K-EXAONE-236B-A23B vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads the LLM Stats Score 49.8 to 26.2. MiMo-V2.6-Pro is 1.3x cheaper per token.
LG AI Research · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 49.8 to 26.2, ranking #19 overall.
On price, MiMo-V2.6-Pro is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Pro 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose K-EXAONE-236B-A23B
- you want predictable pricing at $0.60/M input and $1.00/M output
Choose MiMo-V2.6-Pro
- overall performance matters — it scores 49.8 and ranks #19 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- cost matters — it's about 1.3x 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 Sep 2026
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
6 reported for K-EXAONE-236B-A23B · 18 for MiMo-V2.6-Pro
K-EXAONE-236B-A23B and MiMo-V2.6-Prodon'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 1.4x more expensive than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 1.1x more expensive than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, K-EXAONE-236B-A23B is more expensive than MiMo-V2.6-Pro.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 784.0B more parameters than K-EXAONE-236B-A23B, making it 332.2% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 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
MiMo-V2.6-Pro supports multimodal inputs, whereas K-EXAONE-236B-A23B does not.
MiMo-V2.6-Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
K-EXAONE-236B-A23B
MiMo-V2.6-Pro
License
Usage and distribution terms
K-EXAONE-236B-A23B is licensed under a proprietary license, while MiMo-V2.6-Pro uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
K-EXAONE-236B-A23B was released on 2025-12-31, while MiMo-V2.6-Pro was released on 2026-09-22.
MiMo-V2.6-Pro is 9 months newer than K-EXAONE-236B-A23B.
Dec 31, 2025
8 months ago
Sep 22, 2026
0 days ago
8mo newerKnowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while MiMo-V2.6-Pro'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 MiMo-V2.6-Pro's cutoff date.
Oct 2025
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Provider Availability
K-EXAONE-236B-A23B is available from FriendliAI. MiMo-V2.6-Pro is available from Xiaomi.
K-EXAONE-236B-A23B
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against K-EXAONE-236B-A23B and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about K-EXAONE-236B-A23B vs MiMo-V2.6-Pro.