Which model is better: Llama 3.3 70B Instruct or OpenAI o4-mini?
Llama 3.3 70B Instruct is 89.1% cheaper for input tokens ($0.12 vs. $1.10 per 1M tokens) and $0.30 vs. $4.40 for output tokens (9.2x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (535ms faster than OpenAI o4-mini) with 165 vs. 125 tok/s throughput. OpenAI o4-mini leads coding evaluations at 68.1% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 81.4% 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
Llama 3.3 70B Instruct
OpenAI o4-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$0.12
$1.10
Llama 3.3 70B Instruct is 9.2x cheaper
Output Cost / 1M Tokens
$0.30
$4.40
Llama 3.3 70B Instruct (93.2% lower)
Prompt Cache Read / 1M
$0.030
$0.275
Llama 3.3 70B Instruct is 9.2x cheaper
Prompt Cache Write / 1M
$0.12
$1.10
Llama 3.3 70B Instruct is 9.2x cheaper
Context Window (Max Output)
131k (33k out)
205k (100k out)
OpenAI o4-mini (1.6x larger context)
Avg TTFT Response Latency
145ms
680ms
Llama 3.3 70B Instruct (535ms faster)
Generation Speed (Throughput)
165 tok/s
125 tok/s
Llama 3.3 70B Instruct (+40 tok/s faster)
SWE-bench Verified (Coding)
42.5%
68.1%
OpenAI o4-mini (+25.6% lead)
GPQA Diamond (PhD STEM)
55.6%
81.4%
OpenAI o4-mini (+25.8% lead)
MMLU-Pro (Multi-Discipline)
76.8%
85.6%
OpenAI o4-mini (+8.8% lead)
MATH-500 (Quantitative Logic)
77%
97.8%
OpenAI o4-mini (+20.8% lead)
Inference Value Score (IVR)
74.6 / 100
63.7 / 100
Llama 3.3 70B Instruct (+10.9 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: Llama 3.3 70B Instruct or OpenAI o4-mini?
Llama 3.3 70B Instruct is cheaper at $0.12/1M input tokens ($0.30/1M output) compared to $1.10/1M input ($4.40/1M output) for OpenAI o4-mini.
Which model has lower TTFT latency and higher tok/s throughput: Llama 3.3 70B Instruct or OpenAI o4-mini?
Llama 3.3 70B Instruct delivers faster first-token latency at 145ms TTFT (165 tok/s throughput) compared to 680ms TTFT (125 tok/s) for OpenAI o4-mini.
How do Llama 3.3 70B Instruct and OpenAI o4-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Llama 3.3 70B Instruct scores 42.5% versus 68.1% for OpenAI o4-mini. On GPQA Diamond graduate-level STEM reasoning, Llama 3.3 70B Instruct scores 55.6% versus 81.4% for OpenAI o4-mini (and 77% vs 97.8% on MATH-500).
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).
When should you choose OpenAI o4-mini?
Choose OpenAI o4-mini (OpenAI) for automated PR review, high-volume agentic coding loops, SQL/data analysis, and visual document reasoning — offering a 204k context window, 680ms TTFT, and $1.10/1M input pricing ($0.275/1M cached reads).
