Head to head · Open-weight models you run yourself · October 2026 research run
EXAONE 4.5 33B vs Qwen3-4B-Instruct-2507
EXAONE 4.5 33B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach and risk. 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
Best for Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights
Ahead on
- Agent interface, 85 against 75
- Documentation and upkeep, 50 against 26
Watch for
Research and education only. No commercial use of the model or its output
Best for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.
Ahead on
- Reach, 90 against 65
- Risk, 55 against 50
Watch for
No safety results, prompt-injection guidance or security contact
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.
Reach Qwen3-4B-Instruct-2507 +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.
Qwen3-4B-Instruct-2507 90
Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3 architecture, Ollama's released build pins a llama.cpp that has it, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 0, on 4 November 2026 the Hub's provider mapping showed only Featherless AI with status error, Nscale was gone and Alibaba's Model Studio doesn't list it. 30 + 25 + 10 + 15 + 10 + 0 = 90.
Documentation and upkeep EXAONE 4.5 33B +24
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.
Qwen3-4B-Instruct-2507 26
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26.
Agent interface EXAONE 4.5 33B +10
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.
Qwen3-4B-Instruct-2507 75
Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.
Risk Qwen3-4B-Instruct-2507 +5
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.
Qwen3-4B-Instruct-2507 55
File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Score by category
| Category | Weight this run | EXAONE 4.5 33B | Qwen3-4B-Instruct-2507 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 90 | Qwen3-4B-Instruct-2507 +25 |
| Risk | 20%20 | 50 | 55 | Qwen3-4B-Instruct-2507 +5 |
| Agent interface | 10%10 | 85 | 75 | EXAONE 4.5 33B +10 |
| Documentation and upkeep | 10%10 | 50 | 26 | EXAONE 4.5 33B +24 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.5/100 |
Facts side by side
| Fact | EXAONE 4.5 33B | Qwen3-4B-Instruct-2507 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | LG AI Research | Alibaba (Qwen team) |
| 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 | no |
| Last release | 2026-07-06 | 2025-09-17 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
EXAONE 4.5 33B
No score change since it was listed.
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.
Qwen3-4B-Instruct-2507
A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published.
Before you call either
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
Qwen3-4B-Instruct-2507
- Quickest start is
ollama run qwen3:4b-instruct, 2.5 GB. Plainqwen3:4bis the Thinking-2507 edition, not this one - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes - Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
- Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
- Drop the context to 32,768 tokens if memory runs out, as the card suggests
- Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, EXAONE 4.5 33B or Qwen3-4B-Instruct-2507?
EXAONE 4.5 33B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach and risk.
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- Qwen3-4B-Instruct-2507 vs Step 3.7 Flash
- Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B
- Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)
- Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2
- Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B
- Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B
- Qwen3-4B-Instruct-2507 vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/exaone-4-5-33b-vs-qwen3-4b-instruct-2507.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/exaone-4-5-33b.json·/api/v1/tools/qwen3-4b-instruct-2507.json - From a terminal
anchor compare exaone-4-5-33b qwen3-4b-instruct-2507(the CLI) - Over MCP
compare_tools {"a": "exaone-4-5-33b", "b": "qwen3-4b-instruct-2507"}at/mcp, no key