In 2023–2024 the race for the biggest model seemed unstoppable: 70B → 175B → 405B → 2T parameters became the main metric of prestige. By mid-2026 the situation has changed dramatically.The era of "the bigger — the better" is rapidly coming to an end. Here's what the leading laboratories and companies are actually doing right now.Main trends of 2026Sharp decline in the average size of frontier models
Most new models that show SOTA results or come close to it have 15–120 billion parameters.
The sweet spot right now: 30–70B for general-purpose models and 8–25B for specialized ones.
Post-training becomes more important than pre-training
The cost of quality improvement has shifted:

pre-training 1 token ≈ $0.4–0.8 per million tokens 
high-quality synthetic data + RL + advanced post-training ≈ $4–12 per million tokens

Result: companies prefer to spend 5–8× more compute on post-training than on initial pre-training.Test-time scaling is replacing parameter scaling
Techniques that are actively used in production right now:

Best-of-N sampling 
Process supervision & self-verification 
Monte-Carlo Tree Search variants 
Recursive self-improvement chains 
Tool-use + long-context routing 
Dynamic inference-time compute allocation

Many models with 20–40B parameters using these methods regularly outperform base models of 300–500B on hard benchmarks.Specialization wins over universal giants
In 2026 the market structure looks like this:

3–5 truly general frontier models (70–200B) 
40–70 strong specialized models in different domains (8–70B) 
hundreds of ultra-specialized small models (1–15B)

The biggest performance/price gains are currently shown by models in the 15–40B range, optimized for concrete tasks.New architectural paradigms are gaining weight
Mixture-of-Experts remains dominant, but new interesting families appeared:

StripedHyena / Mamba-2 hybrids 
Griffin-style recurrent + attention blocks 
BitNet b1.58 and ternary/quantized transformers 
RWKV-7 / xLSTM derivatives 
Liquid neural networks & continuous-time models

Many of them achieve comparable quality to classic transformers while using 30–70% less memory and inference compute.Quick verdict for 2026Giant models (500B+) are not dead yet.
They still hold the absolute top on the most difficult tasks (especially long reasoning chains and multimodal).
But the difference between 70B and 1T+ has become much smaller than the difference between 7B and 70B two years ago.The main question of 2026–2027 is no longer "how many parameters?", but:
How efficiently can the model use compute at inference time?
How good is its post-training?
How well is it specialized for the real task?It seems that 2026 became the year when scaling laws finally started to bend — and the center of gravity of progress decisively shifted from size to intelligence engineering.