Which model is better: DeepSeek V4 Pro or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is 90.9% cheaper for input tokens ($0.12 vs. $1.32 per 1M tokens) and $0.30 vs. $3.96 for output tokens (11.0x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (375ms faster than DeepSeek V4 Pro) with 165 vs. 78 tok/s throughput. DeepSeek V4 Pro leads coding evaluations at 80.6% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 90.1% 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 V4 Pro
Llama 3.3 70B Instruct
Net Delta / Advantage
Input Cost / 1M Tokens
$1.32
$0.12
Llama 3.3 70B Instruct is 11.0x cheaper
Output Cost / 1M Tokens
$3.96
$0.30
Llama 3.3 70B Instruct (92.4% lower)
Prompt Cache Read / 1M
$0.145
$0.030
Llama 3.3 70B Instruct is 4.8x cheaper
Prompt Cache Write / 1M
$1.32
$0.12
Llama 3.3 70B Instruct is 11.0x cheaper
Context Window (Max Output)
1,024k (128k out)
131k (33k out)
DeepSeek V4 Pro (7.8x larger context)
Avg TTFT Response Latency
520ms
145ms
Llama 3.3 70B Instruct (375ms faster)
Generation Speed (Throughput)
78 tok/s
165 tok/s
Llama 3.3 70B Instruct (+87 tok/s faster)
SWE-bench Verified (Coding)
80.6%
42.5%
DeepSeek V4 Pro (+38.1% lead)
GPQA Diamond (PhD STEM)
90.1%
55.6%
DeepSeek V4 Pro (+34.5% lead)
MMLU-Pro (Multi-Discipline)
88.4%
76.8%
DeepSeek V4 Pro (+11.6% lead)
MATH-500 (Quantitative Logic)
97.3%
77%
DeepSeek V4 Pro (+20.3% lead)
Inference Value Score (IVR)
67 / 100
74.6 / 100
Llama 3.3 70B Instruct (+7.6 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 V4 Pro 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 $1.32/1M input ($3.96/1M output) for DeepSeek V4 Pro.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek V4 Pro 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 520ms TTFT (78 tok/s) for DeepSeek V4 Pro.
How do DeepSeek V4 Pro and Llama 3.3 70B Instruct compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek V4 Pro scores 80.6% versus 42.5% for Llama 3.3 70B Instruct. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V4 Pro scores 90.1% versus 55.6% for Llama 3.3 70B Instruct (and 97.3% vs 77% on MATH-500).
When should you choose DeepSeek V4 Pro?
Choose DeepSeek V4 Pro (DeepSeek) for open-weight 1.6T MoE powerhouse designed for cost-effective full-repository code generation, mathematical reasoning, and persistent chain-of-thought multi-step agent workflows — offering a 1,024k context window, 520ms TTFT, and $1.32/1M input pricing ($0.145/1M 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).
