Kimi K2.5 vs Gemini 3.1 Pro: Math Prodigy vs the Knowledge Generalist
Detailed comparison of Moonshot AI's Kimi K2.5 and Google DeepMind's Gemini 3.1 Pro - a trillion-parameter open MoE against Google's flagship multimodal model.
James Kowalski
AI Benchmarks & Tools Analyst
James is a software engineer turned tech writer who spent six years building backend systems at a fintech startup in Chicago before pivoting to full-time analysis of AI tools and infrastructure. His engineering background means he doesn't just read the spec sheet - he runs the benchmarks, profiles the latency, and checks whether the marketing claims hold up under real workloads.
He studied Computer Science at the University of Illinois at Urbana-Champaign, where he first got hooked on natural language processing during a senior research project on sentiment analysis. He later completed a certificate in data journalism from Northwestern's Medill School.
At Awesome Agents, James owns the leaderboards and tool comparison coverage. He maintains the site's benchmark tracking methodology and is the person who actually runs the numbers before publishing any ranking. He is also an open-source advocate and contributes to several projects in the LLM inference space.
Based in Chicago, IL.
Articles by James Kowalski
Kimi K2.5 vs Gemma 3 27B: Trillion-Parameter Frontier vs Google's Accessible Multimodal Model
Comparing Moonshot AI's 1T-parameter Kimi K2.5 with Google DeepMind's Gemma 3 27B - two multimodal open-weight models separated by 37x in parameter count but sharing a vision-first design philosophy.
Kimi K2.5 vs GLM-4.7-Flash: China's Open-Source Heavyweight vs the Free API Underdog
Comparing two Chinese AI models with MIT-family licenses - Moonshot AI's trillion-parameter Kimi K2.5 against Zhipu AI's ultra-efficient GLM-4.7-Flash that punches well above its weight on coding and agentic tasks.
Kimi K2.5 vs GPT-4o mini: Open-Weight Titan vs OpenAI's Budget Workhorse
Comparing Kimi K2.5 and GPT-4o mini - Moonshot AI's trillion-parameter frontier model with agent swarms against OpenAI's most widely deployed budget model.
Kimi K2.5 vs GPT-5.3 Codex: Open-Weight Swarm vs the Codex Juggernaut
Benchmark comparison of Kimi K2.5 and GPT-5.3 Codex - Moonshot AI's open-weight trillion-parameter MoE versus OpenAI's premium agentic coding model.
Kimi K2.5 vs Llama 4 Maverick: The Open MoE Heavyweights Go Head to Head
A detailed comparison of Kimi K2.5 and Llama 4 Maverick - two open-weight MoE models with radically different takes on the size, cost, and capability trade-off.
Kimi K2.5 vs Llama 4 Scout: Benchmark King Meets Context King
Comparing Kimi K2.5 and Llama 4 Scout - Moonshot AI's benchmark-crushing trillion-parameter model versus Meta's 10-million-token context window specialist.
Kimi K2.5 vs MiniMax M2.5: Chinese MoE Rivals Battle for the Coding Crown
Kimi K2.5 and MiniMax M2.5 compared side by side - two Chinese MoE models where the smaller, cheaper one actually wins on SWE-bench. A detailed analysis of when each model delivers more value.
Kimi K2.5 vs Mistral Large 3: Moonshot's MoE Titan vs Europe's Open-Weight Flagship
Comparison of Kimi K2.5 and Mistral Large 3 - two large open-weight MoE models with 256K context, each representing a different vision for open AI.
Kimi K2.5 vs Mistral Small 3.2: Frontier Agent Swarm vs Europe's Tool-Use Specialist
Comparing Kimi K2.5 and Mistral Small 3.2 - Moonshot AI's trillion-parameter open-weight frontier model against Mistral's compact, EU-compliant function calling specialist.
Kimi K2.5 vs Nemotron 3 Nano 30B-A3B: Benchmark King vs the Throughput Machine
Comparing Moonshot AI's trillion-parameter Kimi K2.5 with NVIDIA's Mamba2-MoE hybrid Nemotron 3 Nano 30B-A3B - frontier intelligence versus a model engineered for maximum throughput, 1M context, and 10x lower cost.
Kimi K2.5 vs Phi-4: Mainframe Intelligence vs the Best Pocket Reasoning Model
A detailed comparison of Moonshot AI's 1T-parameter Kimi K2.5 against Microsoft's 14B Phi-4 - the most extreme size gap in frontier AI, with 71x the parameters but vastly different use cases.
