What is the context window of GLM-4.5-Flash?

GLM-4.5-Flash has a 131,072-token context window with a maximum output of 98,304 tokens. It accepts text input.

What are the rate limits for GLM-4.5-Flash?

Rate limits for GLM-4.5-Flash depend on your MixRoute plan tier. Check your current quota in the console and run a small load test on your own workload.

What is GLM-4.5-Flash best used for?

GLM-4.5-Flash is optimized for fast, cost-effective inference for high-throughput tasks. It supports thinking mode, function calling, structured outputs, and MCP integration for agentic workflows.

Can I use the existing OpenAI SDK to call GLM-4.5-Flash?

Yes. GLM-4.5-Flash is available through MixRoute’s OpenAI-compatible Chat Completions endpoint. Set the model parameter to “glm-4.5-flash” and use your MixRoute API key.

What should you evaluate before moving production traffic to GLM-4.5-Flash?

Before moving production traffic to GLM-4.5-Flash, verify the specs and rate limits in the console, run a pilot on your typical tasks, and compare quality, latency, and cost against your current setup on a representative sample.

Model detail

GLM-4.5-Flash

Provided by GLM

Delisted Pay-as-you-go

A free, lightweight GLM-4.5 model supporting reasoning and everyday conversational tasks.

Model specs

Model ID: glm-4.5-flash

Context length: 131.072K
Max output: 98.304K
I/O modalities
Released: 2025-07

API endpoints

One MixRoute gateway, OpenAI-compatible

OpenAI-compatible /v1/chat/completions POST

Pricing

Flat rate, unit: /1M Tokens

Input

$0.0000 /1M Tokens

Completion

$0.0000 /1M Tokens

Selection summary

Quickly judge whether it fits your workload

What is this model good for?

A free, lightweight GLM-4.5 model supporting reasoning

A free, lightweight GLM-4.5 model supporting reasoning and everyday conversational tasks.

What should you check before using it?

131,072 context, 98,304 output tokens

GLM model that supports reasoning, tool calling, structured output, streaming, prompt caching and the OpenAI SDK. Knowledge cutoff: 2025-04. Released 2025-07-28.

Why use it through MixRoute?

Drop-in via OpenAI-compatible endpoint on MixRoute

Set the model parameter to "glm-4.5-flash" and use your MixRoute API key. Compatible with OpenAI SDK and standard tool-calling patterns.

Capabilities & compatibility

Unconfirmed capabilities stay "Unknown," no guesses

Tool calling

GLM-4.5-Flash supports function and tool calls.

EX Send a tools definition; the response returns tool_calls to execute and feed results back.

Structured output

GLM-4.5-Flash can produce JSON-formatted output on demand.

EX response_format: { type: "json_schema" } forces valid JSON output.

Streaming

GLM-4.5-Flash supports streamed responses.

EX stream: true streams tokens incrementally, so chat UIs render as they arrive.

Prompt caching

GLM-4.5-Flash supports prompt caching under supported routing conditions.

EX Keep the system prompt and tool definitions at the start of the request; repeated prefixes are automatically billed at the cache-read rate.

OpenAI SDK

Call GLM-4.5-Flash through MixRoute's OpenAI-compatible endpoint.

EX Change base_url to https://api.mixroute.ai/v1; everything else follows the OpenAI style.

Token cost estimator

Live estimate from this page's pricing, not an actual bill

Input tokens Max per request 131.072K

Output tokens Max per request 98.304K

Requests / month

Cache hit rate –

This model has no cache-read price; caching is not counted

FAQ

GLM-4.5-Flash supports context caching, which can significantly reduce latency and cost for repeated prefixes. Stable content such as system prompts and tool definitions benefits most.

Get started

01

Create an API key

Generate a key in the Console; one key calls every model.
02

Set the base URL

Fully OpenAI SDK-compatible; just swap the URL to connect.

https://api.mixroute.ai/v1
03

Specify the model ID

In the request's model field, enter:

glm-4.5-flash

Create an API key

New users get $5 in free credits

Test this model and alternate routes with the same request format

Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.

Create an API key View integration docs