Back to Models
z-ai logoZ.ai
glm-5.3-flashx

GLM 5.3 FlashX

GLM-5.3-FlashX is the high-speed variant of Z.ai's GLM-5.3-Flash, a native multimodal model delivering inference speeds of up to 200 tokens/s. Built on the same hybrid sparse and linear attention architecture...

9/18/2026
1,048,576 tokens
Input: $0.407/M
Output: $1.38/M

Specifications

Modalities

Input
text
image
video
Output
text

Supported Parameters

include_reasoning
max_tokens
reasoning
reasoning_effort
response_format
temperature
tool_choice
tools
top_k
top_p

Max Output Tokens

131,072

Reasoning Configuration

Default
Thinking on (effort: max)
Selectable effort levels
max
high
low
Turning thinking off
Not possible (always thinks)

How to control thinking, and what it costs

Data policy

Prompt retention
Not retained: every host this model runs on is configured not to retain prompts
Training
Not used for training

Whether a provider trains on prompts is a declared value from our terms with them. Retention is determined from the upstream listing for every host this model can reach. Neither is guessed.

Measured performance

Time to first response (p50)1.2s
Throughput (p50)316 tokens/s

    Measured through FastMetal's own gateway over the last 30 days (662 first-response samples).

    Throughput is sampled once a day: the median of 21 observations over the last 30 days.

    Time to first response is measured to the first streamed chunk; throughput counts all output tokens, including reasoning tokens. These are observations, not a performance guarantee.

    Code Examples

    curl https://api.fastmetal.ai/v1/chat/completions \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer YOUR_API_KEY" \
      -d '{
        "model": "glm-5.3-flashx",
        "messages": [{"role": "user", "content": "Hello!"}]
      }'

    How GLM 5.3 FlashX actually answers

    Real responses to our standard prompts, recorded on FastMetal.

    Count the number of 'r's in 'strawberry'

    Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

    # Counting 'r's in "strawberry" **Step 1: Write out each letter with its position** | Position | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |----------|---|---|---|---|---|---|---|---|---|----| | Letter | s | t | r | a | w | b | e | r | r | y | **Step 2: Check each letter one by one** - Position 1: **s** → not an 'r' - Position 2: **t** → not an 'r' - Position 3: **r** → ✅ 'r' found (count: 1) - Position 4: **a** → not an 'r' - Position 5: **w** → not an 'r' - Position 6: **b** → not an 'r' - Position 7: **e** → not an 'r' - Position 8: **r** → ✅ 'r' found (count: 2) - Position 9: **r** → ✅ 'r' found (count: 3) - Position 10: **y** → not an 'r' **Step 3: Tally the results** The 'r's a…
    4.1s response549 output tokens

    Debug This Error

    I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…

    ## What's Happening The error means `users` is `undefined` at the time `UserList` renders. You're calling `.map()` on `undefined`, which JavaScript doesn't allow. This almost always happens for one of these reasons: 1. **The parent isn't passing the prop** — `<UserList />` with no `users` prop 2. **Data hasn't loaded yet** — you're fetching asynchronously, so the first render happens before the data arrives 3. **Prop name mismatch** — e.g., passing `user` but reading `users` 4. **Parent state initialized to `undefined`** — e.g., `useState()` instead of `useState([])` ## Fixes ### Option 1: Default parameter (simplest) ```jsx function UserList({ users = [] }) { return ( <ul>…
    6.8s response934 output tokens

    Code Review

    Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…

    # Code Review The function works, but there are several issues worth addressing — including one subtle bug caused by the bare `except`. ## Correctness issues (most important) 1. **Bare `except:` is dangerous and masks a bug.** It catches *everything*, including `KeyboardInterrupt` and `SystemExit` (so Ctrl+C during a retry sleep is swallowed). Worse: because your data-processing loop sits inside the `try`, a `KeyError` from a malformed item is treated as a network failure — triggering a pointless re-download of the URL and silently discarding the real error. Catch only `requests.RequestException` (and `ValueError` for JSON decode failures). 2. **Silent failure.** Returning `[]` on error…
    36.9s response5836 output tokens

    Compare these answers side by side with other models →

    Frequently asked questions

    How much does the GLM 5.3 FlashX API cost?
    On FastMetal, GLM 5.3 FlashX is billed per token: $0.407 per 1M input tokens and $1.38 per 1M output tokens on a US-dollar account, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.
    Can I call GLM 5.3 FlashX with the OpenAI SDK?
    Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "glm-5.3-flashx" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.
    What is the context window of GLM 5.3 FlashX?
    1,048,576 tokens, shared between the prompt and the response.
    What do I need to try GLM 5.3 FlashX?
    Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

    Try GLM 5.3 FlashX right now

    GLM 5.3 FlashX is available on FastMetal through one API key. Start in the browser, or call it from the OpenAI SDK.