DeepSeek

DeepSeek R1 has emerged as the leading open-source model for mathematical reasoning, outperforming many closed-source alternatives on MATH and GSM8K benchmarks.

Open source models have stabilized at ~30% market share, with DeepSeek leading at 14.37T tokens. Chinese OSS models grew from 1.2% to nearly 30% of total usage, reshaping competitive dynamics.

NVIDIA GB200 NVL72 with TRT-LLM Dynamo achieves 4x better TCO per million tokens than single-node servers for DeepSeek R1 at 30 tok/s/user. Rack-scale inference with disaggregated prefill, wide expert parallelism, and multi-token prediction (MTP) delivers 2-3x throughput gains.

xAI's Grok Code Fast 1 has surged to the #1 position on OpenRouter with 572.7B tokens processed weekly, more than 3x the second-place model. This dethroned mimo-v2-flash which dropped from #1 (170.9B) to #9 (77.6B), signaling a major shift toward specialized coding models.

Coding-optimized models now dominate OpenRouter's top 10: Grok Code Fast (#1), Claude Sonnet (#2), Claude Opus (#3), and Kwaipilot's kat-coder-pro-v1 (#4, 118.4B tokens). Mistral's Devstral 2512 (#8, 81.3B) adds to the coding focus. This reflects the broader industry shift where programming surpassed roleplay as the dominant LLM use case.

Labs like OpenAI and Anthropic claim RL reasoning scaling cannot be sustained beyond 1-2 years due to compute infrastructure limits, suggesting the exceptional 2024-2025 capability growth could slow.

DeepSeek V3 used 10x less compute than Llama 3 through MLA (multi-head latent attention), MoE innovations, and multi-token prediction, demonstrating 3x yearly algorithmic efficiency gains.

The best open models runnable on consumer GPUs lag frontier AI by only ~1 year across GPQA, MMLU, and LMArena benchmarks, suggesting rapid capability democratization and regulatory implications.