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gemini-3.1-pro-preview vs kimi-k2.5

Side-by-side comparison of gemini-3.1-pro-preview and kimi-k2.5: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-3.1-pro-preview outperforms kimi-k2.5 on 5 of 6 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
gemini-3.1-pro-preview36.7%
kimi-k2.527.6%
Coding Indexcoding
gemini-3.1-pro-preview68.8%
kimi-k2.546.8%
GPQA Diamondreasoning
gemini-3.1-pro-preview94.1%
kimi-k2.587.9%
Terminal-Bench Hardagentic
gemini-3.1-pro-preview53.8%
kimi-k2.534.8%
τ²-Benchagentic
gemini-3.1-pro-preview95.6%
kimi-k2.595.9%
SciCodecoding
gemini-3.1-pro-preview58.7%
kimi-k2.5N/A
Humanity's Last Examreasoning
gemini-3.1-pro-preview47.0%
kimi-k2.530.7%

Scores sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and don't capture every aspect of model quality.

Pricing & specifications

gemini-3.1-pro-previewkimi-k2.5
Input price / 1M$2.00$0.60
Output price / 1M$12.00$3.00
Context window1.0M tokens262K tokens
Max output66K tokens262K tokens
Vision inputYesYes
Tool callingYesYes
ReasoningYesYes
Prompt cachingYesYes
Computer useN/AN/A
ProviderGoogle LLC (Gemini API)Moonshot AI

Questions people ask

Is gemini-3.1-pro-preview better than kimi-k2.5?
gemini-3.1-pro-preview outperforms kimi-k2.5 on 5 of 6 shared benchmarks. See the benchmark comparison above for specifics: gemini-3.1-pro-preview and kimi-k2.5 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, gemini-3.1-pro-preview or kimi-k2.5?
kimi-k2.5 is cheaper. gemini-3.1-pro-preview costs $2.00/$12.00 per 1M input/output tokens, while kimi-k2.5 costs $0.60/$3.00.
Can I use gemini-3.1-pro-preview and kimi-k2.5 through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "google/gemini-3.1-pro-preview" and "moonshot/kimi-k2.5", no other code changes needed.
What are the context windows?
gemini-3.1-pro-preview supports up to 1.0M tokens of context. kimi-k2.5 supports up to 262K tokens. Longer context means you can feed larger documents or codebases in a single prompt, though quality often degrades past 128K for most models.

Switch between gemini-3.1-pro-preview and kimi-k2.5 with one line of code

Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.