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GLM-5.3

Alibaba Cloud Model Studio (Bailian) · Zhipu (direct) · Flagship reasoning, Strong tool calling

Text Flagship reasoningStrong tool calling

GLM-5.3 is Zhipu's flagship reasoning model, served on Alibaba Cloud Model Studio (Bailian). It is a text model with strong tool-calling support and a 1024K context window, priced per 1M tokens in CNY.

API pricing (official list price, CNY per 1M tokens)

Input (cache miss)¥8
Output¥28
Cache hit¥2
Time-based pricing Single price around the clock
Billing currencyCNY (Chinese yuan) — the vendor's original billing currency, not converted

Specifications

Context window1024K tokens
ModalitiesText
Noted featuresFlagship reasoning, Strong tool calling
Vendor / channelAlibaba Cloud Model Studio (Bailian) · Zhipu (direct)
Price sourceAlibaba Cloud Model Studio — GLM-5.3 model page
Verified2026-10-28

Site calculation: estimated monthly cost at default usage

Site calculation, not a vendor quote. Assumptions: 100 calls/day · 2000 input / 800 output tokens per call · 30-day month · 30% cache hit rate. Same formula as the cost calculator.

Effective input price30% × ¥2 (cache hit) + 70% × ¥8 (standard) = 6.2 CNY/1M tokens
Input cost100 × 30 × (2000 ÷ 1,000,000) × 6.2 = ¥37.20
Output cost100 × 30 × (800 ÷ 1,000,000) × ¥28 = ¥67.20
Estimated monthly cost¥104.40

Notes and limitations

· Prices are official published list prices per 1M tokens in the original billing currency (here CNY). Limited-time promotions, resource packs and subscription discounts are not included; the vendor's console prevails.

· "Verified" is the date this page's figures were last checked against the official source linked above. Pricing changes over time — confirm on the source page before making decisions.

· The monthly cost above is this site's own calculation under fixed usage assumptions. It is not a vendor quote and does not constitute purchase advice.

· This page records no benchmark results and claims no leadership on any task. Editor's note: this page records official pricing and specifications only and makes no quality or performance ranking claims. Evaluate output quality on your own workloads before committing.

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