GLM-5.3-Flash
How TheRouter serves this differently from the vendor
Z.AI documents GLM-5.3-Flash under the VLM section with text parameters matching GLM-5.3 and additional image_url input support. Context is 1M tokens, reasoning is always on (effort: low/high/max). No separate Flash pricing page has been extracted.
TheRouter exposes zai/glm-5.3-flash through the OpenAI Chat Completion surface with 1M context, 131,072 max completion tokens, text+image input, text output, tools, tool_choice, response_format, and reasoning_effort. Pricing is $0.162/M input and $0.54/M output.
API guide
Vision + text chat
GLM-5.3-Flash accepts image_url content blocks alongside text. Pass the image as a URL or base64 data URI in the user message content array.
curl https://api.therouter.ai/v1/chat/completions \
-H "Authorization: Bearer $THEROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai/glm-5.3-flash",
"reasoning_effort": "low",
"messages": [
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/diagram.png"}},
{"type": "text", "text": "Describe the architecture shown in this diagram."}
]
}
],
"max_tokens": 800
}'Text-only chat completion
For text-only tasks, GLM-5.3-Flash behaves like a cost-efficient version of GLM-5.3. Use reasoning_effort: "max" for hard tasks, "low" to reduce TTFT on quick queries.
curl https://api.therouter.ai/v1/chat/completions \
-H "Authorization: Bearer $THEROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai/glm-5.3-flash",
"reasoning_effort": "high",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarise the key risks in this migration plan."}
],
"max_tokens": 600
}'Tool calling
Tool calling with GLM-5.3-Flash follows the same pattern as GLM-5.3. Reasoning runs before tool selection; at low effort this is fast enough for high-frequency agentic loops while staying inside the ~9x cost saving.
curl https://api.therouter.ai/v1/chat/completions \
-H "Authorization: Bearer $THEROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai/glm-5.3-flash",
"reasoning_effort": "low",
"messages": [{"role": "user", "content": "Get the latest CI status for the main branch."}],
"tools": [{
"type": "function",
"function": {
"name": "get_ci_status",
"description": "Fetch CI pipeline status for a git branch",
"parameters": {
"type": "object",
"properties": {"branch": {"type": "string"}},
"required": ["branch"]
}
}
}],
"tool_choice": "auto"
}'Fact ledger β every claim on this page traces here
| source | URL | retrieved | |
|---|---|---|---|
| Context window | docs.z.ai β | 2026-09-18 | verified |
| Maximum output tokens | therouter.ai β | 2026-09-18 | verified |
| Modalities | docs.z.ai β | 2026-09-18 | verified |
| Image input | docs.z.ai β | 2026-09-18 | verified |
| Reasoning | docs.z.ai β | 2026-09-18 | verified |
| TheRouter pricing | therouter.ai β | 2026-09-18 | verified |
| Cost vs GLM-5.3 flagship | therouter.ai β | 2026-09-18 | verified |
| API capabilities | docs.z.ai β | 2026-09-18 | verified |
| Generation family | docs.z.ai β | 2026-09-18 | verified |
| Training data cutoff | β | β | unknown |
| DeepSWE v1.1 (GLM-5.3 flagship, generation reference) | docs.z.ai β | 2026-09-18 | verified |
| Z.ai Code Bench Max (GLM-5.3 flagship, generation reference) | docs.z.ai β | 2026-09-18 | verified |
| CyberGym vulnerability discovery (GLM-5.3 flagship, generation reference) | docs.z.ai β | 2026-09-18 | verified |
| GLM-5.3: Zhipu's Open-Source Flagship with 1M Context β API Integration and Routing Guide | TheRouter Blog β | 2026-09-18 | verified |
| What is different between GLM-5.3-Flash and GLM-5.3? | docs.z.ai β | 2026-09-18 | to verify |
| Can I disable reasoning in GLM-5.3-Flash? | docs.z.ai β | 2026-09-18 | to verify |
| Is GLM-5.3-Flash suitable for production vision pipelines? | therouter.ai β | 2026-09-18 | to verify |
| How does GLM-5.3-Flash compare to Google Gemini Flash for multimodal tasks? | therouter.ai β | 2026-09-18 | to verify |