Every few weeks, a viral post claims AI is lazy because a model failed a trick question about feathers and steel. These posts are fine for engagement, but they are useless for building systems. Most of the time, the person is accidentally testing an old model name, an outdated wrapper, or a version that was deprecated a year ago. If you want a comparison that holds up for more than a day, you have to treat model selection like a dependency. You need to name the version, the interface, the task, and the constraints. Otherwise, you are just evaluating your own vague setup.
The current landscape is dominated by a few specific models that actually deliver. Google Gemini 3 Pro and OpenAI GPT-5.2 are the safest defaults for general work. They lead in reasoning, speed, and multimodal tasks. GPT-5.2 builds on the GPT-4o foundation with lower latency and broader enterprise adoption, while Gemini 3 Pro offers massive context windows up to one million tokens for document handling. If you are doing complex coding or advanced tool-calling, Anthropic Claude Opus 4.5 is the distinct leader. It handles summarization, factual accuracy, and 200K context tasks better than the competitors.
Relative strengths for late 2025. Proprietary models still hold the edge in reasoning.
Open source is a different story. GLM-4.7 is currently outperforming DeepSeek-V3.2 in reasoning and efficiency benchmarks. If you are looking for cost efficiency in API use, Z.ai is a superior choice. I have written before about how GLM-4.7 pushes harder on agents and tools. Meanwhile, Meta Llama 4 Maverick is a joke. It ranks poorly even among open-weights options and lacks the intelligence found in models from Google, OpenAI, or Anthropic. I do not recommend touching anything from Meta right now if you need reasoning depth. You can read more about why the packaging is the only thing Meta gets right in my post on Meta’s Manus bet.
Most comparison posts fail because they ignore how models are actually deployed. They test model names like GPT-4 when they actually mean a product UI that has been routed and filtered into a completely different experience. They also ignore context windows. Research tasks are often less about raw intelligence and more about whether the setup can feed the right data to the model. Finally, one trick question is not a benchmark. Real work covers a distribution of tasks like writing, extracting data, and error recovery. This is also why OpenAI naming habits make things difficult. I covered this in OpenAI’s Naming Nightmare.
If you are building a tech stack in 2025, pick two models. Use a general model like Gemini 3 Pro or GPT-5.2 for research and multimodal work. Pair it with Claude Opus 4.5 for engineering and agent execution. This combination ensures reliability where failures waste the most time. If you need to push costs down or require self-hosting, start with GLM-4.7. Stop arguing about feathers versus steel and start running test sets that match the work you actually do.
