Which model is better: DeepSeek R1 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is 78.2% cheaper for input tokens ($0.12 vs. $0.55 per 1M tokens) and $0.30 vs. $2.19 for output tokens (4.6x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (1305ms faster than DeepSeek R1) with 165 vs. 58 tok/s throughput. DeepSeek R1 leads coding evaluations at 49.2% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 71.5% vs. 55.6% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
DeepSeek R1
Llama 3.3 70B Instruct
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$0.12
Llama 3.3 70B Instruct is 4.6x cheaper
Output Cost / 1M Tokens
$2.19
$0.30
Llama 3.3 70B Instruct (86.3% lower)
Prompt Cache Read / 1M
$0.140
$0.030
Llama 3.3 70B Instruct is 4.7x cheaper
Prompt Cache Write / 1M
$0.55
$0.12
Llama 3.3 70B Instruct is 4.6x cheaper
Context Window (Max Output)
131k (33k out)
131k (33k out)
Equal Context Window
Avg TTFT Response Latency
1450ms
145ms
Llama 3.3 70B Instruct (1305ms faster)
Generation Speed (Throughput)
58 tok/s
165 tok/s
Llama 3.3 70B Instruct (+107 tok/s faster)
SWE-bench Verified (Coding)
49.2%
42.5%
DeepSeek R1 (+6.7% lead)
GPQA Diamond (PhD STEM)
71.5%
55.6%
DeepSeek R1 (+15.9% lead)
MMLU-Pro (Multi-Discipline)
84%
76.8%
DeepSeek R1 (+7.2% lead)
MATH-500 (Quantitative Logic)
97.3%
77%
DeepSeek R1 (+20.3% lead)
Inference Value Score (IVR)
64.8 / 100
74.6 / 100
Llama 3.3 70B Instruct (+9.8 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Which is cheaper: DeepSeek R1 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is cheaper at $0.12/1M input tokens ($0.30/1M output) compared to $0.55/1M input ($2.19/1M output) for DeepSeek R1.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct delivers faster first-token latency at 145ms TTFT (165 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and Llama 3.3 70B Instruct compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 42.5% for Llama 3.3 70B Instruct. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 55.6% for Llama 3.3 70B Instruct (and 97.3% vs 77% on MATH-500).
When should you choose DeepSeek R1?
Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).
When should you choose Llama 3.3 70B Instruct?
Choose Llama 3.3 70B Instruct (Meta) for cost-optimized on-premise deployments, fast text transformation, structured entity extraction, and enterprise dialogue pipelines — offering a 131k context window, 145ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
