DeepSeek-V3.2-Exp vs EXAONE 4.5 33B
DeepSeek-V3.2-Exp and EXAONE 4.5 33B are closely matched at 28.2 and 26.0 on the LLM Stats Score.
DeepSeek · LG AI Research · Updated for 2026
Which is better?
DeepSeek-V3.2-Exp and EXAONE 4.5 33B are closely matched on the overall LLM Stats Score at 28.2 and 26.0.
In the 3 individual benchmarks reported for both models, EXAONE 4.5 33B wins 2; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2-Exp
- you need open weights you can self-host or fine-tune
Choose EXAONE 4.5 33B
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- you want the most recent training data — it shipped Apr 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DeepSeek-V3.2-Exp · 15 for EXAONE 4.5 33B
DeepSeek-V3.2-Exp outperforms in 1 benchmarks (MMLU-Pro), while EXAONE 4.5 33B is better at 2 benchmarks (AIME 2025, GPQA).
EXAONE 4.5 33B shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V3.2-Exp has 652.0B more parameters than EXAONE 4.5 33B, making it 1975.8% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2-Exp specifies input context (163,840 tokens). Only DeepSeek-V3.2-Exp specifies output context (65,536 tokens).
Input capabilities
Documented input modalities across available providers
EXAONE 4.5 33B supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.
EXAONE 4.5 33B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2-Exp
EXAONE 4.5 33B
License
Usage and distribution terms
DeepSeek-V3.2-Exp is licensed under MIT, while EXAONE 4.5 33B uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3.2-Exp was released on 2025-09-29, while EXAONE 4.5 33B was released on 2026-04-09.
EXAONE 4.5 33B is 6 months newer than DeepSeek-V3.2-Exp.
Sep 29, 2025
11 months ago
Apr 9, 2026
5 months ago
6mo newerKnowledge Cutoff
When training data ends
EXAONE 4.5 33B has a documented knowledge cutoff of 2024-12-01, while DeepSeek-V3.2-Exp's cutoff date is not specified.
We can confirm EXAONE 4.5 33B's training data extends to 2024-12-01, but cannot make a direct comparison without DeepSeek-V3.2-Exp's cutoff date.
—
Dec 2024
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and EXAONE 4.5 33B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Exp vs EXAONE 4.5 33B.