Pareto Inference — GLM 5.3 Flash

Served on our GPUs. Pay per token. Connect your coding agent, router, or app.

Available on

Supported by

Recent news

Pareto raises $4M in seed funding to expand its GPU fleet.

Growth

Aug 28, 2026Oct 10, 2026

Performance

GLM 5.3 FlashChecking sample time

Uptime: 100.00%
Time to first response: 1.63 s
Output speed: 94.8 tok/s
Cached input: 97.4%

Uptime history12h

2026-10-11 10:22:58 UTC2026-10-11 22:22:58 UTC

Pricing

GLM 5.3 Flash · USD per 1M tokens
Token type	OpenRouter	Pareto	Pareto Promo
InputTokens you send	$0.15	$0.09	$0.06
OutputTokens the model returns	$0.50	$0.30	$0.20
Cached inputPrompt tokens reused from cache	$0.03	$0.018	$0.012

Pricing details

40% less than OpenRouter.

Setup

Direct API

Coding agents

and more

Routers

and more

Chat apps

Install the OpenAI Python SDK and set PARETO_API_KEY in your environment.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.paretoinference.com/v1",
    api_key=os.environ["PARETO_API_KEY"],
)

response = client.chat.completions.create(
    model="z-ai/glm-5.3-flash",
    messages=[{"role": "user", "content": "Hello, Pareto."}],
    stream=True,
)
for chunk in response:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Open setup guide

Install the OpenAI SDK and set PARETO_API_KEY in your server environment.

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.PARETO_API_KEY,
  baseURL: "https://api.paretoinference.com/v1",
});

const response = await client.chat.completions.create({
  model: "z-ai/glm-5.3-flash",
  messages: [{ role: "user", content: "Hello, Pareto." }],
  stream: true,
});

for await (const chunk of response) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

Open setup guide

Set PARETO_API_KEY for OpenClaw and merge this into ~/.openclaw/openclaw.json. Start a new session.

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "pareto/z-ai/glm-5.3-flash"
      }
    }
  },
  "models": {
    "mode": "merge",
    "providers": {
      "pareto": {
        "baseUrl": "https://api.paretoinference.com/v1",
        "apiKey": "${PARETO_API_KEY}",
        "api": "openai-completions",
        "models": [
          {
            "id": "z-ai/glm-5.3-flash",
            "name": "GLM 5.3 Flash (Pareto)"
          }
        ]
      }
    }
  }
}

Open setup guide

Set PARETO_API_KEY and merge this into ~/.config/opencode/opencode.json. Keep your existing providers and settings, then start OpenCode.

{
  "$schema": "https://opencode.ai/config.json",
  "model": "pareto/z-ai/glm-5.3-flash",
  "small_model": "pareto/z-ai/glm-5.3-flash",
  "provider": {
    "pareto": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Pareto Inference",
      "options": {
        "baseURL": "https://api.paretoinference.com/v1",
        "apiKey": "{env:PARETO_API_KEY}"
      },
      "models": {
        "z-ai/glm-5.3-flash": {
          "name": "GLM 5.3 Flash"
        }
      }
    }
  }
}

Open setup guide

Uses Claude Code Router. Back up ~/.claude/settings.json, save this as ~/.claude-code-router/config.json, and run ccr start. Set ANTHROPIC_MODEL and ANTHROPIC_DEFAULT_HAIKU_MODEL to pareto/z-ai/glm-5.3-flash.

{
  "HOST": "127.0.0.1",
  "PORT": 3456,
  "APIKEY": "your-local-ccr-key",
  "Providers": [
    {
      "name": "pareto",
      "api_base_url": "https://api.paretoinference.com/v1/chat/completions",
      "api_key": "$PARETO_API_KEY",
      "models": [
        "z-ai/glm-5.3-flash"
      ]
    }
  ]
}

Open setup guide

Uses LiteLLM. Run LiteLLM with this model, then add a Codex provider for http://127.0.0.1:4000/v1 with wire_api = "responses" and model pareto-flash.

model_list:
  - model_name: pareto-flash
    litellm_params:
      model: openai/z-ai/glm-5.3-flash
      api_base: https://api.paretoinference.com/v1
      api_key: os.environ/PARETO_API_KEY
      use_chat_completions_api: true
      additional_drop_params: ["web_search_options"]

Open setup guide

Set PARETO_API_KEY and merge this provider into ~/.pi/agent/models.json. Select it with /model.

{
  "providers": {
    "pareto": {
      "baseUrl": "https://api.paretoinference.com/v1",
      "api": "openai-completions",
      "apiKey": "$PARETO_API_KEY",
      "models": [
        {
          "id": "z-ai/glm-5.3-flash",
          "name": "GLM 5.3 Flash (Pareto)"
        }
      ]
    }
  }
}

Open setup guide

Set PARETO_API_KEY and save this as ~/.config/goose/custom_providers/pareto.json. Set GOOSE_PROVIDER=pareto and GOOSE_MODEL, then run Goose with the developer extension.

{
  "name": "pareto",
  "engine": "openai",
  "display_name": "Pareto Inference",
  "api_key_env": "PARETO_API_KEY",
  "base_url": "https://api.paretoinference.com/v1/chat/completions",
  "models": [
    {
      "name": "z-ai/glm-5.3-flash"
    }
  ],
  "requires_auth": true,
  "supports_streaming": true
}

Open setup guide

Set PARETO_API_KEY, then start Aider in your repository. Keep the openai/ prefix and ignore the model names Aider suggests.

export OPENAI_API_BASE=https://api.paretoinference.com/v1
export OPENAI_API_KEY="$PARETO_API_KEY"
aider --model openai/z-ai/glm-5.3-flash --no-show-model-warnings

Open setup guide

Put PARETO_API_KEY in ~/.continue/.env and merge this into ~/.continue/config.yaml. The editor extension does not read shell variables.

name: Pareto Inference
version: 1.0.0
schema: v1
models:
  - name: GLM 5.3 Flash (Pareto)
    provider: openai
    model: z-ai/glm-5.3-flash
    apiBase: https://api.paretoinference.com/v1
    apiKey: ${{ secrets.PARETO_API_KEY }}
    roles:
      - chat
      - edit
      - apply
    capabilities:
      - tool_use

Open setup guide

Set PARETO_API_KEY and merge this into ~/.omp/agent/models.yml. Start Oh My Pi with omp --model pareto/z-ai/glm-5.3-flash.

providers:
  pareto:
    baseUrl: https://api.paretoinference.com/v1
    api: openai-completions
    apiKey: PARETO_API_KEY
    authHeader: true
    models:
      - id: z-ai/glm-5.3-flash
        name: GLM 5.3 Flash (Pareto)

Open setup guide

Set PARETO_API_KEY and add this to ~/.config/crush/crushrc. Select pareto/z-ai/glm-5.3-flash in Crush. Use openai-compat, not openai.

provider add pareto --type openai-compat \
  --name "Pareto Inference" \
  --base-url "https://api.paretoinference.com/v1" \
  --api-key "$PARETO_API_KEY"

model add pareto/z-ai/glm-5.3-flash --name "GLM 5.3 Flash"

Open setup guide

Set PARETO_API_KEY, then start Qwen Code. Use --auth-type openai, not openai-responses.

export OPENAI_API_KEY="$PARETO_API_KEY"
export OPENAI_BASE_URL=https://api.paretoinference.com/v1
export OPENAI_MODEL=z-ai/glm-5.3-flash
qwen --auth-type openai

Open setup guide

Settings → Model settings → Add provider → Create custom provider. Use Chat completions, the Pareto base URL, and model z-ai/glm-5.3-flash. Clear Image, Video and PDF, and paste this into the model's Reasoning parameter mapping. The default mapping sends fields Pareto rejects.

{
  "reasoning_effort": reasoningLevel == "disabled" ? "none" : reasoningLevel == "enabled" ? "high" : reasoningLevel
}

Open setup guide

Add this model to your existing config. Keep your proxy access settings.

model_list:
  - model_name: pareto-flash
    litellm_params:
      model: openai/z-ai/glm-5.3-flash
      api_base: https://api.paretoinference.com/v1
      api_key: os.environ/PARETO_API_KEY

Open setup guide

Add this entry to config.yaml and restart CLIProxyAPI. The base URL keeps /v1. Apps use a key from api-keys, not your Pareto key.

openai-compatibility:
  - name: "pareto"
    base-url: "https://api.paretoinference.com/v1"
    api-key-entries:
      - api-key: "YOUR_PARETO_API_KEY"
    models:
      - name: "z-ai/glm-5.3-flash"

Open setup guide

Run the gateway from the Docker image; the npx build breaks streaming. Apps send your Pareto key and both x-portkey headers.

docker run -d -p 127.0.0.1:8787:8787 portkeyai/gateway:latest

curl http://localhost:8787/v1/chat/completions \
  -H "Authorization: Bearer $PARETO_API_KEY" \
  -H "Content-Type: application/json" \
  -H "x-portkey-provider: openai" \
  -H "x-portkey-custom-host: https://api.paretoinference.com/v1" \
  -d '{"model": "z-ai/glm-5.3-flash", "messages": [{"role": "user", "content": "Say hello."}], "stream": true}'

Open setup guide

Set PARETO_API_KEY and save this as config.json in the Bifrost app directory. The base URL has no /v1. Apps use the model pareto/z-ai/glm-5.3-flash.

{
  "providers": {
    "pareto": {
      "keys": [
        {
          "name": "pareto",
          "value": "env.PARETO_API_KEY",
          "models": [
            "*"
          ],
          "weight": 1
        }
      ],
      "network_config": {
        "base_url": "https://api.paretoinference.com",
        "default_request_timeout_in_seconds": 300
      },
      "custom_provider_config": {
        "base_provider_type": "openai",
        "allowed_requests": {
          "chat_completion": true,
          "chat_completion_stream": true,
          "list_models": true
        }
      }
    }
  }
}

Open setup guide

Run NextChat with your key on the server. BASE_URL has no /v1, and CUSTOM_MODELS adds the Pareto model.

docker run -d -p 3000:3000 \
  -e OPENAI_API_KEY="$PARETO_API_KEY" \
  -e BASE_URL="https://api.paretoinference.com" \
  -e CUSTOM_MODELS="-all,+z-ai/glm-5.3-flash@OpenAI" \
  -e DEFAULT_MODEL="z-ai/glm-5.3-flash@OpenAI" \
  yidadaa/chatgpt-next-web:v2.16.1

Open setup guide

Run AnythingLLM with the Generic OpenAI provider. Keep Max Tokens at 8192 or more; reasoning counts toward it.

docker run -d -p 3001:3001 --cap-add SYS_ADMIN \
  -v anythingllm_storage:/app/server/storage \
  -e STORAGE_DIR=/app/server/storage \
  -e LLM_PROVIDER=generic-openai \
  -e GENERIC_OPEN_AI_BASE_PATH=https://api.paretoinference.com/v1 \
  -e GENERIC_OPEN_AI_API_KEY="$PARETO_API_KEY" \
  -e GENERIC_OPEN_AI_MODEL_PREF=z-ai/glm-5.3-flash \
  -e GENERIC_OPEN_AI_MAX_TOKENS=8192 \
  mintplexlabs/anythingllm:1.16.2

Open setup guide

Add this endpoint to librechat.yaml, set PARETO_API_KEY in .env, and restart. Keep dropParams: Pareto rejects the user field.

endpoints:
  custom:
    - name: "Pareto"
      apiKey: ${PARETO_API_KEY}
      baseURL: "https://api.paretoinference.com/v1"
      models:
        default: ["z-ai/glm-5.3-flash"]
        fetch: true
      titleConvo: true
      titleModel: "current_model"
      dropParams: ["user"]

Open setup guide

Frontier GLM 5.3 Flash inference at a 40% discount