Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Sonar Reasoning Pro | Mercury 2 |
|---|---|---|
| Input ($/M tokens) | — | $0.25 |
| Output ($/M tokens) | — | $0.75 |
Verdict. Mercury 2 takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
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. Sonar Reasoning Pro is strongest on Intelligence Index (18.0). 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. Perplexity and Inception sell to overlapping but distinct developer audiences: Perplexity 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 Sonar Reasoning Pro.
Mercury 2 wins 2 out of 12 benchmarks compared to 0 for Sonar Reasoning Pro. 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, Mercury 2 for stronger benchmark performance, and Mercury 2 for faster generation. For latency-sensitive apps, check the TTFT comparison above.