GoogleGemini 3.1 ProVSMoonshot AI (Kimi)kimi-k2.5
Our Take
We recommend Gemini 3.1 Pro if you need peak intelligence for reasoning and coding tasks, or the 3.8x cheaper kimi-k2.5 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 kimi-k2.5 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 kimi-k2.5 scored 50.7%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Gemini 3.1 Pro scored 94.3%, while kimi-k2.5 scored 87.6%.
Cost Efficiency: kimi-k2.5 pricing ($0.6/M input, $3/M output) is 3.8x cheaper than Gemini 3.1 Pro ($2/M input, $12/M output).
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Benchmarks & Scores
Coding (swe-bench-pro)Winner (+3.5%)
54.2%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+6.7%)
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)
50.7%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
87.6%graduate-level science QA
Cost & Context
Cost (per 1M tokens)3.8x cheaper
$1.20Input: $0.60 | Output: $3.00Context Window
262.14k tokensFrequently Asked Questions about Gemini 3.1 Pro vs kimi-k2.5
kimi-k2.5 is cheaper than Gemini 3.1 Pro. kimi-k2.5 has a blended cost of $1.20/1M tokens, which is about 3.8x 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 kimi-k2.5 which scores 50.7%.
Related Matchups
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