Morph: Morph V3 Large vs Inception: Mercury 2.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Morph: Morph V3 Large (morph/morph-v3-large) vs Inception: Mercury 2.5 (inception/mercury-2.5). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Morph: Morph V3 Large (morph/morph-v3-large) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Morph: Morph V3 Large: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.45/1M) or route via asynchronous Batch API for 50% off standard token rates.
Inception: Mercury 2.5 (inception/mercury-2.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Inception: Mercury 2.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.02/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Morph: Morph V3 Large (API ID: morph/morph-v3-large) vs Inception: Mercury 2.5 (API ID: inception/mercury-2.5), Inception: Mercury 2.5 offers lower standard input pricing at $0.04/1M tokens ($0.15/1M output), while Inception: Mercury 2.5 leads in cached prefix economics at $0.02/1M tokens (50% discount). Morph: Morph V3 Large achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
Auto-discovered via live global telemetry registry.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | Morph: Morph V3 Large | Inception: Mercury 2.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | morph/morph-v3-large | inception/mercury-2.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.9 / $1.9 | $0.04 / $0.15 | Inception: Mercury 2.5 |
| Cache Read Price (per 1M) | $0.45/1M (50% Off) | $0.02/1M (50% Off) | Inception: Mercury 2.5 |
| Context Window / Max Output | 256K In / 128K Out | 254K In / 64K Out | Morph: Morph V3 Large |
| P90 First-Token / Edge Latency | 280ms | 280ms | Morph: Morph V3 Large (-0ms) |
Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following
Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple t
Real-Time Stack Cost & Hidden-Fee Simulator (MAU / Token / Egress / GMV)
Calibrated with Reddit r/FinOps & r/devops telemetry: Includes Context Caching, Bandwidth Egress, and MoR Tax
🛰️Switch from Morph: Morph V3 Large to Inception: Mercury 2.5
Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple t Verified P90 latency of 280ms.