Which model is better: Gemini 3.1 Pro or OpenAI o3-mini?
OpenAI o3-mini is 12.0% cheaper for input tokens ($1.10 vs. $1.25 per 1M tokens) and $4.40 vs. $10.00 for output tokens (1.1x cost advantage). In terms of operational performance, Gemini 3.1 Pro delivers lower response latency at 420ms TTFT (530ms faster than OpenAI o3-mini) with 98 vs. 98 tok/s throughput. Gemini 3.1 Pro leads coding evaluations at 71.8% SWE-bench Verified vs. 49.3% for OpenAI o3-mini and 82.4% 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 3.1 Pro
OpenAI o3-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$1.25
$1.10
OpenAI o3-mini is 1.1x cheaper
Output Cost / 1M Tokens
$10.00
$4.40
OpenAI o3-mini (56.0% lower)
Prompt Cache Read / 1M
$0.310
$0.550
Gemini 3.1 Pro is 1.8x cheaper
Prompt Cache Write / 1M
$1.25
$1.10
OpenAI o3-mini is 1.1x cheaper
Context Window (Max Output)
1,049k (66k out)
205k (100k out)
Gemini 3.1 Pro (5.1x larger context)
Avg TTFT Response Latency
420ms
950ms
Gemini 3.1 Pro (530ms faster)
Generation Speed (Throughput)
98 tok/s
98 tok/s
Equal Throughput
SWE-bench Verified (Coding)
71.8%
49.3%
Gemini 3.1 Pro (+22.5% lead)
GPQA Diamond (PhD STEM)
82.4%
79.7%
Gemini 3.1 Pro (+2.7% lead)
MMLU-Pro (Multi-Discipline)
89.5%
83.2%
Gemini 3.1 Pro (+6.3% lead)
MATH-500 (Quantitative Logic)
96.8%
97.9%
OpenAI o3-mini (+1.1% lead)
Inference Value Score (IVR)
60 / 100
59.7 / 100
Gemini 3.1 Pro (+0.3 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 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 3.1 Pro or OpenAI o3-mini?
OpenAI o3-mini is cheaper at $1.10/1M input tokens ($4.40/1M output) compared to $1.25/1M input ($10.00/1M output) for Gemini 3.1 Pro.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.1 Pro or OpenAI o3-mini?
Gemini 3.1 Pro delivers faster first-token latency at 420ms TTFT (98 tok/s throughput) compared to 950ms TTFT (98 tok/s) for OpenAI o3-mini.
How do Gemini 3.1 Pro and OpenAI o3-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.1 Pro scores 71.8% versus 49.3% for OpenAI o3-mini. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.1 Pro scores 82.4% versus 79.7% for OpenAI o3-mini (and 96.8% vs 97.9% 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 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).
