💻 Coding (SWE) WinnerGPT-6.1 Sol 86.4% SWE-bench · 96.8% HumanEval
🧠 STEM Reasoning WinnerGPT-6.1 Sol 92.4% GPQA · 98.8% MATH-500
Which model is better: Gemini 3.1 Pro or GPT-6.1 Sol?
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 (100ms faster than GPT-6.1 Sol) with 98 vs. 135 tok/s throughput. GPT-6.1 Sol leads coding evaluations at 86.4% SWE-bench Verified vs. 71.8% for Gemini 3.1 Pro and 92.4% 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
GPT-6.1 Sol
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
GPT-6.1 Sol is 3.1x cheaper
Prompt Cache Write / 1M
$1.25
$2.00
Gemini 3.1 Pro is 1.6x cheaper
Context Window (Max Output)
1,049k (66k out)
1,075k (128k out)
GPT-6.1 Sol (1.0x larger context)
Avg TTFT Response Latency
420ms
520ms
Gemini 3.1 Pro (100ms faster)
Generation Speed (Throughput)
98 tok/s
135 tok/s
GPT-6.1 Sol (+37 tok/s faster)
SWE-bench Verified (Coding)
71.8%
86.4%
GPT-6.1 Sol (+14.6% lead)
GPQA Diamond (PhD STEM)
82.4%
92.4%
GPT-6.1 Sol (+10.0% lead)
MMLU-Pro (Multi-Discipline)
89.5%
91%
GPT-6.1 Sol (+1.5% lead)
MATH-500 (Quantitative Logic)
96.8%
98.8%
GPT-6.1 Sol (+2.0% lead)
Inference Value Score (IVR)
60 / 100
62.9 / 100
GPT-6.1 Sol (+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 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 GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
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 GPT-6.1 Sol.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.1 Pro or GPT-6.1 Sol?
Gemini 3.1 Pro delivers faster first-token latency at 420ms TTFT (98 tok/s throughput) compared to 520ms TTFT (135 tok/s) for GPT-6.1 Sol.
How do Gemini 3.1 Pro and GPT-6.1 Sol compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.1 Pro scores 71.8% versus 86.4% for GPT-6.1 Sol. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.1 Pro scores 82.4% versus 92.4% for GPT-6.1 Sol (and 96.8% vs 98.8% 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 GPT-6.1 Sol?
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
