Skip to main content
Open SourceZ AI

GLM-5.3-Flash

GLM-5.3-Flash is a chat model from Z AI, positioned in the GLM line's "Flash" tier for latency- and cost-sensitive workloads.

License Open Source
Input from
$0.045 / 1M tokens
across 17 providers

API Pricing

Cheapest on OpenRelay — 65% below avg
ProviderInput / 1MOutput / 1MCached / 1M
$0.045$0.450$0.0090
$0.060$0.200$0.012
$0.060$0.200$0.012
$0.075$0.250$0.075
$0.090$0.300$0.018
$0.110$0.400-
$0.140$0.745$0.070
$0.150$0.500$0.030
$0.150$0.500$0.030
$0.150$0.500-
$0.150$0.500$0.030
$0.150$0.500-
$0.150$0.500-
$0.150$0.500$0.030
$0.150$0.500$0.030
$0.150$0.500$0.030
$0.200$0.500$0.050
$0.200$0.600$0.037

Prices updated daily. Last check: Oct 12, 2026

Compare API pricing for every GLM model →

GLM-5.3-Flash pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Performance & Benchmarks

Source: Artificial Analysis →
Intelligence
41.8 / 100
Coding
71.5 / 100
Output Speed
58.8 t/s
Latency (TTFT)
2.5s

Reasoning & Knowledge

  • GPQA Diamond91.2%
  • Humanity's Last Exam39.9%

Coding

  • SciCode51.6%

Agentic & Tool Use

  • Terminal-Bench v2.184.3%
  • τ-bench Banking47.2%

Instruction & Long Context

  • Long-Context Reasoning80.0%

Benchmarks measured Oct 2026. Scores are independent evaluations, not vendor-reported.

Model Details

General

Creator
Z AI
Modalities
Text

Capabilities

Open Source
Yes

Strengths & Limitations

Strengths

  • Positioned in the GLM "Flash" tier, which Z AI uses for lower-cost, lower-latency serving rather than maximum-capability workloads
  • Independently measured throughput of approximately 49 output tokens per second (Artificial Analysis)
  • Time to first token of roughly 1,180 ms supports streaming chat interfaces
  • Part of the GLM 5.3 generation, so it can be paired with larger same-generation models in a routing or fallback setup
  • Suited to high-request-volume text pipelines where per-request cost dominates total spend
  • Available through multiple hosted API providers, letting buyers compare price and measured speed side by side

Limitations

  • We do not have a confirmed context window for this model — verify limits with your serving provider before building long-document workflows
  • No standardized reasoning, coding, or multilingual benchmark scores are tracked in our database for this entry
  • Time to first token near 1,180 ms is slower than the quickest-responding endpoints, which matters for latency-critical voice or autocomplete use
  • Flash-tier models generally trade capability for cost, so complex multi-step reasoning may be better served by larger GLM 5.3 models
  • Measured throughput of ~49 tokens/second is mid-range; long-form generation will feel slower than on high-throughput serving stacks

Key Features

•Chat/instruction-following text generation
•GLM 5.3 generation, Flash tier
•Approximately 49 output tokens per second measured throughput
•~1,180 ms measured time to first token
•Streaming response support via hosted API endpoints
•Multi-provider availability for price and performance comparison
•Designed for cost-sensitive, high-volume request patterns

About GLM-5.3-Flash

GLM-5.3-Flash is a text chat model developed by Z AI (the team behind the GLM series). The "Flash" suffix marks it as a lighter-weight member of the GLM 5.3 generation, intended for workloads where responsiveness and serving cost matter more than maximum capability. As with other Flash-tier GLM releases, it sits below the larger models in the same generation rather than replacing them. Third-party measurements from Artificial Analysis place GLM-5.3-Flash at roughly 49 output tokens per second with a time to first token of about 1,180 ms. That combination — moderate streaming throughput with just over a second of initial latency — makes it usable for interactive chat and streaming interfaces, though the TTFT figure is higher than the fastest-responding endpoints in the market. Measured speed varies by provider, region, prompt length, and load, so figures observed in production may differ from benchmark conditions. We do not currently track a confirmed context window, modality list, or standard reasoning/coding benchmark scores for GLM-5.3-Flash, so buyers evaluating it for a specific task should verify those details against the serving provider's documentation. In practice, Flash-tier models in the GLM family are typically deployed for high-request-volume text work — summarization, classification, extraction, chat assistants — where a smaller model is run at scale instead of routing every request to a larger sibling. Compare the providers listed in the pricing table on this page, since throughput and cost for the same model can differ substantially between hosts.

Common Use Cases

GLM-5.3-Flash fits workloads that send many text requests and care about per-request cost: chat assistants and support bots, summarization of tickets or documents, classification and tagging, structured data extraction, content rewriting, and drafting steps inside a larger pipeline. Its measured ~49 tokens/second output rate and just-over-one-second time to first token are adequate for streaming chat UIs where users see partial output immediately. A common pattern with Flash-tier models is tiered routing — handle the bulk of straightforward requests here and escalate harder prompts to a larger GLM 5.3 model. For tasks that hinge on deep multi-step reasoning, long-context document analysis, or verified benchmark thresholds, confirm capabilities with the provider first, since we do not track context length or task benchmarks for this entry.

Frequently Asked Questions

How much does GLM-5.3-Flash cost?

Pricing depends on which provider hosts the model and on the pricing type — input tokens, output tokens, and any cached-input or batch rates are billed separately, and rates change frequently. Check the pricing table on this page for current per-provider figures rather than relying on a fixed number.

What is GLM-5.3-Flash best used for?

High-volume text work where cost and responsiveness matter more than maximum capability: chat assistants, summarization, classification, extraction, and drafting steps in automated pipelines. It is also a reasonable default tier in a routing setup that escalates harder prompts to a larger GLM 5.3 model.

How fast is GLM-5.3-Flash?

Artificial Analysis measured roughly 49 output tokens per second with a time to first token of about 1,180 ms. Real-world numbers vary by provider, region, prompt length, and load, so compare providers if latency is a hard requirement.

How does GLM-5.3-Flash differ from larger GLM 5.3 models?

Flash-tier GLM models are built for cheaper, faster serving, while the larger models in the same generation target harder reasoning and agentic tasks. If your prompts involve long multi-step reasoning or complex tool orchestration, evaluate a larger sibling alongside Flash before committing.

What is the context window of GLM-5.3-Flash?

We do not have a confirmed context window for this model in our database. Check the documentation of the provider you plan to use, since hosted endpoints can also impose their own maximum input and output limits.