You’ve undoubtedly already observed that “using AI” in 2025 entails more than merely conversing with a model. Now that we’ve firmly entered the agentic AI era, LLMs do more than just respond to your questions; they also reason with you, plan for you, take action, use tools, use APIs, search the web, schedule things, and function as completely independent assistants. 2025 belongs to the agent if 2023–2024 belonged to the “chatbot.” Allow me to guide you through the models that are most effective when creating AI agents.
1. o1/o1-mini OpenAI
With OpenAI’s o1/o1-mini, you’ll see the difference right away when working on deep-reasoning robots. These models continue to rank among the best for multi-step tool use, mathematical reasoning, step-by-step thinking, and meticulous planning. Any organized process you run will show that o1 is near the top for decomposition stability, API reliability, and action accuracy, according to the Agent Leaderboard. Yes, it is more costly, slower, and occasionally overthinks basic tasks, but if your agent requires precision and careful reasoning, o1’s benchmark results more than make up for the expense. The OpenAI documentation offers further exploration opportunities.
2. Flash Thinking On Google Gemini 2.0
Gemini 2.0 Flash Thinking is where you’ll truly see a difference if speed is your goal. Because it combines high multimodality with quick reasoning, it dominates real-time use cases. Gemini Flash consistently ranks close to the top of the StackBench leaderboard for multimodal performance and quick tool execution. This model manages your agent’s transitions between text, graphics, video, and audio with ease. Gemini Flash is among the greatest choices available when you require responsiveness and engagement, but it is not as good as O1 for deep technical reasoning, and lengthy tasks can exhibit accuracy dips. The Gemini documentation is available at ai.google.dev.
3. The Open-Source Kimi’s K2
When you run agentic tasks on K2, you’ll understand why it’s the open-source surprise of 2025. K2 is the open-source model with the greatest scores for Action Completion and Tool Selection Quality, according to the Agent Leaderboard v2. It is rapidly emerging as a leading substitute for Llama for self-hosted and research agents due to its exceptional long-context reasoning capabilities. K2 is one of the most significant open-source arrivals this year, as evidenced by its leaderboard performance. Its primary disadvantages are the high memory requirements and the fact that its ecosystem is still expanding.
4. Open-Source DeepSeek V3/R1
Developers who desire powerful reasoning at a fraction of the price have taken to DeepSeek models. In structured reasoning tasks, DeepSeek V3 and R1 perform competitively with top-tier proprietary models on the StackBench LLM Leaderboard. You will recognize their cost-effectiveness if you intend to implement long-context workflows or huge agent fleets. However, remember that the ecosystem is still catching up, its safety filters are weaker, and dependability can falter in extremely intricate thought processes. When price and size are more important than flawless accuracy, they are ideal. The documentation for DeepSeek may be found at api-docs.deepseek.com.
5. Open-Source Meta Llama 3.1/3.2
You’ve undoubtedly previously encountered Llama 3.1 and 3.2 if you’re developing agents locally or privately. These models continue to be the foundation of the open-source agent community due to their adaptability, efficiency, and seamless integration with frameworks such as AutoGen, OpenHands, and LangChain. Llama routinely scores highly on organized tasks and tool dependability on open-source leaderboards like the Hugging Face Agent Arena. However, you should be aware that in terms of long-term planning and mathematical thinking, it still lags behind models like Claude and O1. Your performance is also greatly influenced by the GPUs and tweaks you’re using because it’s self-hosted. The official documentation is available at llama.meta.com/docs.
Concluding
The idea of agentic AI is no longer futuristic. It’s quick, it’s here, and it’s changing the way we operate. These LLMs are the brains behind the emerging generation of intelligent agents, which includes research copilots, personal assistants, and enterprise automation.