University of Kwazulu-Natal

The State Of Llms December 2025 What Llm

Bonisiwe Shabane
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the state of llms december 2025 what llm

The benchmarks that defined progress are now meaningless. The models everyone relies on cost 30x what the alternatives do. And nobody agrees on what to measure anymore. Math is solved. Agentic coding is not. The gap between what models can memorize and what they can do has never been wider.

Three models sit at the top: Gemini 3 Pro Preview at 73 on the Artificial Analysis Intelligence Index, GPT-5.1 and Claude Opus 4.5 tied at 70. This ordering has been stable for months. Google, OpenAI, and Anthropic take turns announcing improvements, benchmark scores tick up a point or two, and nothing fundamentally changes at the summit. The real movement is happening below. In the 60-67 range, open-weight models from Chinese labs are stacking up fast. DeepSeek V3.2 landed at 66 this week.

Kimi K2 Thinking holds 67. These aren't research previews or experimental checkpoints. They're production-ready models with MIT licenses, priced at a fraction of what the leaders charge. Here's the comparison that should concern every AI product manager: As 2025 comes to a close, I want to look back at some of the year’s most important developments in large language models, reflect on the limitations and open problems that remain, and share... As I tend to say every year, 2025 was a very eventful year for LLMs and AI, and this year, there was no sign of progress saturating or slowing down.

There are many interesting topics I want to cover, but let’s start chronologically in January 2025. ​Scaling still worked, but it didn’t really change how LLMs behaved or felt in practice (the only exception to that was OpenAI’s freshly released o1, which added reasoning traces). So, when DeepSeek released their R1 paper in January 2025, which showed that reasoning-like behavior can be developed with reinforcement learning, it was a really big deal. (Reasoning, in the context of LLMs, means that the model explains its answer, and this explanation itself often leads to improved answer accuracy.) DeepSeek R1 got a lot of attention for various reasons: As we close December 2025, multiple in-depth industry reports highlight a clear trend: the new generation of Large Language Models is no longer judged only by raw power but by adaptability, multimodal capability, deployment...

Articles from Prismetric, Backlinko, Shakudo, CodeDesign, TechRadar, Business Insider, and others converge on a consistent narrative—GPT-5 stands out as the most powerful general-purpose model, dominating reasoning, coding, research tasks, and long-context workflows, making it... At the same time, enterprise evaluations emphasize that Claude Opus and Claude Sonnet remain unmatched in stability, safe reasoning, long-form content quality, and consistent code generation, making them ideal for businesses prioritizing reliability over... On the multimodal side, Google’s Gemini 2.5 Pro receives major attention as the most capable engine for seamlessly integrating text, images, documents, maps, and structured datasets—giving it an edge in domains like education, digital... The open-source ecosystem also continues to accelerate. Reviews from Shakudo and Prismetric highlight Meta’s Llama 4 family—Scout and Maverick—as the strongest deploy-anywhere models, offering impressive performance, fine-tuning freedom, and lower operational cost compared to proprietary systems. Meanwhile, the DeepSeek V-series earns global recognition for delivering near–GPT-5-level efficiency at a fraction of the training overhead, challenging assumptions about what large-scale AI development truly requires.

Overall, the 2025 LLM landscape is more diverse than ever. No single model is universally “best.” Instead, each model has evolved into a specialized tool: GPT-5 leads in frontier reasoning and general-purpose intelligence; Claude excels in structured enterprise workflows and production-grade coding; Gemini dominates... Collectively, these advancements show that choosing the right LLM in 2025 is less about picking the strongest engine and more about aligning capabilities with your product needs, infrastructure, and long-term AI strategy. This shift marks an important moment in AI development—one where the future belongs not just to the most powerful models, but to the most adaptable. This is the third in my annual series reviewing everything that happened in the LLM space over the past 12 months. For previous years see Stuff we figured out about AI in 2023 and Things we learned about LLMs in 2024.

It’s been a year filled with a lot of different trends. OpenAI kicked off the “reasoning” aka inference-scaling aka Reinforcement Learning from Verifiable Rewards (RLVR) revolution in September 2024 with o1 and o1-mini. They doubled down on that with o3, o3-mini and o4-mini in the opening months of 2025 and reasoning has since become a signature feature of models from nearly every other major AI lab. My favourite explanation of the significance of this trick comes from Andrej Karpathy: By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like “reasoning” to humans—they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going...

[...] Autonomous Multi-Agent Platform in Your Cloud Connect Scattered Data Into Clear Insight Automate Repetitive Tasks and Data Flows Deploy Context-Aware AI Applications at Scale Interact with Your Data using Natural Language

An in-depth analysis of the most significant large language model releases and developments from the past six months, exploring performance, capabilities, and industry trends. The large language model (LLM) landscape has undergone remarkable transformation in recent months, with over 30 significant model releases fundamentally reshaping the AI development ecosystem. This comprehensive review examines the most impactful developments, from breakthrough open-source models to enterprise-grade solutions that are redefining what's possible in artificial intelligence. Traditional benchmarks and leaderboards have become increasingly unreliable for assessing real-world model performance. The abundance of numerical metrics often obscures practical capabilities, leading developers to seek alternative evaluation methods. One particularly creative approach involves testing models' ability to generate SVG code for complex visual scenarios—a task that combines coding proficiency, spatial reasoning, and creative problem-solving.

This evaluation method proves especially revealing because it requires models to handle multiple challenging aspects simultaneously: generating valid code syntax, understanding geometric relationships, and reasoning about impossible scenarios. The results provide insights that traditional benchmarks often miss. Amazon's Nova model family marked a significant milestone in cloud-based AI services. These models offer competitive performance with million-token context windows while maintaining remarkably low pricing structures. Nova Micro has established itself as one of the most cost-effective options available, making advanced AI capabilities accessible to smaller organizations and individual developers.

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