DeepSeek: DeepSeek V3.1 vs OpenAI: gpt-oss-120b (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1) vs OpenAI: gpt-oss-120b (batch) (openai/gpt-oss-120b:batch). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1) — Automatic Disk KV Cache ($0.125/1M) & 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-oss-120b (batch) (openai/gpt-oss-120b:batch) 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 V3.1 (API ID: deepseek/deepseek-chat-v3.1) vs OpenAI: gpt-oss-120b (batch) (API ID: openai/gpt-oss-120b:batch), OpenAI: gpt-oss-120b (batch) offers lower standard input pricing at $0.0296/1M tokens ($0.136/1M output), while OpenAI: gpt-oss-120b (batch) leads in cached prefix economics at $0.0148/1M tokens (50% discount). DeepSeek: DeepSeek V3.1 achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
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Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek: DeepSeek V3.1 | OpenAI: gpt-oss-120b (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-chat-v3.1 | openai/gpt-oss-120b:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.25 / $0.95 | $0.0296 / $0.136 | OpenAI: gpt-oss-120b (batch) |
| Cache Read Price (per 1M) | $0.125/1M (50% Off) | $0.0148/1M (50% Off) | OpenAI: gpt-oss-120b (batch) |
| Context Window / Max Output | 160K In / 32K Out | 128K In / 115K Out | OpenAI: gpt-oss-120b (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V3.1 (-0ms) |
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. I
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 V3.1 to OpenAI: gpt-oss-120b (batch)
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. I Verified P90 latency of 280ms.