Which model is better: GPT-4o or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is 95.2% cheaper for input tokens ($0.12 vs. $2.50 per 1M tokens) and $0.30 vs. $10.00 for output tokens (20.8x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (135ms faster than GPT-4o) with 165 vs. 100 tok/s throughput. GPT-4o leads coding evaluations at 48.0% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 53.6% 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
GPT-4o
Llama 3.3 70B Instruct
Net Delta / Advantage
Input Cost / 1M Tokens
$2.50
$0.12
Llama 3.3 70B Instruct is 20.8x cheaper
Output Cost / 1M Tokens
$10.00
$0.30
Llama 3.3 70B Instruct (97.0% lower)
Prompt Cache Read / 1M
$1.250
$0.030
Llama 3.3 70B Instruct is 41.7x cheaper
Prompt Cache Write / 1M
$2.50
$0.12
Llama 3.3 70B Instruct is 20.8x cheaper
Context Window (Max Output)
131k (16k out)
131k (33k out)
Equal Context Window
Avg TTFT Response Latency
280ms
145ms
Llama 3.3 70B Instruct (135ms faster)
Generation Speed (Throughput)
100 tok/s
165 tok/s
Llama 3.3 70B Instruct (+65 tok/s faster)
SWE-bench Verified (Coding)
48%
42.5%
GPT-4o (+5.5% lead)
GPQA Diamond (PhD STEM)
53.6%
55.6%
Llama 3.3 70B Instruct (+2.0% lead)
MMLU-Pro (Multi-Discipline)
75.9%
76.8%
Llama 3.3 70B Instruct (+0.9% lead)
MATH-500 (Quantitative Logic)
76.6%
77%
Llama 3.3 70B Instruct (+0.4% lead)
Inference Value Score (IVR)
47.6 / 100
74.6 / 100
Llama 3.3 70B Instruct (+27 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: GPT-4o 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 $2.50/1M input ($10.00/1M output) for GPT-4o.
Which model has lower TTFT latency and higher tok/s throughput: GPT-4o 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 280ms TTFT (100 tok/s) for GPT-4o.
How do GPT-4o and Llama 3.3 70B Instruct compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-4o scores 48% versus 42.5% for Llama 3.3 70B Instruct. On GPQA Diamond graduate-level STEM reasoning, GPT-4o scores 53.6% versus 55.6% for Llama 3.3 70B Instruct (and 76.6% vs 77% on MATH-500).
When should you choose GPT-4o?
Choose GPT-4o (OpenAI) for general enterprise assistants, multimodal customer support, and structured JSON output pipelines — offering a 131k context window, 280ms TTFT, and $2.50/1M input pricing ($1.250/1M 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).
