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GLM-5.3 Pricing

Zhipu AI · Released August 28, 2026. GLM-5.3 keeps the GLM-5.2 744B MoE base and applies pu

TL;DR
Price: $1.11/1M input · $3.89/1M output
Context: 1M · max 131,072 output
Cost advantage: Up to 70% cheaper than official API
Access: OpenAI-compatible · No credit card · Instant API key

Save up to 70% on GLM-5.3 API cost

Same model, same quality — pay less per token than the official API. Pay-as-you-go, no credit card required.

$1.11/1M
input token price
INPUT
$1.11
/1M tokens
OUTPUT
$3.89
/1M tokens
CONTEXT
1M

Other Zhipu AI Models

GLM-5.3-Flash
$0.11 / $0.39
View Pricing
GLM-5.2
$1.40 / $4.40
View Pricing
GLM-5.1
$0.85 / $3.40
View Pricing
GLM-5-Turbo
$0.70 / $3.10
View Pricing
GLM-4.5-Air
$0.11 / $0.28
View Pricing
GLM-4.7 Flash
$0.00 / $0.00
View Pricing

GLM-5.3 Pricing Context

Pricing position within Zhipu AI

GLM-5.3 sits in the middle of Zhipu AI's pricing at $1.11/1M input — 82% above the lineup average ($0.00 cheapest, $1.40 most expensive). 5 siblings cost less, 1 cost more. This mid-tier positioning makes it a sensible default when you're unsure which variant to pick.

When GLM-5.3 is worth the cost

GLM-5.3 is deployed where problems need step-by-step reasoning before code output — complex bug fixes, algorithmic implementation, math-heavy analysis, and multi-step refactoring. The internal chain-of-thought adds latency (expect slower responses), so don't route high-volume simple queries here. Use it for the hard 10% of tasks where cheaper models fail.

Cost vs sibling models

What makes GLM-5.3 different from sibling models: compared to GLM-5.3-Flash ($1.00/1M cheaper, same 1M context); GLM-5.2 ($0.29/1M more expensive, same 1M context); GLM-5.1 ($0.26/1M cheaper, 128K vs 1M context (smaller)). Choose GLM-5.3 when step-by-step reasoning matters more than speed.

Real-world cost scenarios

Real-world deployments: autonomous coding agents that fix complex bugs across multi-file codebases, algorithmic problem solving, math-heavy data analysis pipelines, and automated code review for architecture-level decisions. Teams route the simple 90% of coding tasks to cheaper models and reserve GLM-5.3 for the hard 10%.

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