MoonshotAI: Kimi K2.6 vs Relace: Relace Search: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MoonshotAI: Kimi K2.6 (moonshotai/kimi-k2.6) vs Relace: Relace Search (relace/relace-search). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MoonshotAI: Kimi K2.6 (moonshotai/kimi-k2.6) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K2.6: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.325/1M) or route via asynchronous Batch API for 50% off standard token rates.
Relace: Relace Search (relace/relace-search) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Relace: Relace Search: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing MoonshotAI: Kimi K2.6 (API ID: moonshotai/kimi-k2.6) vs Relace: Relace Search (API ID: relace/relace-search), MoonshotAI: Kimi K2.6 offers lower standard input pricing at $0.65/1M tokens ($3.41/1M output), while MoonshotAI: Kimi K2.6 leads in cached prefix economics at $0.325/1M tokens (50% discount). MoonshotAI: Kimi K2.6 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 | MoonshotAI: Kimi K2.6 | Relace: Relace Search | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | moonshotai/kimi-k2.6 | relace/relace-search | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.65 / $3.41 | $1 / $3 | MoonshotAI: Kimi K2.6 |
| Cache Read Price (per 1M) | $0.325/1M (50% Off) | $0.5/1M (50% Off) | MoonshotAI: Kimi K2.6 |
| Context Window / Max Output | 256K In / 230K Out | 250K In / 125K Out | MoonshotAI: Kimi K2.6 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MoonshotAI: Kimi K2.6 (-0ms) |
Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end
The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search pe
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 MoonshotAI: Kimi K2.6 to Relace: Relace Search
The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search pe Verified P90 latency of 280ms.