# EXAONE 4.5 33B vs Gemma 4 12B IT > Gemma 4 12B IT scores 72.3 (BB) to EXAONE 4.5 33B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it - Markdown: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it.md (~9,400 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 Gemma 4 12B IT scores 72.3 (BB) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in 3 of 4 scored categories. 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 - Gemma 4 12B IT: grade BB, 72.3/100, rank #4 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.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 Watch for: Research and education only. No commercial use of the model or its output ### Gemma 4 12B IT (BB) Good for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context Ahead on: - Reach, 95 against 65 - Risk, 70 against 50 - Documentation and upkeep, 85 against 50 Also in its favour: - Agent-ready, a grade of BB or better Watch for: No safety evaluation figures published ## Why the edge ### Documentation and upkeep: Gemma 4 12B IT +35 - 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. - Gemma 4 12B IT (85): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 210 glitch tokens in tokenizer.json on 30 October 2026, two days before 1 November. Responsiveness 20, Google replied to 18 of 34 questions and reports, the last on 2026-07-31, re-counted on 1 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply. ### Reach: Gemma 4 12B IT +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. - Gemma 4 12B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 7.15 GB, which with a fifth on top is 8.6 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six, all for the separate gemma4_unified architecture. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one. ### Risk: Gemma 4 12B IT +20 - 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. - Gemma 4 12B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found. ## Score by category | Category | Weight | EXAONE 4.5 33B | Gemma 4 12B IT | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 95 | Gemma 4 12B IT +30 | | Risk | 20% (20 this run) | 50 | 70 | Gemma 4 12B IT +20 | | Agent interface | 10% (10 this run) | 85 | 85 | even | | Documentation and upkeep | 10% (10 this run) | 50 | 85 | Gemma 4 12B IT +35 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **72.3 · BB** | | ## Facts side by side | Fact | EXAONE 4.5 33B | Gemma 4 12B IT | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | LG AI Research | Google DeepMind | | 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) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | yes | | Last release | 2026-07-06 | 2026-07-20 | | Terms last updated | not read yet | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3.8/5 (4) | ## How the scores have moved ### EXAONE 4.5 33B No score change since it was listed. ### Gemma 4 12B IT - +1.0 on 1 November 2026: product ## 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. **Gemma 4 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output. ## 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 ### Gemma 4 12B IT 1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio 2. With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K 3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there 4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns 5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them 6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama ## Questions ### Which is better for AI agents, EXAONE 4.5 33B or Gemma 4 12B IT? Gemma 4 12B IT scores 72.3 (BB) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-12b-it.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "exaone-4-5-33b", "b": "gemma-4-12b-it"}`. 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