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Z AI
GLM-5.3
GLM-5.3 is a chat-oriented large language model from Z AI, tracked on this page with measured throughput and latency figures alongside provider pricing.
Input from
$1.20 / 1M tokens
across 9 providers
API Pricing
Cheapest on Deep Infra — 14% below avg| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $1.20 | $4.00 | $0.120 | |
| $1.25 | $4.30 | - | |
| $1.33 | $4.31 | $0.666 | |
| $1.40 | $4.40 | $0.260 | |
| $1.40 | $4.40 | - | |
| $1.40 | $4.40 | - | |
| $1.40 | $4.40 | $0.260 | |
| $1.40 | $4.40 | $0.140 | |
| $1.75 | $4.50 | $0.440 |
Prices updated daily. Last check: Sep 5, 2026
GLM-5.3 pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Intelligence
48.6 / 100
Coding
74.8 / 100
Output Speed
78.4 t/s
Latency (TTFT)
1.4s
Reasoning & Knowledge
- GPQA Diamond91.7%
- Humanity's Last Exam42.3%
Coding
- SciCode59.0%
Agentic & Tool Use
- Terminal-Bench v2.183.9%
- τ-bench Banking50.3%
Instruction & Long Context
- Long-Context Reasoning79.7%
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- Z AI
- Modalities
- Text
Capabilities
- Tool Calling
- No
- Open Source
- No
Strengths & Limitations
Strengths
- Measured output speed of approximately 58.7 tokens per second in third-party benchmarking, sufficient for streaming chat interfaces
- Positioned in the GLM 5 series from Z AI, a model family with an established track record of general-purpose chat releases
- Chat-tuned rather than base-only, so it is usable for instruction-following and multi-turn dialogue without additional fine-tuning
- Independent performance data from Artificial Analysis is available, giving a vendor-neutral reference for speed and latency
- Listed across multiple providers on this page, allowing direct price and performance comparison before committing to a serving route
Limitations
- Time to first token of roughly 1,571 ms is a noticeable delay for latency-sensitive interfaces such as voice agents or inline code completion
- We do not currently track a confirmed context window for GLM-5.3, so long-document workloads require verification against provider documentation
- Modality support (image, audio, or other non-text inputs) is not recorded in our database and should be confirmed before building multimodal features
- No quality benchmark scores are tracked for this model in our data, making capability comparison against peers harder without your own evaluation
- Provider availability for GLM models is generally narrower than for the most widely hosted Western model families, which can limit routing and failover options
Key Features
•Chat and instruction-following interface for multi-turn conversation
•Part of the GLM 5 series from Z AI
•Measured output throughput of ~58.7 tokens per second
•Measured time to first token of ~1,571 ms
•Third-party performance data sourced from Artificial Analysis
•Streaming-capable generation suited to interactive assistant UIs
•Multi-provider pricing comparison available in the table on this page
About GLM-5.3
GLM-5.3 is a conversational large language model developed by Z AI, the group behind the GLM series of models. It is catalogued here as a chat model, meaning it is designed around instruction-following and multi-turn dialogue rather than a single narrow task such as embedding or transcription. The GLM line has historically been positioned as a general-purpose assistant family, and GLM-5.3 sits within that lineage as a numbered release in the GLM 5 series.
On the measurement side, our tracked benchmark data (sourced from Artificial Analysis) puts GLM-5.3 at roughly 58.7 output tokens per second with a time to first token of about 1,571 milliseconds. That combination describes a model that begins responding after a noticeable but modest delay and then streams at a moderate pace — adequate for interactive chat and background generation, and less suited to workloads that require sub-second first-token latency, such as voice pipelines or aggressive autocomplete. Throughput and latency both vary by serving provider, hardware, region, and prompt length, so treat these numbers as a reference point rather than a guarantee.
We do not currently track a confirmed context window, modality list, or benchmark suite scores for GLM-5.3 in our database, so this page focuses on what is verified: the model's identity as a Z AI chat model and its measured serving performance. For capability details such as maximum context length, tool calling support, or vision input, consult Z AI's own model documentation or the documentation of the specific provider you plan to route through, since hosted deployments of the same model can expose different feature sets.
Common Use Cases
GLM-5.3 suits general chat and assistant workloads where a moderate first-token delay is acceptable: customer-facing support bots, internal knowledge assistants, drafting and rewriting tools, summarization jobs, and batch content generation where total completion time matters more than instant responsiveness. Its measured throughput of roughly 59 tokens per second is comfortable for reading-speed streaming in a chat window, so users see text appear faster than they can read it. Workloads that are a poorer fit include real-time voice agents, inline IDE completion, and other interactions where the ~1.6 second time to first token would be felt directly. Before deploying it for long-context tasks such as whole-repository analysis or large document review, confirm the maximum context length with your chosen provider, since we do not track that figure in our database.
Frequently Asked Questions
How much does GLM-5.3 cost to run?
Pricing depends on which provider you route through and whether you are billed per input token, per output token, or through a dedicated or batch arrangement. Rates change frequently and differ between hosts of the same model, so check the pricing table on this page for the current per-provider figures rather than relying on a fixed number.
What is GLM-5.3 best used for?
It is a chat model, so it fits conversational assistants, drafting and summarization, and general instruction-following tasks. Its measured throughput makes streaming chat responses feel responsive once generation begins, while its time to first token makes it less appropriate for real-time voice or autocomplete scenarios.
How fast is GLM-5.3?
Third-party benchmarking from Artificial Analysis measures roughly 58.7 output tokens per second with a time to first token of about 1,571 milliseconds. Actual figures vary by provider, region, prompt length, and load, so treat these as reference values and test against your own workload.
What context window does GLM-5.3 support?
We do not currently have a confirmed context window recorded for GLM-5.3 in our database. Check Z AI's model documentation or the documentation of the specific provider you plan to use, since hosted deployments sometimes cap context below the model's maximum.
Who created GLM-5.3?
GLM-5.3 is developed by Z AI, the organization behind the GLM series of language models. It is a numbered release within the GLM 5 line.
How should I choose between GLM-5.3 and another chat model?
Compare the measured latency and throughput figures on this page against your interface requirements, then compare per-provider pricing in the table above. Because we do not track quality benchmark scores for GLM-5.3, running a short evaluation on your own prompts is the most reliable way to judge whether its output quality meets your needs.