Which model is better: GPT-4o Mini or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is 20.0% cheaper for input tokens ($0.12 vs. $0.15 per 1M tokens) and $0.30 vs. $0.60 for output tokens (1.2x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (75ms faster than GPT-4o Mini) with 165 vs. 140 tok/s throughput. Llama 3.3 70B Instruct leads coding evaluations at 42.5% SWE-bench Verified vs. 41.0% for GPT-4o Mini and 55.6% vs. 40.2% 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 Mini
Llama 3.3 70B Instruct
Net Delta / Advantage
Input Cost / 1M Tokens
$0.15
$0.12
Llama 3.3 70B Instruct is 1.3x cheaper
Output Cost / 1M Tokens
$0.60
$0.30
Llama 3.3 70B Instruct (50.0% lower)
Prompt Cache Read / 1M
$0.075
$0.030
Llama 3.3 70B Instruct is 2.5x cheaper
Prompt Cache Write / 1M
$0.15
$0.12
Llama 3.3 70B Instruct is 1.3x cheaper
Context Window (Max Output)
131k (16k out)
131k (33k out)
Equal Context Window
Avg TTFT Response Latency
220ms
145ms
Llama 3.3 70B Instruct (75ms faster)
Generation Speed (Throughput)
140 tok/s
165 tok/s
Llama 3.3 70B Instruct (+25 tok/s faster)
SWE-bench Verified (Coding)
41%
42.5%
Llama 3.3 70B Instruct (+1.5% lead)
GPQA Diamond (PhD STEM)
40.2%
55.6%
Llama 3.3 70B Instruct (+15.4% lead)
MMLU-Pro (Multi-Discipline)
68%
76.8%
Llama 3.3 70B Instruct (+8.8% lead)
MATH-500 (Quantitative Logic)
70.2%
77%
Llama 3.3 70B Instruct (+6.8% lead)
Inference Value Score (IVR)
67.2 / 100
74.6 / 100
Llama 3.3 70B Instruct (+7.4 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 Mini 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 $0.15/1M input ($0.60/1M output) for GPT-4o Mini.
Which model has lower TTFT latency and higher tok/s throughput: GPT-4o Mini 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 220ms TTFT (140 tok/s) for GPT-4o Mini.
How do GPT-4o Mini and Llama 3.3 70B Instruct compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-4o Mini scores 41% versus 42.5% for Llama 3.3 70B Instruct. On GPQA Diamond graduate-level STEM reasoning, GPT-4o Mini scores 40.2% versus 55.6% for Llama 3.3 70B Instruct (and 70.2% vs 77% on MATH-500).
When should you choose GPT-4o Mini?
Choose GPT-4o Mini (OpenAI) for high-volume classification, lightweight RAG chatbots, and structured JSON extraction — offering a 131k context window, 220ms TTFT, and $0.15/1M input pricing ($0.075/1M 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).
