OpenAI GPT-5.3-Codex vs DeepSeek-R1 (Reasoner): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing OpenAI GPT-5.3-Codex (gpt-5.3-codex) vs DeepSeek-R1 (Reasoner) (deepseek-reasoner). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
OpenAI GPT-5.3-Codex (gpt-5.3-codex) 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).
DeepSeek-R1 (Reasoner) (deepseek-reasoner) — Automatic Disk KV Cache ($0.275/1M) & 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 OpenAI GPT-5.3-Codex (API ID: gpt-5.3-codex) vs DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner), DeepSeek-R1 (Reasoner) offers lower standard input pricing at $0.55/1M tokens ($2.19/1M output), while DeepSeek-R1 (Reasoner) leads in cached prefix economics at $0.275/1M tokens (50% discount). OpenAI GPT-5.3-Codex achieves a 290ms faster P90 time-to-first-token latency (250ms vs 540ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | OpenAI GPT-5.3-Codex | DeepSeek-R1 (Reasoner) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | gpt-5.3-codex | deepseek-reasoner | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1.75 / $14 | $0.55 / $2.19 | DeepSeek-R1 (Reasoner) |
| Cache Read Price (per 1M) | $0.875/1M (50% Off) | $0.275/1M (50% Off) | DeepSeek-R1 (Reasoner) |
| Context Window / Max Output | 1000K In / 128K Out | 400K In / 128K Out | OpenAI GPT-5.3-Codex |
| P90 First-Token / Edge Latency | 250ms | 540ms | OpenAI GPT-5.3-Codex (-290ms) |
Dedicated agentic software engineering model tuned for multi-hour terminal and IDE sessions.
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
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 OpenAI GPT-5.3-Codex to DeepSeek-R1 (Reasoner)
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis. Verified P90 latency of 540ms.