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Z.ai — glm-5.3-flash logo

GLM 5.3 Flash

by Z.ai · Released 2026

Z.ai's fast, low-cost GLM 5.3 Flash with a 1,048,576-token context window, image input, tool calling, and reasoning you can set to low, high or max.

Z.ai — glm-5.3-flash logo
LLM Chat

GLM 5.3 Flash

Powered by Z.ai · Undisclosed

Context Window

1M

Parameters

Undisclosed

Max Output

1M (shared with input)

Category

LLM Chat

Overview

GLM 5.3 Flash is the fast, low-cost member of Z.ai's GLM 5.3 family, built for coding agents and tool use. It has a 1,048,576-token context window, accepts images alongside text, supports multi-turn tool calling and structured output with a JSON schema, and streams its replies.

GLM 5.3 Flash always reasons before it answers and the reasoning cannot be turned off. `reasoning_effort` sets how much: `low`, `high` or `max`, with `high` as the default. Reasoning tokens bill as output tokens and count toward `max_tokens`, so raise `max_tokens` or lower the effort if a reply comes back short; when you omit `max_tokens` we send 16,384. On CallMissed, `none` and `minimal` map to `low`, `medium` to `high` and `xhigh` to `max`, so any OpenAI-style value returns a 200.

For latency-sensitive work, send `reasoning_effort: "low"`: in our tests the first token arrived in about 0.4 to 0.5 seconds. Send images as base64 data URIs in the standard `image_url` content part; `https://` image URLs are fetched and inlined for you.

Pricing

MetricPrice
Input /1M tokens₹22.0000
Output /1M tokens₹73.0000

1 credit = ₹1 ≈ US$0.0104 (US$1 = ₹96). Transparent per-call pricing — you pay only for what you call, with no per-seat charge or minimums.

Key Highlights

  • 1,048,576-token context window
  • Image input and tool calling
  • Reasoning effort: low, high or max
  • OpenAI-compatible, with streaming and structured output

Benchmarks

BenchmarkScore
Context1M
First token~0.4-0.5s
Tool CallingYes

Technical Details

  • Context window: 1,048,576 tokens
  • Reasoning always on: low / high / max (default high)
  • Reasoning tokens are billed as output tokens and count toward max_tokens (16,384 sent when omitted)
  • Tool calling via OpenAI-compatible tools and tool_choice
  • Structured output with a JSON schema response_format
  • Text and image input; images as base64 data URIs (https URLs are inlined for you)

Strengths

  • Low price for a 1M-context reasoning model
  • Image input plus tool calling in one model
  • Fast first token at low reasoning effort

Limitations

  • Always reasons, so it is slower than a direct-answer model
  • Reasoning shares the max_tokens budget: raise max_tokens or lower the effort
  • Images must be sent inline as base64 (https URLs are fetched for you)
  • Paid plans only

Use Cases

Coding agentsTool-using agentsImage and document understandingLong-document analysis

API Example

curl https://api.callmissed.com/v1/chat/completions \
  -H "Authorization: Bearer cm_YOUR_KEY" \
  -d '{"model": "glm-5.3-flash", "reasoning_effort": "low", "messages": [{"role": "user", "content": "Summarise these meeting notes"}]}'

Endpoint: POST /v1/chat/completions · Model ID: glm-5.3-flash

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