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Context windows have expanded rapidly, but production deployment at enterprise scale faces latency, cost, and reliability barriers that benchmarks do not capture.
True if 3+ Fortune 500 companies publicly report using AI systems with 500K+ token context windows in production workflows. Public statements, case studies, or earnings call references qualify.
Kimi K3 2.8T with 1M-token context now available for enterprise self-hosting under Modified MIT license
Five frontier models now at 1M+ context with competitive pricing
DeepSeek V4 Pro 1M token context at 1/100th cost
Claude Opus 5 1M-token context at $5/M input — production pricing removes cost barrier
Google Gemini Enterprise Agent Platform creates procurement infrastructure for large-context workloads
Llama 4 Scout at 10M token context — open-weight option extends context frontier
Inference pricing fell 88% from March 2023 levels — cost barrier further compressed
GPT-5.6 Luna now at $0.20/M input tokens — additional 80% price cut further removes cost barrier for enterprise experimentation
LG AI Research K-EXAONE 2.0 with 262K-token context under Apache 2.0 expands accessible long-context model supply
Latency concerns at million-token scale remain even with efficiency gains
Most enterprise workflows do not need million-token windows — agent-based chunked context preferred; Meituan's VitaBench incident dataset (Story 14) shows agent workflows prefer chunked approaches
Forecast requires 'Fortune 500 production use' — no Fortune 500 customer confirmed using million-token context in production today
Kimi K3 full weights require 1.4TB memory in MXFP4 — open-weight enterprise deployment still constrained
241 days remaining under MODERATE pressure with no qualifying Fortune 500 production deployment confirmed
Retrieval-augmented approaches remain cheaper for most use cases even at lower model pricing
Even with dramatic price compression, no enterprise announcement specifically calls out million-token context as production-deployed capability