DeepSeek-R1 (Reasoner) vs OpenAI GPT-6.1 Terra — API Pricing, 150K Cache & Latency Benchmark
Side-by-side unit economics audit comparing DeepSeek-R1 (Reasoner) (deepseek/deepseek-reasoner: Input $0.55/1M, Output $2.19/1M, Warm Cache Read $0.14/1M) against OpenAI GPT-6.1 Terra (openai/gpt-6.1-terra: Input $1/1M, Output $5/1M, Warm Cache Read $0.05/1M).
Activate 4-breakpoint prefix caching on DeepSeek-R1 (Reasoner) to cut input token costs from $0.55/1M down to $0.14/1M, or route async workloads via Batch API for 50% off.
Lock static system prompt prefixes on OpenAI GPT-6.1 Terra to pay $0.05/1M warm cache rates instead of $1/1M uncached input, plus eligible startup cloud credits.
In Etuki FinOps telemetry comparing DeepSeek-R1 (Reasoner) (API ID: deepseek/deepseek-reasoner) vs OpenAI GPT-6.1 Terra (API ID: openai/gpt-6.1-terra), DeepSeek-R1 (Reasoner) offers lower standard uncached input pricing at $0.55/1M tokens, while DeepSeek-R1 (Reasoner) leads in 10-turn 150K cache-pinned coding economics at $0.3153 per 10 turns (saving $0.0022 over OpenAI GPT-6.1 Terra). For latency-sensitive interactive agents, OpenAI GPT-6.1 Terra delivers 615ms faster P90 time-to-first-token (265ms vs 880ms).
| Specification / FinOps Dimension | DeepSeek-R1 (Reasoner) (deepseek/deepseek-reasoner) | OpenAI GPT-6.1 Terra (openai/gpt-6.1-terra) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-reasoner | openai/gpt-6.1-terra | UMCE 563-Registry Locked |
| 563-Registry & Routing Eligibility | is_known: true | routable: true | is_known: true | routable: true | Both Merkle Verified |
| Standard Uncached Input (/1M tokens) | $0.55 / 1M | $1 / 1M | DeepSeek-R1 (Reasoner) |
| Warm Prefix Cache Read (/1M tokens) | $0.14 / 1M | $0.05 / 1M | OpenAI GPT-6.1 Terra |
| Standard Output (/1M tokens) | $2.19 / 1M | $5 / 1M | DeepSeek-R1 (Reasoner) |
| Context Window Capacity | 200K Tokens | 200K Tokens | DeepSeek-R1 (Reasoner) |
| 150K Prompt × 10 Turns (Unpinned Cold + 2K Out) | $0.8688 | $1.6000 | DeepSeek-R1 (Reasoner) |
| 150K × 10 Turns Case A (100% Static Pinned) | $0.3153 (saves $0.5535) | $0.3175 (saves $1.2825) | DeepSeek-R1 (Reasoner) (-$0.0022) |
| 150K × 10 Turns Case B (90% Static + 10% Tail) | $0.3707 (saves $0.4981) | $0.4457 (saves $1.1543) | DeepSeek-R1 (Reasoner) |
| P50 / P90 First-Token Latency | 598ms / 880ms (64 tok/s) | 180ms / 265ms (132 tok/s) | OpenAI GPT-6.1 Terra (-615ms) |
1. Full-Turn Rate-Card Substitution for DeepSeek-R1 (Reasoner): At an uncached input rate of $0.55/1M, output rate of $2.19/1M, and warm cache read rate of $0.14/1M, running 10 consecutive 150,000-token coding agent turns (with 2,000 output tokens/turn) without prefix pinning costs $0.8688. Locking 100% static prefixes (Case A: 1 cold turn + 9 warm cached turns + 20K total output) reduces 10-turn spend to $0.3153 (saving $0.5535); under a 90% static prefix + 10% dynamic tail workload (Case B), 10-turn spend is $0.3707.
2. Full-Turn Rate-Card Substitution for OpenAI GPT-6.1 Terra & Winner Verdict: On OpenAI GPT-6.1 Terra ($1/1M input, $5/1M output, $0.05/1M warm cache), 10 unpinned 150K turns cost $1.6000, dropping to $0.3175 in Case A and $0.4457 in Case B. Across every 10-turn 150K coding loop (including output tokens), DeepSeek-R1 (Reasoner) delivers an additional $0.0022 Case-A cost advantage.
Production Workload TCO: 25,000 MAU • 50M Tokens/Mo (50% Cache Hit) • 2,000 GB Egress
💡 Optimal FinOps Verdict: Deploying DeepSeek-R1 (Reasoner) saves $35.30/month ($424/year) at 25,000 MAU & 50M monthly tokens (80% input / 20% output split with 50% prefix cache hit rate).