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Real human-preference Elo from LMArena blind head-to-head votes. Higher is better; — means not yet ranked in that arena. This is measured, not our estimate.
Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-05-11 · Source: zhipu_bigmodel_official_reference
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing com...
glm-4.7 is a Text model from Zhipu AI (GLM) (CN). HotON.ai tracks it at $0.39 per 1M input tokens and $1.75 per 1M output tokens, with a 203K-token context window. Its composite efficiency score is 89/100 at an estimated $0.002 per successful task.
glm-4.7 is tracked at $0.39 per 1M input tokens and $1.75 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $1.41 per 1M tokens. Figures are illustrative demo data.
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
glm-4.7 supports up to a 203K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, glm-4.7 is cheaper than 49% of models on input price and ranks #220 of 535 by overall efficiency.
Yes — minimax-m2.1 is a lower-cost option at $0.95 per 1M output tokens, while still covering similar Text use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — glm-4.7 (Zhipu AI (GLM)): $0.39/1M input, $1.75/1M output, as of 2026-05-11. https://hoton.ai/en/models/z-ai-glm-4-7Pricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.