GLM 5.3 Flash

zai-org/GLM-5.3-Flash

Get API key Chat Completions API reference

GLM 5.3 Flash is an LLM listed in RunInfra Model APIs. RunInfra serves it as zai-org/GLM-5.3-Flash at $0.08 per 1M input tokens until Oct 13, 2026, 5:44 AM UTC, with standard rates resuming automatically. Its context window is 1,048,576 tokens. The API provides OpenAI-compatible chat completions and Anthropic-compatible Messages, POST /v1/messages.

Pricing

USD, pay per token

per 1M input tokens: $0.08
per 1M cached input tokens: $0.02
per 1M output tokens: $0.33

Input and output are 26% off until Oct 13, 2026, 5:44 AM UTC.

02days

08hrs

28min

46sec

Standard rates resume automatically when the window ends.

Measured performance

Output speed

254.1output tokens per second, model only

Time to first token

703milliseconds to first reasoning token

Cache hit rate

99.8%last 24 hours

Access

Confirm how your client reaches this model.

Provider: Z.ai
API compatibility: OpenAI-compatible chat completions
Anthropic compatibility: Anthropic-compatible Messages, POST /v1/messages
Accepted input: Text and images
Availability: Available
Data retention: Zero data retention by default. Never used for training.

Capacity

Check the limits your workload must fit.

Context window: 1,048,576 tokens
Maximum request size: 3.5 MB per request
Maximum generated output: 1,048,576 tokens

Capabilities

See which request modes the API supports.

Tool calling: Supported
JSON mode: Supported
Streaming: Supported
Precision: FP8, vendor-native release

View full spec

Gateway compatibility: OpenAI- and Anthropic-compatible APIs for compatible clients, tools, and gateways
Prefix caching: Automatic prefix caching runs on every replica, and requests from the same session or conversation are routed back to the replica that holds their cached prefix. A stable session hint or prompt_cache_key provides explicit grouping; otherwise RunInfra derives it from the start of the conversation or, failing that, the workspace. Hits remain best effort until eviction or a serve restart, never guaranteed. Cached input is billed at the cached input rate. Responses report cached token counts.
Cache retention: Prefix cache is held on the serving GPU and evicted under memory pressure. Retention is best effort.
Upstream model: View model

Code examples

Set RUNINFRA_GATEWAY_KEY to your workspace API key before using an example.

ShellChat completion

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
curl -X POST "https://api.runinfra.ai/v1/chat/completions" \
  -H "Authorization: Bearer $RUNINFRA_GATEWAY_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"glm-5-3-flash","messages":[{"role":"user","content":"Hello"}],"max_tokens":2048}'

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import os
import uuid
from openai import OpenAI

api_key = os.environ.get("RUNINFRA_GATEWAY_KEY")
if not api_key: raise RuntimeError("Set RUNINFRA_GATEWAY_KEY first.")

client = OpenAI(
    api_key=api_key,
    base_url="https://api.runinfra.ai/v1",
)

response = client.chat.completions.create(
    model="glm-5-3-flash",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=2048,
    extra_headers={"X-Client-Request-Id": str(uuid.uuid4())},
)
print(response.choices[0].message.content)

// Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import { randomUUID } from "node:crypto";
import OpenAI from "openai";

const apiKey = process.env.RUNINFRA_GATEWAY_KEY;
if (!apiKey) throw new Error("Set RUNINFRA_GATEWAY_KEY first.");

const client = new OpenAI({
  apiKey,
  baseURL: "https://api.runinfra.ai/v1",
});

const response = await client.chat.completions.create(
  {
    model: "glm-5-3-flash",
    messages: [{ role: "user", content: "Hello" }],
    max_tokens: 2048,
  },
  { headers: { "X-Client-Request-Id": randomUUID() } },
);

console.log(response.choices[0]?.message.content ?? "");

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import base64
import os
import uuid
from openai import OpenAI

api_key = os.environ.get("RUNINFRA_GATEWAY_KEY")
if not api_key: raise RuntimeError("Set RUNINFRA_GATEWAY_KEY first.")

client = OpenAI(
    api_key=api_key,
    base_url="https://api.runinfra.ai/v1",
)

# Images travel inline as base64 data URLs (png, jpeg, webp or gif,
# up to 8 per request). Remote image URLs are not fetched.
with open("photo.jpg", "rb") as image_file:
    image_b64 = base64.b64encode(image_file.read()).decode("ascii")

response = client.chat.completions.create(
    model="glm-5-3-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this photo?"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"},
                },
            ],
        }
    ],
    max_tokens=2048,
    extra_headers={"X-Client-Request-Id": str(uuid.uuid4())},
)
print(response.choices[0].message.content)

// Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
// Server-side only: never expose RUNINFRA_GATEWAY_KEY in browser code.
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { generateText } from "ai";

const apiKey = process.env.RUNINFRA_GATEWAY_KEY;
if (!apiKey) throw new Error("Set RUNINFRA_GATEWAY_KEY first.");

const runinfra = createOpenAICompatible({
  name: "runinfra",
  baseURL: "https://api.runinfra.ai/v1",
  apiKey,
});

const { text } = await generateText({
  model: runinfra.chatModel("glm-5-3-flash"),
  maxOutputTokens: 2048,
  messages: [
    { role: "user", content: "Write a TypeScript retry helper." },
  ],
});

console.log(text);

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import os
from anthropic import Anthropic

api_key = os.environ.get("RUNINFRA_GATEWAY_KEY")
if not api_key: raise RuntimeError("Set RUNINFRA_GATEWAY_KEY first.")

client = Anthropic(
    # The SDK appends /v1, so use the bare host.
    base_url="https://api.runinfra.ai",
    api_key=api_key,
)

message = client.messages.create(
    model="glm-5-3-flash",
    max_tokens=2048,
    messages=[{"role": "user", "content": "Hello"}],
)
print(message.content[0].text)

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
export ANTHROPIC_BASE_URL="https://api.runinfra.ai"
export ANTHROPIC_AUTH_TOKEN=$RUNINFRA_GATEWAY_KEY
export ANTHROPIC_MODEL="glm-5-3-flash"
export ANTHROPIC_DEFAULT_SONNET_MODEL="glm-5-3-flash"
export ANTHROPIC_DEFAULT_OPUS_MODEL="glm-5-3-flash"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="glm-5-3-flash"
export CLAUDE_CODE_SUBAGENT_MODEL="glm-5-3-flash"
claude

Trust and provenance

Verify the company and operating credentials behind this API.

RunInfra is a sub-product of RightNow Research Lab.

SOC 2 Type IIAudited access, logging, and incident response.
Y CombinatorBacked by Y Combinator.
NVIDIA InceptionMember of NVIDIA Inception.