Comprehensive side-by-side LLM comparison
Gemini 3.1 Pro leads with 17.7% higher average benchmark score. Gemini 3.1 Pro offers 849.9K more tokens in context window than Kimi K2.5. Kimi K2.5 is $6.50 cheaper per million tokens. Gemini 3.1 Pro supports multimodal inputs. Overall, Gemini 3.1 Pro is the stronger choice for coding tasks.
Google DeepMind
Gemini 3.1 Pro is a multimodal language model from Google DeepMind, released in preview in February 2026 as a point-version upgrade to Gemini 3 Pro focused on improving reasoning depth, factual grounding, and coding and agentic task performance. The model accepts text, images, video, audio, and PDFs as inputs across a 1M token context window, extending the multimodal breadth of the Gemini 3 series with a companion endpoint specifically optimized for custom tool use in agentic pipelines. Google describes it as built to refine the reliability and performance of the Gemini 3 Pro series, reflecting an incremental engineering iteration rather than an architectural overhaul.
Moonshot AI
Kimi K2.5, released by Moonshot AI in January 2026, is an updated Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters. It builds on Kimi K2 with improved coding performance across multiple languages and an expanded context window. Kimi K2.5 targets agentic development workflows, polyglot code generation, and open-source deployments requiring large-scale MoE reasoning.
1 month newer
Kimi K2.5
Moonshot AI
2026-01

Gemini 3.1 Pro
Google DeepMind
2026-02-19
Cost per million tokens (USD)
Gemini 3.1 Pro
Kimi K2.5
Context window and performance specifications
Average performance across 2 common benchmarks
Gemini 3.1 Pro
Kimi K2.5
Performance comparison across key benchmark categories
Gemini 3.1 Pro
Kimi K2.5
Gemini 3.1 Pro
2025-01
Available providers and their performance metrics
Gemini 3.1 Pro
Kimi K2.5
Gemini 3.1 Pro
Kimi K2.5
Gemini 3.1 Pro
Kimi K2.5
Moonshot AI