Which model is better: Gemini 4 Argon or OpenAI o4-mini?
OpenAI o4-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, OpenAI o4-mini delivers lower response latency at 680ms TTFT (170ms faster than Gemini 4 Argon) with 125 vs. 115 tok/s throughput. Gemini 4 Argon leads coding evaluations at 84.5% SWE-bench Verified vs. 68.1% for OpenAI o4-mini and 93.8% vs. 81.4% 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 o4-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$1.10
OpenAI o4-mini is 1.8x cheaper
Output Cost / 1M Tokens
$10.00
$4.40
OpenAI o4-mini (56.0% lower)
Prompt Cache Read / 1M
$0.100
$0.275
Gemini 4 Argon is 2.8x 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
680ms
OpenAI o4-mini (170ms faster)
Generation Speed (Throughput)
115 tok/s
125 tok/s
OpenAI o4-mini (+10 tok/s faster)
SWE-bench Verified (Coding)
84.5%
68.1%
Gemini 4 Argon (+16.4% lead)
GPQA Diamond (PhD STEM)
93.8%
81.4%
Gemini 4 Argon (+12.4% lead)
MMLU-Pro (Multi-Discipline)
91.4%
85.6%
Gemini 4 Argon (+5.8% lead)
MATH-500 (Quantitative Logic)
98.2%
97.8%
Gemini 4 Argon (+0.4% lead)
Inference Value Score (IVR)
62.6 / 100
63.7 / 100
OpenAI o4-mini (+1.1 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).
Which is cheaper: Gemini 4 Argon or OpenAI o4-mini?
OpenAI o4-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 o4-mini?
OpenAI o4-mini delivers faster first-token latency at 680ms TTFT (125 tok/s throughput) compared to 850ms TTFT (115 tok/s) for Gemini 4 Argon.
How do Gemini 4 Argon and OpenAI o4-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 4 Argon scores 84.5% versus 68.1% for OpenAI o4-mini. On GPQA Diamond graduate-level STEM reasoning, Gemini 4 Argon scores 93.8% versus 81.4% for OpenAI o4-mini (and 98.2% vs 97.8% 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 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).
