DeepSeek-V4-Flash-0731 vs K-EXAONE-236B-A23B
Comparing DeepSeek-V4-Flash-0731 and K-EXAONE-236B-A23B across benchmarks, pricing, and capabilities.
DeepSeek · LG AI Research · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and K-EXAONE-236B-A23B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V4-Flash-0731 is roughly 6.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- cost matters — it's about 6.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
- you need open weights you can self-host or fine-tune
Choose K-EXAONE-236B-A23B
- you want predictable pricing at $0.60/M input and $1.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and K-EXAONE-236B-A23Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 6.7x cheaper than K-EXAONE-236B-A23B ($0.60/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 5.6x cheaper than K-EXAONE-236B-A23B ($1.00/1M tokens).
In conclusion, K-EXAONE-236B-A23B is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 68.0B more parameters than K-EXAONE-236B-A23B, making it 28.8% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 65,536 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while K-EXAONE-236B-A23B 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-V4-Flash-0731 was released on 2026-07-31, while K-EXAONE-236B-A23B was released on 2025-12-31.
DeepSeek-V4-Flash-0731 is 7 months newer than K-EXAONE-236B-A23B.
Jul 31, 2026
3 weeks ago
7mo newerDec 31, 2025
7 months ago
Knowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while DeepSeek-V4-Flash-0731'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 DeepSeek-V4-Flash-0731's cutoff date.
—
Oct 2025
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. K-EXAONE-236B-A23B is available from FriendliAI.
DeepSeek-V4-Flash-0731
K-EXAONE-236B-A23B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and K-EXAONE-236B-A23B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs K-EXAONE-236B-A23B.