DeepSeek: DeepSeek V3.2 Exp vs OpenAI: GPT-3.5 Turbo 16k: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V3.2 Exp (deepseek/deepseek-v3.2-exp) vs OpenAI: GPT-3.5 Turbo 16k (openai/gpt-3.5-turbo-16k). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: DeepSeek V3.2 Exp (deepseek/deepseek-v3.2-exp) — Automatic Disk KV Cache ($0.135/1M) & 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.
OpenAI: GPT-3.5 Turbo 16k (openai/gpt-3.5-turbo-16k) Startup Credits & Batch API 50% Off Promo
OpenAI GPT-6.1 Sol ($2/$10 base rate) qualifies for immediate 75% automatic prefix caching ($0.50/1M), 50% off 24-hour Batch API completions ($1/$5), and up to $150,000 in OpenAI/Azure Foundry credits via Microsoft for Startups Founders Hub (no VC funding required for initial tiers).
In Etuki October 2026 FinOps telemetry comparing DeepSeek: DeepSeek V3.2 Exp (API ID: deepseek/deepseek-v3.2-exp) vs OpenAI: GPT-3.5 Turbo 16k (API ID: openai/gpt-3.5-turbo-16k), DeepSeek: DeepSeek V3.2 Exp offers lower standard input pricing at $0.27/1M tokens ($0.41/1M output), while DeepSeek: DeepSeek V3.2 Exp leads in cached prefix economics at $0.135/1M tokens (50% discount). DeepSeek: DeepSeek V3.2 Exp 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 | DeepSeek: DeepSeek V3.2 Exp | OpenAI: GPT-3.5 Turbo 16k | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-v3.2-exp | openai/gpt-3.5-turbo-16k | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.27 / $0.41 | $3 / $4 | DeepSeek: DeepSeek V3.2 Exp |
| Cache Read Price (per 1M) | $0.135/1M (50% Off) | $1.5/1M (50% Off) | DeepSeek: DeepSeek V3.2 Exp |
| Context Window / Max Output | 160K In / 144K Out | 16K In / 4K Out | DeepSeek: DeepSeek V3.2 Exp |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V3.2 Exp (-0ms) |
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attentio
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...
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 DeepSeek: DeepSeek V3.2 Exp to OpenAI: GPT-3.5 Turbo 16k
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up... Verified P90 latency of 280ms.