[R] Benchmarked 94 LLM endpoints for jan 2026. open source is now within 5 quality points of proprietary

A comprehensive benchmark analysis of 94 large language model endpoints for January 2026 reveals a dramatic acceleration in open-source AI capabilities, with the performance gap to proprietary leaders shrinking to just 5 quality points. Using a Quality Index (QI) derived from benchmarks like AIME 2025, LiveCodeBench, and τ²-Bench, the data shows top open-source models—GLM-4.7 (68 QI), Kimi K2 Thinking (67 QI), and MiMo-V2-Flash (66 QI)—now approaching the performance tier of proprietary giants like GPT-5.2 and Gemini 3 Pro (both 73 QI). Notably, on agentic tasks, open-source models have already pulled ahead, signaling a shift in the competitive landscape driven by rapid community and corporate development outside major AI labs.

The most compelling story emerges in the cost-to-performance ratio, where open-source models deliver staggering value. DeepSeek V3.2, at 66 QI, is available for $0.30 per million tokens via DeepInfra, compared to GPT-5.1's $3.50/M for a 70 QI score—an 85% cost reduction for a minimal 4-point QI difference. This narrows a gap that was 12 points wide in early 2025, fundamentally changing the calculus for production deployment. While proprietary models still hold advantages in specific areas like GPQA Diamond reasoning and 1M+ context windows (Gemini), the benchmark suggests that for many real-world workloads, the output quality difference has become negligible, forcing a strategic reevaluation of model selection based on economics rather than peak capability.