K-EXAONE-236B-A23B vs Mercury 3
Mercury 3 leads the LLM Stats Score 36.1 to 25.8. Mercury 3 is 4.3x cheaper per token.
LG AI Research · Inception · Updated for 2026
Which is better?
Mercury 3 leads the overall LLM Stats Score 36.1 to 25.8, ranking #98 overall.
In the 1 individual benchmarks reported for both models, Mercury 3 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Mercury 3 is roughly 4.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mercury 3 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
- you want predictable pricing at $0.60/M input and $1.00/M output
Choose Mercury 3
- overall performance matters — it scores 36.1 and ranks #98 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 4.3x cheaper per token
- 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 · 12 for Mercury 3
K-EXAONE-236B-A23B outperforms in 0 benchmarks, while Mercury 3 is better at 1 benchmark (IFBench).
Mercury 3 significantly outperforms across most benchmarks.
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 6.0x more expensive than Mercury 3 ($0.10/1M tokens).
For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 2.9x more expensive than Mercury 3 ($0.35/1M tokens).
In conclusion, K-EXAONE-236B-A23B is more expensive than Mercury 3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Mercury 3 accepts 1,000,000 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. Mercury 3 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
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 Mercury 3 was released on 2026-10-19.
Mercury 3 is 10 months newer than K-EXAONE-236B-A23B.
Dec 31, 2025
9 months ago
Oct 19, 2026
3 days ago
9mo newerKnowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while Mercury 3'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 Mercury 3's cutoff date.
Oct 2025
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Provider Availability
K-EXAONE-236B-A23B is available from FriendliAI. Mercury 3 is available from Inception.
K-EXAONE-236B-A23B
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against K-EXAONE-236B-A23B and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about K-EXAONE-236B-A23B vs Mercury 3.