Compare/K-EXAONE (Reasoning) vs Mercury 2

K-EXAONE (Reasoning)vsMercury 2

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

LG AI Research

K-EXAONE (Reasoning)

Input
Output
Speed
TTFT
Inception

Mercury 2

Input
$0.25/M
Output
$0.75/M
Speed
835 tok/s
TTFT
3.93s

Winner by Category

Cheaper
Mercury 2
Faster (tok/s)
Mercury 2
Lower Latency
Mercury 2
Benchmarks (2-0)
K-EXAONE (Reasoning)

Pricing Comparison

MetricK-EXAONE (Reasoning)Mercury 2
Input ($/M tokens)$0.25
Output ($/M tokens)$0.75
Cost for 1M input + 100K output tokens:
Mercury 2$0.33

Speed Comparison

Output Speed (tokens/s) — higher is better
K-EXAONE (Reasoning)
Mercury 2
835 tok/s
Time to First Token (seconds) — lower is better
K-EXAONE (Reasoning)
Mercury 2
3.93s

Editorial Analysis

Verdict. K-EXAONE (Reasoning) wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.

Strengths. K-EXAONE (Reasoning) is strongest on Coding Index (32.1), Intelligence Index (22.5). Mercury 2 leads on Coding Index (31.1), Intelligence Index (21.9).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. LG AI Research and Inception sell to overlapping but distinct developer audiences: LG AI Research tends to ship frontier reasoning models with premium positioning, while Inception often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Head-to-head deltas

  • Aggregate benchmark score (sum across 12 categories, capped at 100): K-EXAONE (Reasoning) = 55, Mercury 2 = 53. Within 15% — effectively equivalent if both meet the threshold your product requires.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
22.521.9
Coding Index
32.131.1
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
K-EXAONE (Reasoning)2 wins
0 winsMercury 2

Frequently Asked Questions

Which is cheaper, K-EXAONE (Reasoning) or Mercury 2?

Mercury 2 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.38/M tokens vs $—/M for K-EXAONE (Reasoning).

Which model performs better on benchmarks?

K-EXAONE (Reasoning) wins 2 out of 12 benchmarks compared to 0 for Mercury 2. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Mercury 2 generates tokens faster at 835 tok/s vs — tok/s. However, Mercury 2 has lower time-to-first-token (3.93s vs —s).

When should I use K-EXAONE (Reasoning) vs Mercury 2?

Choose based on your priorities: Mercury 2 for lower cost, K-EXAONE (Reasoning) for stronger benchmark performance, and Mercury 2 for faster generation. For latency-sensitive apps, check the TTFT comparison above.