Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM 5V Turbo (Reasoning) | Mercury 2 |
|---|---|---|
| Input ($/M tokens) | — | $0.25 |
| Output ($/M tokens) | — | $0.75 |
Verdict. GLM 5V Turbo (Reasoning) and Mercury 2 split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
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. GLM 5V Turbo (Reasoning) is strongest on Intelligence Index (35.3). 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. Z AI and Inception sell to overlapping but distinct developer audiences: Z AI 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
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 GLM 5V Turbo (Reasoning).
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. 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, both perform similarly on benchmarks, and Mercury 2 for faster generation. For latency-sensitive apps, check the TTFT comparison above.