Head to head · Open-weight models you run yourself · October 2026 research run
Cola2 vs EXAONE 4.5 33B
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.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Cola2 F
Best for Researchers working on latent or diffusion language models who want a checkpoint to study
Ahead on
- Documentation and upkeep, 58 against 50
Watch for
No licence stated for the weights
Best for Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights
Ahead on
- Reach, 65 against 25
- Risk, 50 against 40
- Agent interface, 85 against 20
Watch for
Research and education only. No commercial use of the model or its output
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Agent interface EXAONE 4.5 33B +65
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.
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.
Reach EXAONE 4.5 33B +40
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.
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.
Risk EXAONE 4.5 33B +10
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.
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.
Documentation and upkeep Cola2 +8
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.
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.
Score by category
| Category | Weight this run | Cola2 | EXAONE 4.5 33B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 25 | 65 | EXAONE 4.5 33B +40 |
| Risk | 20%20 | 40 | 50 | EXAONE 4.5 33B +10 |
| Agent interface | 10%10 | 20 | 85 | EXAONE 4.5 33B +65 |
| Documentation and upkeep | 10%10 | 58 | 50 | Cola2 +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | F 33.9/100 | C 61/100 |
Facts side by side
| Fact | Cola2 | EXAONE 4.5 33B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | ByteDance (Seed) | LG AI Research |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | None stated for the weights (the code is Apache 2.0) | EXAONE AI Model License Agreement 1.2 - NC (research and education only) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-07-06 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
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.
Before you call either
Cola2
- 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
EXAONE 4.5 33B
- 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_thinkingfalse 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
Questions
Which is better for AI agents, Cola2 or EXAONE 4.5 33B?
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.
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Machine-readable
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/api/v1/tools/cola2.json·/api/v1/tools/exaone-4-5-33b.json - From a terminal
anchor compare cola2 exaone-4-5-33b(the CLI) - Over MCP
compare_tools {"a": "cola2", "b": "exaone-4-5-33b"}at/mcp, no key