Meta: Llama 3.3 70B Instruct vs DeepSeek: DeepSeek V4 Pro 0423: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama 3.3 70B Instruct (meta-llama/llama-3.3-70b-instruct) vs DeepSeek: DeepSeek V4 Pro 0423 (deepseek/deepseek-v4-pro). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta: Llama 3.3 70B Instruct (meta-llama/llama-3.3-70b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 3.3 70B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.295/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V4 Pro 0423 (deepseek/deepseek-v4-pro) — Automatic Disk KV Cache ($0.4777/1M) & 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 Meta: Llama 3.3 70B Instruct (API ID: meta-llama/llama-3.3-70b-instruct) vs DeepSeek: DeepSeek V4 Pro 0423 (API ID: deepseek/deepseek-v4-pro), Meta: Llama 3.3 70B Instruct offers lower standard input pricing at $0.59/1M tokens ($0.79/1M output), while Meta: Llama 3.3 70B Instruct leads in cached prefix economics at $0.295/1M tokens (50% discount). Meta: Llama 3.3 70B Instruct achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
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| Specification / FinOps Dimension | Meta: Llama 3.3 70B Instruct | DeepSeek: DeepSeek V4 Pro 0423 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-3.3-70b-instruct | deepseek/deepseek-v4-pro | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.59 / $0.79 | $0.9553 / $1.9105 | Meta: Llama 3.3 70B Instruct |
| Cache Read Price (per 1M) | $0.295/1M (50% Off) | $0.4777/1M (50% Off) | Meta: Llama 3.3 70B Instruct |
| Context Window / Max Output | 128K In / 32K Out | 1024K In / 375K Out | DeepSeek: DeepSeek V4 Pro 0423 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama 3.3 70B Instruct (-0ms) |
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text on
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designe
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 Meta: Llama 3.3 70B Instruct to DeepSeek: DeepSeek V4 Pro 0423
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designe Verified P90 latency of 280ms.