Which model is better: Gemini 3.1 Pro or Gemini 4 Argon?
Gemini 3.1 Pro is 37.5% cheaper for input tokens ($1.25 vs. $2.00 per 1M tokens) and $10.00 vs. $10.00 for output tokens (1.6x cost advantage). In terms of operational performance, Gemini 3.1 Pro delivers lower response latency at 420ms TTFT (430ms faster than Gemini 4 Argon) with 98 vs. 115 tok/s throughput. Gemini 4 Argon leads coding evaluations at 84.5% SWE-bench Verified vs. 71.8% for Gemini 3.1 Pro and 93.8% vs. 82.4% 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 3.1 Pro
Gemini 4 Argon
Net Delta / Advantage
Input Cost / 1M Tokens
$1.25
$2.00
Gemini 3.1 Pro is 1.6x cheaper
Output Cost / 1M Tokens
$10.00
$10.00
Identical Output Pricing
Prompt Cache Read / 1M
$0.310
$0.100
Gemini 4 Argon is 3.1x cheaper
Prompt Cache Write / 1M
$1.25
$0.50
Gemini 4 Argon is 2.5x cheaper
Context Window (Max Output)
1,049k (66k out)
1,024k (1000k out)
Gemini 3.1 Pro (1.0x larger context)
Avg TTFT Response Latency
420ms
850ms
Gemini 3.1 Pro (430ms faster)
Generation Speed (Throughput)
98 tok/s
115 tok/s
Gemini 4 Argon (+17 tok/s faster)
SWE-bench Verified (Coding)
71.8%
84.5%
Gemini 4 Argon (+12.7% lead)
GPQA Diamond (PhD STEM)
82.4%
93.8%
Gemini 4 Argon (+11.4% lead)
MMLU-Pro (Multi-Discipline)
89.5%
91.4%
Gemini 4 Argon (+1.9% lead)
MATH-500 (Quantitative Logic)
96.8%
98.2%
Gemini 4 Argon (+1.4% lead)
Inference Value Score (IVR)
60 / 100
62.6 / 100
Gemini 4 Argon (+2.6 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 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M cached reads).
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 3.1 Pro or Gemini 4 Argon?
Gemini 3.1 Pro is cheaper at $1.25/1M input tokens ($10.00/1M 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 3.1 Pro or Gemini 4 Argon?
Gemini 3.1 Pro delivers faster first-token latency at 420ms TTFT (98 tok/s throughput) compared to 850ms TTFT (115 tok/s) for Gemini 4 Argon.
How do Gemini 3.1 Pro and Gemini 4 Argon compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.1 Pro scores 71.8% versus 84.5% for Gemini 4 Argon. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.1 Pro scores 82.4% versus 93.8% for Gemini 4 Argon (and 96.8% vs 98.2% on MATH-500).
When should you choose Gemini 3.1 Pro?
Choose Gemini 3.1 Pro (Google) for complex enterprise architecture planning, multi-file code refactoring, legal/financial corpus auditing, and multimodal research — offering a 1,048k context window, 420ms TTFT, and $1.25/1M input pricing ($0.310/1M cached reads).
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).
