Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | DeepSeek R1 Distill Llama 70B | GPT-5.6 Luna (low) |
|---|---|---|
| Input ($/M tokens) | $0.7 | $0.2 |
| Output ($/M tokens) | $1.1 | $1.2 |
Verdict. GPT-5.6 Luna (low) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 0.9× the per-million-token cost, DeepSeek R1 Distill Llama 70B is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). DeepSeek R1 Distill Llama 70B makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. DeepSeek R1 Distill Llama 70B is strongest on Intelligence Index (9.8). GPT-5.6 Luna (low) leads on Coding Index (44.2), Intelligence Index (33.9).
Speed. On throughput, GPT-5.6 Luna (low) generates tokens at 138 tok/s versus 25 tok/s — about 82% faster. On time-to-first-token, DeepSeek R1 Distill Llama 70B responds in 1020ms vs 1740ms, which matters most for chat-style UIs.
Provider. DeepSeek and OpenAI sell to overlapping but distinct developer audiences: DeepSeek tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): DeepSeek R1 Distill Llama 70B costs $37.50 ($450/year); GPT-5.6 Luna (low) costs $24.00 ($288/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): DeepSeek R1 Distill Llama 70B ≈ $5.70/run, GPT-5.6 Luna (low) ≈ $3.40/run. At agent/realtime scale (200M input / 100M output per million requests): DeepSeek R1 Distill Llama 70B ≈ $250/run, GPT-5.6 Luna (low) ≈ $160/run. GPT-5.6 Luna (low) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
GPT-5.6 Luna (low) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.45/M tokens vs $0.80/M for DeepSeek R1 Distill Llama 70B.
GPT-5.6 Luna (low) wins 2 out of 12 benchmarks compared to 0 for DeepSeek R1 Distill Llama 70B. See the detailed benchmark chart above for per-category results.
GPT-5.6 Luna (low) generates tokens faster at 138 tok/s vs 25 tok/s. DeepSeek R1 Distill Llama 70B also has lower time-to-first-token (1.02s vs 1.74s).
Choose based on your priorities: GPT-5.6 Luna (low) for lower cost, GPT-5.6 Luna (low) for stronger benchmark performance, and GPT-5.6 Luna (low) for faster generation. For latency-sensitive apps, check the TTFT comparison above.