K-EXAONE-236B-A23B vs Mistral Large 4
Mistral Large 4 leads the LLM Stats Score 46.2 to 26.2. K-EXAONE-236B-A23B is 1.5x cheaper per token.
LG AI Research · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to 26.2, ranking #34 overall.
On price, K-EXAONE-236B-A23B is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 4 also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose K-EXAONE-236B-A23B
- cost matters — it's about 1.5x cheaper per token
Choose Mistral Large 4
- overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
6 reported for K-EXAONE-236B-A23B · 18 for Mistral Large 4
K-EXAONE-236B-A23B and Mistral Large 4don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, K-EXAONE-236B-A23B ($0.60/1M tokens) is 1.1x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 2.1x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than K-EXAONE-236B-A23B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 814.0B more parameters than K-EXAONE-236B-A23B, making it 344.9% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. Only K-EXAONE-236B-A23B specifies output context (32,768 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas K-EXAONE-236B-A23B does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
K-EXAONE-236B-A23B
Mistral Large 4
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 Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 9 months newer than K-EXAONE-236B-A23B.
Dec 31, 2025
9 months ago
Oct 6, 2026
2 days ago
9mo newerKnowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while Mistral Large 4'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 Mistral Large 4's cutoff date.
Oct 2025
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Provider Availability
K-EXAONE-236B-A23B is available from FriendliAI. Mistral Large 4 is available from Mistral AI.
K-EXAONE-236B-A23B
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against K-EXAONE-236B-A23B and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about K-EXAONE-236B-A23B vs Mistral Large 4.