GoogleGemini 3.1 ProVSOpenAIGPT-5 mini
Our Take
We recommend Gemini 3.1 Pro if you need peak intelligence for reasoning and coding tasks, or the 6.5x cheaper GPT-5 mini to optimize your budget for high-volume pipelines. While Gemini 3.1 Pro holds a clear performance lead, it carries a heavy price premium. Choose Gemini 3.1 Pro for complex logic, or GPT-5 mini for budget efficiency.
▶WHY?
Benchmark Calculations & Evidence:
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Gemini 3.1 Pro scored 54.2%, while GPT-5 mini scored 45.7%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Gemini 3.1 Pro scored 94.3%, while GPT-5 mini scored 81.6%.
Cost Efficiency: GPT-5 mini pricing ($0.25/M input, $2/M output) is 6.5x cheaper than Gemini 3.1 Pro ($2/M input, $12/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)Winner (+8.5%)
54.2%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+12.7%)
94.3%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$4.50Input: $2.00 | Output: $12.00Context WindowLarger
1.05M tokensBenchmarks & Scores
Coding (swe-bench-pro)
45.7%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
81.6%graduate-level science QA
Cost & Context
Cost (per 1M tokens)6.5x cheaper
$0.69Input: $0.25 | Output: $2.00Context Window
400k tokensFrequently Asked Questions about Gemini 3.1 Pro vs GPT-5 mini
GPT-5 mini is cheaper than Gemini 3.1 Pro. GPT-5 mini has a blended cost of $0.69/1M tokens, which is about 6.5x cheaper than Gemini 3.1 Pro at $4.50/1M tokens.
Gemini 3.1 Pro is better for coding tasks on this benchmark. It scores 54.2% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to GPT-5 mini which scores 45.7%.
Related Matchups
Explore similar comparisons for Gemini 3.1 Pro and GPT-5 mini.
Do you want to find a model for your constraints?
Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.