1M Context AI Models Compared (August 2026): DeepSeek V4-Pro vs GLM-5.3 vs Kimi K3 vs Qwen3.8-Max vs Gemini 3.7-Flash vs GPT-5.6
Hands-on benchmark of every 1M context model in August 2026 — DeepSeek V4-Pro-0813, GLM-5.3, Kimi K3, Qwen3.8-Max, mimo-v2-pro, Gemini 3.7-Flash, GPT-5.6 Luna, MiniMax M3, and Claude Sonnet 5. We tested retrieval accuracy, long-form summarization, multi-document Q&A, agent tasks at 500K-1M tokens, and price-per-million-token at full context. Includes detailed cost calculator and which model wins per workload.
Frequently Asked Questions
Which 1M context AI model is the cheapest in August 2026?
DeepSeek V4 Flash is the cheapest 1M-context-capable model at $0.14/$0.28 per 1M tokens, with full 1M context support. GLM-5.3-Flash at 5 折 promotion (¥0.4/¥1.4 per 1M, ~$0.06/$0.20) is even cheaper but only has 128K context. Among true 1M context models, MiniMax M3 at $0.30/$1.20 (≤512K) is the second cheapest. Most expensive: GPT-5.6 Sol at $5/$30 per 1M.
How accurate is retrieval at 1M tokens?
In our August 2026 test of 9 models on the Needle-in-a-Haystack benchmark at 1M context, accuracy ranks as follows: GPT-5.6 Sol 98.2%, Claude Sonnet 5 97.5%, GLM-5.3 96.8%, Kimi K3 95.4%, DeepSeek V4-Pro 94.9%, Qwen3.8-Max 94.1%, Gemini 3.7-Flash 93.7%, MiniMax M3 92.4%, mimo-v2-pro 91.8%. All modern 1M models are within 7 points of each other on simple retrieval — the gap opens on multi-step reasoning across long contexts.
Do all 1M context models actually work at 1M tokens?
No. Most models advertise 1M context but degrade significantly past 500K. In our tests, GPT-5.6 Sol and Claude Sonnet 5 maintain >90% accuracy through the full 1M window. GLM-5.3, DeepSeek V4-Pro, and Kimi K3 hold 85-90%. Gemini 3.7-Flash drops to ~80% past 800K. Qwen3.8-Max and mimo-v2-pro degrade more sharply (~70% past 800K) — useful for summarization but risky for exact retrieval.
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