# EXAONE 4.5 33B vs Ornith-1.5-9B > EXAONE 4.5 33B and Ornith-1.5-9B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b - Markdown: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b.md (~8,950 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 EXAONE 4.5 33B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach. Both do open-weight models you run yourself. - 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 - Ornith-1.5-9B: grade C, 61/100, rank #51 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### EXAONE 4.5 33B (C) Good for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights Ahead on: - Documentation and upkeep, 50 against 31 Watch for: Research and education only. No commercial use of the model or its output ### Ornith-1.5-9B (C) Good for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required Ahead on: - Reach, 95 against 65 Watch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on ## Why the edge ### Reach: Ornith-1.5-9B +30 - 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. - Ornith-1.5-9B (95): Memory 30, since Ornith's own MLX 4-bit build is 5.0 GB and needs about 6.0 GB with the fifth added, and its Q4_K_M GGUF and Ollama's build fit 16 GB too. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35 architecture, Ollama's library and LM Studio's catalogue for the qwen35 architecture. Builds 15, Ornith publishes GGUF, MLX and NVFP4 at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and only one inference provider (featherless-ai) serves it. 30 + 25 + 15 + 15 + 10 + 0 = 95. ### Documentation and upkeep: EXAONE 4.5 33B +19 - 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. - Ornith-1.5-9B (31): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 15, the newest change to the model's files was Ornith's NVFP4 build on 26 August 2026, 69 days ago, and no card of the model or its builds has changed since. Responsiveness 0, Ornith answered none of the 15 questions and reports. Version policy 0, the fourth safetensors shard and every GGUF file were replaced in place on 23 and 24 August with no note. File health 0, the card links a missing LICENSE and an unpublished Mobile build, and the two chat templates differ. 11 + 5 + 15 + 0 + 0 + 0 = 31. ## Score by category | Category | Weight | EXAONE 4.5 33B | Ornith-1.5-9B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 95 | Ornith-1.5-9B +30 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 85 | 85 | even | | Documentation and upkeep | 10% (10 this run) | 50 | 31 | EXAONE 4.5 33B +19 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | EXAONE 4.5 33B | Ornith-1.5-9B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | LG AI Research | Ornith | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-06 | 2026-08-23 | | Terms last updated | not read yet | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.3/5 (4) | ## Verdicts **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. **Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies. ## Before you call either ### EXAONE 4.5 33B 1. Don't use it, or anything it writes, commercially. The licence covers the output too 2. Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes` 3. Set `enable_thinking` false for a direct answer. Thinking is on by default 4. 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 5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input 6. Its knowledge stops at December 2024 ### Ornith-1.5-9B 1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file 2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed 3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention 4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it 5. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card 6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp 7. Validate JSON output yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, EXAONE 4.5 33B or Ornith-1.5-9B? EXAONE 4.5 33B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "exaone-4-5-33b", "b": "ornith-1-5-9b"}`. 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