DeepSeek Deepseek R1 Zero vs Meta Llama Llama 3.3 70B Instruct Fp8: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek Deepseek R1 Zero (deepseek/deepseek-r1-zero) vs Meta Llama Llama 3.3 70B Instruct Fp8 (meta-llama/llama-3.3-70b-instruct-fp8). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek Deepseek R1 Zero (deepseek/deepseek-r1-zero) — Automatic Disk KV Cache (0.05/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.022/1M off-peak input tokens (vs $0.66 off-peak base) with zero code changes required, plus off-peak UTC scheduling discounts.
Meta Llama Llama 3.3 70B Instruct Fp8 (meta-llama/llama-3.3-70b-instruct-fp8) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta Llama Llama 3.3 70B Instruct Fp8: Activate ephemeral prefix caching to slash input costs by 90% (down to 0.01/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek Deepseek R1 Zero (API ID: deepseek/deepseek-r1-zero) vs Meta Llama Llama 3.3 70B Instruct Fp8 (API ID: meta-llama/llama-3.3-70b-instruct-fp8), Meta Llama Llama 3.3 70B Instruct Fp8 offers lower standard input pricing at $0.1/1M tokens ($0.25/1M output), while Meta Llama Llama 3.3 70B Instruct Fp8 leads in cached prefix economics at $0.01/1M tokens (90% discount). DeepSeek Deepseek R1 Zero achieves a 0ms faster P90 time-to-first-token latency (195ms vs 195ms).
Gateway-synced rate ($0.5/$2 per 1M, flat 128K window). Verify upstream provider cache TTL and regional routing before high-volume batch runs.
Gateway-synced rate ($0.1/$0.25 per 1M, flat 128K window). Verify upstream provider cache TTL and regional routing before high-volume batch runs.
| Specification / FinOps Dimension | DeepSeek Deepseek R1 Zero | Meta Llama Llama 3.3 70B Instruct Fp8 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1-zero | meta-llama/llama-3.3-70b-instruct-fp8 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2 | $0.1 / $0.25 | Meta Llama Llama 3.3 70B Instruct Fp8 |
| Cache Read Price (per 1M) | $0.05/1M (90% Off) | $0.01/1M (90% Off) | Meta Llama Llama 3.3 70B Instruct Fp8 |
| Context Window / Max Output | 128K In / 64K Out | 128K In / 64K Out | DeepSeek Deepseek R1 Zero |
| P90 First-Token / Edge Latency | 195ms | 195ms | DeepSeek Deepseek R1 Zero (-0ms) |
DeepSeek Deepseek R1 Zero (deepseek/deepseek-r1-zero): OpenRouter gateway-synced model at $0.5/1M input, $2/1M output, $0.05/1M cached input (128K context window).
Meta Llama Llama 3.3 70B Instruct Fp8
API ID: meta-llama/llama-3.3-70b-instruct-fp8 • OpenRouter SyncedMeta Llama Llama 3.3 70B Instruct Fp8 (meta-llama/llama-3.3-70b-instruct-fp8): OpenRouter gateway-synced model at $0.1/1M input, $0.25/1M output, $0.01/1M cached input (128K context window).
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 R1 Zero to Meta Llama Llama 3.3 70B Instruct Fp8
Meta Llama Llama 3.3 70B Instruct Fp8 (meta-llama/llama-3.3-70b-instruct-fp8): OpenRouter gateway-synced model at $0.1/1M input, $0.25/1M output, $0.01/1M cached input (128K context window). Verified P90 latency of 195ms.