Relace: Relace Search vs MoonshotAI: Kimi K2 0905: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Relace: Relace Search (relace/relace-search) vs MoonshotAI: Kimi K2 0905 (moonshotai/kimi-k2-0905). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
MoonshotAI: Kimi K2 0905 (moonshotai/kimi-k2-0905) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K2 0905: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.3/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Relace: Relace Search (API ID: relace/relace-search) vs MoonshotAI: Kimi K2 0905 (API ID: moonshotai/kimi-k2-0905), MoonshotAI: Kimi K2 0905 offers lower standard input pricing at $0.6/1M tokens ($2.5/1M output), while MoonshotAI: Kimi K2 0905 leads in cached prefix economics at $0.3/1M tokens (50% discount). Relace: Relace Search 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 | Relace: Relace Search | MoonshotAI: Kimi K2 0905 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | relace/relace-search | moonshotai/kimi-k2-0905 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1 / $3 | $0.6 / $2.5 | MoonshotAI: Kimi K2 0905 |
| Cache Read Price (per 1M) | $0.5/1M (50% Off) | $0.3/1M (50% Off) | MoonshotAI: Kimi K2 0905 |
| Context Window / Max Output | 250K In / 125K Out | 256K In / 96K Out | Relace: Relace Search |
| P90 First-Token / Edge Latency | 280ms | 280ms | Relace: Relace Search (-0ms) |
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
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trilli
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 Relace: Relace Search to MoonshotAI: Kimi K2 0905
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trilli Verified P90 latency of 280ms.