Tencent: Hy-MT2-7B vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Tencent: Hy-MT2-7B (tencent/hy-mt2-7b) vs DeepSeek: R1 (deepseek/deepseek-r1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Tencent: Hy-MT2-7B (tencent/hy-mt2-7b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Tencent: Hy-MT2-7B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.037/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: R1 (deepseek/deepseek-r1) — Automatic Disk KV Cache ($0.35/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Tencent: Hy-MT2-7B (API ID: tencent/hy-mt2-7b) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), Tencent: Hy-MT2-7B offers lower standard input pricing at $0.074/1M tokens ($0.295/1M output), while Tencent: Hy-MT2-7B leads in cached prefix economics at $0.037/1M tokens (50% discount). Tencent: Hy-MT2-7B 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 | Tencent: Hy-MT2-7B | DeepSeek: R1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | tencent/hy-mt2-7b | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.074 / $0.295 | $0.7 / $2.5 | Tencent: Hy-MT2-7B |
| Cache Read Price (per 1M) | $0.037/1M (50% Off) | $0.35/1M (50% Off) | Tencent: Hy-MT2-7B |
| Context Window / Max Output | 8K In / 4K Out | 63K In / 16K Out | DeepSeek: R1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Tencent: Hy-MT2-7B (-0ms) |
Hy-MT2-7B is a 7B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delim
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf
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 Tencent: Hy-MT2-7B to DeepSeek: R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf Verified P90 latency of 280ms.