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Model details

Z.ai: GLM 5.3 FlashX

GLM-5.3-FlashX is Z.ai's multimodal coding-oriented model within the GLM family, introduced on September 18, 2026. Available through third-party inference gateways at launch, it is positioned as a high-speed serving option that streams generated output at roughly 200 tokens per second. That streaming throughput is the model's defining practical characteristic, shaping its role in latency-sensitive developer workflows rather than marking it as a benchmark-topping reasoning system.

The model's design emphasis falls on agentic and interactive scenarios where small per-token delays compound across many turns. Z.ai and gateway partners specifically highlight its fit for coding agents, tool-driven loops, and applications where users watch output arrive in real time, since each iteration can begin generating the next step sooner when streaming is faster. Because no Z.ai technical report, model card, or independent benchmark suite was provided in the supporting evidence, claims about training data composition, parameter count, architecture choices, or comparative quality versus sibling GLM variants cannot be substantiated here.

Kilo Gatewayz-ai/glm-5.3-flashxglm

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Provider
Kilo Gateway
Model key
z-ai/glm-5.3-flashx
Release date
Sep 18, 2026
Last updated
Sep 18, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.37
Output token cost
$1.25

Limits

Output tokens
131,072 tokens
Context window
1,048,576 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Coverage

The CometAPI page for GLM-5.3 FlashX documents Z.ai's September 18, 2026 announcement for the FlashX API, identifying the model as a high-speed serving variant of Z.ai's multimodal coding model and reporting a generation speed of up to 200 tokens per second. It also provides concrete technical specifications attributab As a third-party reseller-hosted page, the CometAPI entry is not a Z.ai primary source, and the benchmark, comparison, and limitation sections referenced in the page are not present in the supplied excerpt, so no comparative quality numbers, latency distributions, or task evaluations can be cited from this candidate. T

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