONE 4.5 33B vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b.json", "title": "EXAONE 4.5 33B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-0-tiny.json", "title": "EXAONE 4.5 33B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-1-flash-1019.json", "title": "EXAONE 4.5 33B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5.json", "title": "EXAONE 4.5 33B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "EXAONE 4.5 33B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.json", "title": "EXAONE 4.5 33B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5.json", "title": "EXAONE 4.5 33B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b.json", "title": "EXAONE 4.5 33B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b.json", "title": "EXAONE 4.5 33B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm5-2b.json", "title": "EXAONE 4.5 33B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-5.json", "title": "EXAONE 4.5 33B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7.json", "title": "EXAONE 4.5 33B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m3-1-flash.json", "title": "EXAONE 4.5 33B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mistral-large-4.json", "title": "EXAONE 4.5 33B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-muse-glimmer-30b.json", "title": "EXAONE 4.5 33B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-2-3b.json", "title": "EXAONE 4.5 33B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-3-33b.json", "title": "EXAONE 4.5 33B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "EXAONE 4.5 33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "EXAONE 4.5 33B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-30b-a3b.json", "title": "EXAONE 4.5 33B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-4b.json", "title": "EXAONE 4.5 33B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "EXAONE 4.5 33B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-super-120b-a12b.json", "title": "EXAONE 4.5 33B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-ultra-550b-a55b.json", "title": "EXAONE 4.5 33B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0.json", "title": "EXAONE 4.5 33B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-occamy-1-0.json", "title": "EXAONE 4.5 33B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think.json", "title": "EXAONE 4.5 33B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b.json", "title": "EXAONE 4.5 33B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-35b-a3b.json", "title": "EXAONE 4.5 33B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-397b.json", "title": "EXAONE 4.5 33B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b.json", "title": "EXAONE 4.5 33B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.json", "title": "EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen-agentworld-35b-a3b.json", "title": "EXAONE 4.5 33B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "EXAONE 4.5 33B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-4b-instruct-2507.json", "title": "EXAONE 4.5 33B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-0-8b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-122b-a10b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-2b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-397b-a17b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-4b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-27b.json", "title": "EXAONE 4.5 33B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-35b-a3b.json", "title": "EXAONE 4.5 33B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-27b.json", "title": "EXAONE 4.5 33B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.json", "title": "EXAONE 4.5 33B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-next.json", "title": "EXAONE 4.5 33B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-2b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-32b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-4b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-8b-instruct.json", "title": "EXAONE 4.5 33B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2.json", "title": "EXAONE 4.5 33B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-recursive-flint.json", "title": "EXAONE 4.5 33B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ripple-65b-preview.json", "title": "EXAONE 4.5 33B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview.json", "title": "EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-smollm3-3b.json", "title": "EXAONE 4.5 33B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.json", "title": "EXAONE 4.5 33B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash.json", "title": "EXAONE 4.5 33B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.json", "title": "EXAONE 4.5 33B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "EXAONE 4.5 33B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-thinkingcap-qwen3-8-27b.json", "title": "EXAONE 4.5 33B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-unlimited-ocr-2.json", "title": "EXAONE 4.5 33B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b.json", "title": "EXAONE 4.5 33B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.json", "title": "EXAONE 4.5 33B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b.json", "title": "EXAONE 4.5 33B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "cola2": 25, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 65, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 10, "cola2": 40, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 50, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 65, "cola2": 20, "edge": "exaone-4-5-33b", "exaone-4-5-33b": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 8, "cola2": 58, "edge": "cola2", "exaone-4-5-33b": 50, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "cola2": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.", "exaone-4-5-33b": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b", "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.min.md" }, "markdown": "EXAONE 4.5 33B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json\n- EXAONE 4.5 33B: grade C, 61/100, rank #61 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/exaone-4-5-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Cola2 (F)\n\nGood for: Researchers working on latent or diffusion language models who want a checkpoint to study\n\nAhead on:\n- Documentation and upkeep, 58 against 50\n\nWatch for: No licence stated for the weights\n\n### EXAONE 4.5 33B (C)\n\nGood for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights\n\nAhead on:\n- Reach, 65 against 25\n- Risk, 50 against 40\n- Agent interface, 85 against 20\n\nWatch for: Research and education only. No commercial use of the model or its output\n\n\n## Why the edge\n\n### Agent interface: EXAONE 4.5 33B +65\n\n- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.\n- EXAONE 4.5 33B (85): Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Reach: EXAONE 4.5 33B +40\n\n- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.\n- EXAONE 4.5 33B (65): Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.\n\n### Risk: EXAONE 4.5 33B +10\n\n- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.\n- EXAONE 4.5 33B (50): File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.\n\n### Documentation and upkeep: Cola2 +8\n\n- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.\n- EXAONE 4.5 33B (50): Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.\n\n## Score by category\n\n| Category | Weight | Cola2 | EXAONE 4.5 33B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 65 | EXAONE 4.5 33B +40 |\n| Risk | 20% (20 this run) | 40 | 50 | EXAONE 4.5 33B +10 |\n| Agent interface | 10% (10 this run) | 20 | 85 | EXAONE 4.5 33B +65 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 50 | Cola2 +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Cola2 | EXAONE 4.5 33B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | LG AI Research |\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 | None stated for the weights (the code is Apache 2.0) | EXAONE AI Model License Agreement 1.2 - NC (research and education only) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-07-06 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.\n\n**EXAONE 4.5 33B.** A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.\n\n## Before you call either\n\n### Cola2\n\n1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions\n2. Don't use it commercially on the strength of this listing. The weights have no stated licence\n3. If you study it, read modeling_cola2.py first: trust_remote_code runs it\n4. Pin Transformers below 5, as the card requires\n5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding\n6. Check the download with `sha256sum -c SHA256SUMS`\n\n### EXAONE 4.5 33B\n\n1. Don't use it, or anything it writes, commercially. The licence covers the output too\n2. Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`\n3. Set `enable_thinking` false for a direct answer. Thinking is on by default\n4. Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean\n5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input\n6. Its knowledge stops at December 2024\n\n## Questions\n\n### Which is better for AI agents, Cola2 or EXAONE 4.5 33B?\n\nEXAONE 4.5 33B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"exaone-4-5-33b\"}`. From a terminal: `anchor compare cola2 exaone-4-5-33b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cola2.json and https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json\n\n## Other comparisons with Cola2 or EXAONE 4.5 33B\n\n- [Agents-A1-4B vs Cola2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.md)\n- [Agents-A1-4B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.md)\n- [Apertus v1.5 8B vs Cola2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.md)\n- [Apertus v1.5 8B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.md)\n- [Apodex-1.1-mini vs Cola2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.md)\n- [Apodex-1.1-mini vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.md)\n- [Arete-27B vs Cola2](https://www.anchorterminal.com/compare/arete-27b-vs-cola2.md)\n- [Arete-27B vs EXAONE 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