LLM Wrapped

2025

A data-driven look at language model development and deployment across the year.

The Enterprise Shift

While industry discourse proclaimed 2025 "the year of the agent," actual deployment tells a more measured story.

The gap between experimentation and production-scale deployment remained significant.

Frontier Model Evolution

2025 saw continued investment in large-scale models

First Measurable Killer App

Code generation emerged as AI's first demonstrable commercial success.

This marks the first clear productization beyond general chatbot interfaces.

From Instant to Iterative

2025 marked a shift from single-pass inference to multi-step reasoning architectures.

• Chain-of-thought prompting

  Moved from research to production

• Extended thinking modes

  16K-64K token reasoning chains

• Self-verification loops

  Reflection and iterative improvement

• Production models

  OpenAI o1 series, Gemini Deep Think, Claude's thinking mode

Improved performance on complex problem-solving at the cost of increased latency and compute.

Beyond Text Processing

Native multimodal processing became standard across frontier models.

• Vision: Image understanding, chart/diagram analysis
• Audio: Voice mode with conversational latency
• Video: Processing hours of video content (MMCTAgent architecture)
• Cross-modal: Unified input/output across text, image, audio, video

The Open Weight Movement

Open source models continued closing the capability gap with proprietary systems.

• Meta Llama 4 - Scout: 10M tokens, Maverick: multimodal
• DeepSeek R1 - Reasoning models
• Mistral Mixtral 8x22B - MoE architecture
• OpenAI GPT-OSS - First open-weight release from OpenAI

Scaling Input Capacity

Context windows grew by orders of magnitude

This enables processing entire codebases, lengthy documents, and multi-session conversations without summarization loss.

Deployment Realities

McKinsey survey data reveals uneven progress

Transition from proof-of-concept to enterprise-wide deployment continues to be the primary bottleneck.

Economics of Model Training

Training costs showed significant variance

Architecture choices (MoE, efficient attention) can dramatically reduce training economics while maintaining capability.

Standardization Efforts

2025 saw the emergence of protocols for agent interoperability

These represent infrastructure necessary for production agentic systems.

Persistent Issues

Despite advances, fundamental challenges remain

Text-to-Video Progress

Video generation moved from research demos to production tools

• OpenAI Sora - General release
• Google Veo - Integrated with Gemini
• Runway Gen-2 - Commercial deployment
• Kling O1 - Unified multimodal creation, solved character consistency

60-second 4K generation, improved temporal consistency, character/scene persistence across frames.

Conversational AI Audio

Voice capabilities advanced beyond text-to-speech

• Real-time conversational latency - Sub-second response
• Emotional tone modulation - Natural expression
• Mid-utterance interruption - Human-like interaction
• Multi-turn conversation - Context retention

Applications: Customer support automation, accessibility tools, language learning

Efficiency-Focused Development

2025 saw increased focus on smaller, specialized models

• On-device models - Phones, laptops (Gemma 3n, Microsoft Mu)
• NPU-optimized inference - Edge computing
• Privacy-preserving - Local deployment
• Domain-specific - Fine-tuning

A counter-trend to "bigger is better," driven by edge computing, privacy requirements, and cost optimization.

Performance Measurement

New benchmarks emerged to test advanced capabilities

• Humanity's Last Exam - Reasoning under open-ended conditions
• GPQA Diamond - Complex question accuracy
• SWE-bench - Software engineering problem-solving
• Video-MME - Multimodal video understanding

As models saturate traditional benchmarks, evaluation frameworks continue evolving to test emerging capabilities.

Hype Calibration

Industry predictions showed typical technology adoption patterns

Gartner positioned AI agents at "Peak of Inflated Expectations" - Classic Gartner Hype Cycle dynamics in action.

Embodied Intelligence

2025 saw early work on robotics integration

• Google Gemini Robotics On-Device - VLA models
• Vision-language-action models - Optimized for edge deployment
• Real-world sensor data - LIDAR, GPS, video integration

This represents a research direction rather than deployed capability, but signals the next frontier beyond purely digital agents.

What 2025 Demonstrated

2026 Outlook

Based on current trajectories

The industry remains in rapid evolution with uncertain convergence points.

My small take: it's not clear that the industry will converge on a single model or architecture. The most likely outcome is a continued diversity of models and architectures, each with its own strengths and weaknesses. This is a good thing, as it will allow for more experimentation and innovation. However, it will also make it more difficult to compare models and architectures, and to understand the trade-offs between them. This is why we need more benchmarks and more standardized evaluation frameworks.