Which model is better: Gemini 4 Argon or OpenAI o3-mini?
OpenAI o3-mini is 45.0% cheaper for input tokens ($1.10 vs. $2.00 per 1M tokens) and $4.40 vs. $10.00 for output tokens (1.8x cost advantage). In terms of operational performance, Gemini 4 Argon delivers lower response latency at 850ms TTFT (100ms faster than OpenAI o3-mini) with 115 vs. 98 tok/s throughput. Gemini 4 Argon leads coding evaluations at 84.5% SWE-bench Verified vs. 49.3% for OpenAI o3-mini and 93.8% vs. 79.7% 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
Gemini 4 Argon
OpenAI o3-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$1.10
OpenAI o3-mini is 1.8x cheaper
Output Cost / 1M Tokens
$10.00
$4.40
OpenAI o3-mini (56.0% lower)
Prompt Cache Read / 1M
$0.100
$0.550
Gemini 4 Argon is 5.5x cheaper
Prompt Cache Write / 1M
$0.50
$1.10
Gemini 4 Argon is 2.2x cheaper
Context Window (Max Output)
1,024k (1000k out)
205k (100k out)
Gemini 4 Argon (5.0x larger context)
Avg TTFT Response Latency
850ms
950ms
Gemini 4 Argon (100ms faster)
Generation Speed (Throughput)
115 tok/s
98 tok/s
Gemini 4 Argon (+17 tok/s faster)
SWE-bench Verified (Coding)
84.5%
49.3%
Gemini 4 Argon (+35.2% lead)
GPQA Diamond (PhD STEM)
93.8%
79.7%
Gemini 4 Argon (+14.1% lead)
MMLU-Pro (Multi-Discipline)
91.4%
83.2%
Gemini 4 Argon (+8.2% lead)
MATH-500 (Quantitative Logic)
98.2%
97.9%
Gemini 4 Argon (+0.3% lead)
Inference Value Score (IVR)
62.6 / 100
59.7 / 100
Gemini 4 Argon (+2.9 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Gemini 4 Argon (Google) for engineered with a 1M output token limit for long-horizon software engineering, cybersecurity vulnerability remediation, and deep multimodal enterprise work across legal and financial domains — offering a 1,024k context window, 850ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
Choose OpenAI o3-mini (OpenAI) for competitive math reasoning, automated unit test generation, and structured logic pipelines — offering a 204k context window, 950ms TTFT, and $1.10/1M input pricing ($0.550/1M cached reads).
Which is cheaper: Gemini 4 Argon or OpenAI o3-mini?
OpenAI o3-mini is cheaper at $1.10/1M input tokens ($4.40/1M output) compared to $2.00/1M input ($10.00/1M output) for Gemini 4 Argon.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 4 Argon or OpenAI o3-mini?
Gemini 4 Argon delivers faster first-token latency at 850ms TTFT (115 tok/s throughput) compared to 950ms TTFT (98 tok/s) for OpenAI o3-mini.
How do Gemini 4 Argon and OpenAI o3-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 4 Argon scores 84.5% versus 49.3% for OpenAI o3-mini. On GPQA Diamond graduate-level STEM reasoning, Gemini 4 Argon scores 93.8% versus 79.7% for OpenAI o3-mini (and 98.2% vs 97.9% on MATH-500).
When should you choose Gemini 4 Argon?
Choose Gemini 4 Argon (Google) for engineered with a 1M output token limit for long-horizon software engineering, cybersecurity vulnerability remediation, and deep multimodal enterprise work across legal and financial domains — offering a 1,024k context window, 850ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
When should you choose OpenAI o3-mini?
Choose OpenAI o3-mini (OpenAI) for competitive math reasoning, automated unit test generation, and structured logic pipelines — offering a 204k context window, 950ms TTFT, and $1.10/1M input pricing ($0.550/1M cached reads).
