K-EXAONE-236B-A23B vs Qwen3-235B-A22B-Thinking-2507
K-EXAONE-236B-A23B shows notably better performance in the majority of benchmarks. K-EXAONE-236B-A23B is 1.4x cheaper per token.
LG AI Research · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
K-EXAONE-236B-A23B outperforms in 2 benchmarks (AIME 2025, LiveCodeBench v6), while Qwen3-235B-A22B-Thinking-2507 is better at 1 benchmark (MMLU-Pro). K-EXAONE-236B-A23B shows notably better performance in the majority of benchmarks.
On price, K-EXAONE-236B-A23B is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3-235B-A22B-Thinking-2507 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose K-EXAONE-236B-A23B
- you want the strongest raw capability — it leads on 2 of 3 shared benchmarks
- cost matters — it's about 1.4x cheaper per token
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3-235B-A22B-Thinking-2507
- you process long inputs — it offers a 262,144 token context window
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
K-EXAONE-236B-A23B outperforms in 2 benchmarks (AIME 2025, LiveCodeBench v6), while Qwen3-235B-A22B-Thinking-2507 is better at 1 benchmark (MMLU-Pro).
K-EXAONE-236B-A23B shows notably better performance in the majority of benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, K-EXAONE-236B-A23B ($0.60/1M tokens) is 2.0x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 3.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than K-EXAONE-236B-A23B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
K-EXAONE-236B-A23B has 1.0B more parameters than Qwen3-235B-A22B-Thinking-2507, making it 0.4% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
License
Usage and distribution terms
K-EXAONE-236B-A23B is licensed under a proprietary license, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
K-EXAONE-236B-A23B was released on 2025-12-31, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
K-EXAONE-236B-A23B is 5 months newer than Qwen3-235B-A22B-Thinking-2507.
Dec 31, 2025
7 months ago
5mo newerJul 25, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while Qwen3-235B-A22B-Thinking-2507's cutoff date is not specified.
We can confirm K-EXAONE-236B-A23B's training data extends to 2025-10-01, but cannot make a direct comparison without Qwen3-235B-A22B-Thinking-2507's cutoff date.
Oct 2025
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Provider Availability
K-EXAONE-236B-A23B is available from FriendliAI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
K-EXAONE-236B-A23B
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against K-EXAONE-236B-A23B and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about K-EXAONE-236B-A23B vs Qwen3-235B-A22B-Thinking-2507.