Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | K-EXAONE 2.0 0803 | Mercury 2 |
|---|---|---|
| Input ($/M tokens) | — | $0.25 |
| Output ($/M tokens) | — | $0.75 |
Verdict. K-EXAONE 2.0 0803 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 2.0 0803 is strongest on Coding Index (40.6), Intelligence Index (31.0). 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.
Data from Artificial Analysis API — 12 benchmarks
Mercury 2 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.38/M tokens vs $—/M for K-EXAONE 2.0 0803.
K-EXAONE 2.0 0803 wins 2 out of 12 benchmarks compared to 0 for Mercury 2. See the detailed benchmark chart above for per-category results.
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).
Choose based on your priorities: Mercury 2 for lower cost, K-EXAONE 2.0 0803 for stronger benchmark performance, and Mercury 2 for faster generation. For latency-sensitive apps, check the TTFT comparison above.