Model Comparison
K-EXAONE-236B-A23B vs o4-miniWhich is better in 2026?
K-EXAONE-236B-A23B significantly outperforms across most benchmarks. K-EXAONE-236B-A23B is 2.8x cheaper per token.
Verdict: K-EXAONE-236B-A23B vs o4-mini — which is better?
K-EXAONE-236B-A23B (by LG AI Research) and o4-mini (by OpenAI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
K-EXAONE-236B-A23B outperforms in 1 benchmarks (AIME 2025), while o4-mini is better at 0 benchmarks. K-EXAONE-236B-A23B significantly outperforms across most benchmarks.
On price, K-EXAONE-236B-A23B is roughly 2.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o4-mini also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose K-EXAONE-236B-A23B if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 2.8x cheaper per token
- you want the most recent training data — it shipped Dec 2025
Choose o4-mini if…
- you process long inputs — it offers a 200,000 token context window
Performance Benchmarks
Comparative analysis across standard metrics
K-EXAONE-236B-A23B outperforms in 1 benchmarks (AIME 2025), while o4-mini is better at 0 benchmarks.
K-EXAONE-236B-A23B significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, K-EXAONE-236B-A23B ($0.60/1M tokens) is 1.8x cheaper than o4-mini ($1.10/1M tokens).
For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 4.4x cheaper than o4-mini ($4.40/1M tokens).
In conclusion, o4-mini is more expensive than K-EXAONE-236B-A23B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
o4-mini accepts 200,000 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. o4-mini can generate longer responses up to 100,000 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
Input Capabilities
Supported data types and modalities
o4-mini supports multimodal inputs, whereas K-EXAONE-236B-A23B does not.
o4-mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
K-EXAONE-236B-A23B
o4-mini
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
K-EXAONE-236B-A23B was released on 2025-12-31, while o4-mini was released on 2025-04-16.
K-EXAONE-236B-A23B is 9 months newer than o4-mini.
Dec 31, 2025
6 months ago
8mo newerApr 16, 2025
1.3 years ago
Knowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a knowledge cutoff of 2025-10-01, while o4-mini has a cutoff of 2024-05-31.
K-EXAONE-236B-A23B has more recent training data (up to 2025-10-01), making it potentially better informed about events through that date compared to o4-mini (2024-05-31).
Oct 2025
1.4 yr newerMay 2024
Provider Availability
K-EXAONE-236B-A23B is available from FriendliAI. o4-mini is available from OpenAI.
K-EXAONE-236B-A23B
o4-mini
Outputs Comparison
Key Takeaways
K-EXAONE-236B-A23B
View detailsLG AI Research
o4-mini
View detailsOpenAI
K-EXAONE-236B-A23Bo4-miniDetailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against K-EXAONE-236B-A23B and o4-mini side-by-side, then vote on the output you prefer.
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FAQ
Common questions about K-EXAONE-236B-A23B vs o4-mini.