Venice.ai API Skill

Venice.ai provides privacy-first AI infrastructure with uncensored models and zero data retention. The API is OpenAI-compatible, allowing use of the OpenAI SDK with Venice’s base URL. Inference runs on a decentralized network (DePIN) where nodes are disincentivized from retaining user data.

Quick Reference

Base URL: https://api.venice.ai/api/v1 Auth: Authorization: Bearer VENICE_API_KEY SDK: Use OpenAI SDK with custom base URL API Key Types: ADMIN (full access) or INFERENCE (inference only)

Setup

from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("VENICE_API_KEY"),
    base_url="https://api.venice.ai/api/v1"
)

import OpenAI from 'openai';

const client = new OpenAI({
    apiKey: process.env.VENICE_API_KEY,
    baseURL: 'https://api.venice.ai/api/v1'
});

Account Tiers

API Capabilities

1. Chat Completions

Text inference with multimodal support (text, images, audio, video).

completion = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello!"}
    ]
)

Popular Models:

• llama-3.3-70b – Balanced performance (Tier M, 128K context)
• zai-org-glm-4.7 – Complex tasks, deep reasoning (Tier L, 128K context)
• mistral-31-24b – Vision + function calling (Tier S, 131K context)
• venice-uncensored – No content filtering (Tier S, 32K context)
• deepseek-ai-DeepSeek-R1 – Advanced reasoning, math, coding (Tier L, 64K context)
• qwen3-235b – Massive MoE reasoning (Tier L)
• qwen3-4b – Fast, lightweight (Tier XS, 40K context)

Venice Parameters (via extra_body in Python, direct in JS):

• enable_web_search: “off” | “on” | “auto”
• enable_web_scraping: boolean
• enable_web_citations: boolean — adds ^index^ citation format
• include_venice_system_prompt: boolean (default: true)
• strip_thinking_response: boolean
• disable_thinking: boolean
• character_slug: string
• prompt_cache_key: string — routing hint for cache hits
• prompt_cache_retention: “default” | “extended” | “24h”

See references/chat-completions.md for full parameter reference.

2. Image Generation

Generate images from text prompts.

import requests

response = requests.post(
    "https://api.venice.ai/api/v1/image/generate",
    headers={"Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}"},
    json={
        "model": "venice-sd35",
        "prompt": "A sunset over mountains",
        "width": 1024,
        "height": 1024
    }
)
# Response contains base64 images in images array

Image Models:

3. Image Upscaling

Enhance image resolution 2x or 4x.

import base64

with open("image.jpg", "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode("utf-8")

response = requests.post(
    "https://api.venice.ai/api/v1/image/upscale",
    headers={"Authorization": f"Bearer {api_key}"},
    json={
        "image": image_base64,
        "scale": 4  # 2 or 4
    }
)
# Returns raw image binary
with open("upscaled.png", "wb") as f:
    f.write(response.content)

Pricing: $0.02 (2x), $0.08 (4x)

4. Image Editing (Inpainting)

Modify existing images with AI-powered instructions.

import base64

with open("photo.jpg", "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode("utf-8")

response = requests.post(
    "https://api.venice.ai/api/v1/image/edit",
    headers={"Authorization": f"Bearer {api_key}"},
    json={
        "prompt": "Change the sky to a sunset",
        "image": image_base64  # or URL starting with http/https
    }
)
# Returns raw image binary
with open("edited.png", "wb") as f:
    f.write(response.content)

Model: Uses Qwen-Image. Pricing: ~$0.04/edit.

See references/image-api.md for all parameters and style presets.

5. Video Generation

Async queue-based video generation. Always call /video/quote first for pricing.

Full Workflow:

import requests
import time
import base64

api_key = os.getenv("VENICE_API_KEY")
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

# Step 1: Get price quote
quote = requests.post(
    "https://api.venice.ai/api/v1/video/quote",
    headers=headers,
    json={
        "model": "kling-2.5-turbo-pro-text-to-video",
        "duration": "10s",
        "resolution": "720p",
        "aspect_ratio": "16:9",
        "audio": True
    }
)
print(f"Estimated cost: ${quote.json()['quote']}")

# Step 2: Queue the job (text-to-video)
queue_resp = requests.post(
    "https://api.venice.ai/api/v1/video/queue",
    headers=headers,
    json={
        "model": "kling-2.5-turbo-pro-text-to-video",
        "prompt": "A serene forest with sunlight filtering through trees",
        "negative_prompt": "low quality, blurry",
        "duration": "10s",
        "resolution": "720p",
        "aspect_ratio": "16:9",
        "audio": True
    }
)
queue_id = queue_resp.json()["queueid"]

# Step 3: Poll until complete
while True:
    status_resp = requests.post(
        "https://api.venice.ai/api/v1/video/retrieve",
        headers=headers,
        json={
            "model": "kling-2.5-turbo-pro-text-to-video",
            "queueid": queue_id,
            "delete_media_on_completion": False
        }
    )
    if (status_resp.status_code == 200
            and status_resp.headers.get("Content-Type") == "video/mp4"):
        with open("output.mp4", "wb") as f:
            f.write(status_resp.content)
        print("Video saved!")
        break
    else:
        status = status_resp.json()
        print(f"Status: {status['status']}, Duration: {status['executionDuration']}ms")
        time.sleep(10)

# Step 4: Cleanup (optional — deletes from Venice storage)
requests.post(
    "https://api.venice.ai/api/v1/video/complete",
    headers=headers,
    json={
        "model": "kling-2.5-turbo-pro-text-to-video",
        "queueid": queue_id
    }
)

Image-to-Video:

with open("image.png", "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode("utf-8")

queue_resp = requests.post(
    "https://api.venice.ai/api/v1/video/queue",
    headers=headers,
    json={
        "model": "wan-2.5-preview-image-to-video",
        "prompt": "Animate this scene with gentle motion",
        "image_url": f"data:image/png;base64,{img_b64}",
        "duration": "5s",
        "resolution": "720p"
    }
)

Video Models:

See references/video-api.md for full parameter reference.

6. Text-to-Speech

Convert text to audio with 60+ voices.

response = requests.post(
    "https://api.venice.ai/api/v1/audio/speech",
    headers={"Authorization": f"Bearer {api_key}"},
    json={
        "input": "Hello, welcome to Venice.",
        "model": "tts-kokoro",
        "voice": "af_sky",
        "speed": 1.0,            # 0.25 to 4.0
        "response_format": "mp3"  # mp3, opus, aac, flac, wav, pcm
    }
)
with open("speech.mp3", "wb") as f:
    f.write(response.content)

Voices: af_sky, af_nova, am_liam, bf_emma, zf_xiaobei, jm_kumo, and 50+ more. Pricing: $3.50 per 1M characters.

7. Speech-to-Text

Transcribe audio files.

with open("audio.mp3", "rb") as f:
    response = requests.post(
        "https://api.venice.ai/api/v1/audio/transcriptions",
        headers={"Authorization": f"Bearer {api_key}"},
        files={"file": f},
        data={
            "model": "nvidia/parakeet-tdt-0.6b-v3",
            "response_format": "json",  # json or text
            "timestamps": "true"
        }
    )

Formats: WAV, FLAC, MP3, M4A, AAC, MP4. Pricing: $0.0001 per audio second.

8. Embeddings

Generate vector embeddings for RAG and semantic search.

response = requests.post(
    "https://api.venice.ai/api/v1/embeddings",
    headers={"Authorization": f"Bearer {api_key}"},
    json={
        "model": "text-embedding-bge-m3",
        "input": "Privacy-first AI infrastructure",
        "encoding_format": "float"  # or "base64"
    }
)

9. Vision (Multimodal)

Analyze images with vision-capable models.

response = client.chat.completions.create(
    model="mistral-31-24b",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What is in this image?"},
            {"type": "image_url", "image_url": {"url": "https://..."}}
        ]
    }]
)

10. Function Calling

Define tools for the model to call.

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"]
        }
    }
}]

response = client.chat.completions.create(
    model="zai-org-glm-4.7",
    messages=[{"role": "user", "content": "Weather in SF?"}],
    tools=tools
)

11. Structured Outputs

Get guaranteed JSON schema responses.

response = client.chat.completions.create(
    model="venice-uncensored",
    messages=[...],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "my_response",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {"answer": {"type": "string"}},
                "required": ["answer"],
                "additionalProperties": False
            }
        }
    }
)

Requirements: strict: true, additionalProperties: false, all fields in required.

12. AI Characters

Interact with predefined AI personas.

# List characters
characters = requests.get(
    "https://api.venice.ai/api/v1/characters",
    headers={"Authorization": f"Bearer {api_key}"},
    params={"categories": "philosophy", "limit": 50}
).json()

# Chat with a character
response = client.chat.completions.create(
    model="venice-uncensored",
    messages=[{"role": "user", "content": "What is the meaning of life?"}],
    extra_body={
        "venice_parameters": {"character_slug": "alan-watts"}
    }
)

13. Model Discovery

Query available models and capabilities programmatically.

# List models by type
models = requests.get(
    "https://api.venice.ai/api/v1/models",
    headers={"Authorization": f"Bearer {api_key}"},
    params={"type": "text"}  # text, image, audio, video, embedding
).json()

# Get model traits for auto-selection
traits = requests.get(
    "https://api.venice.ai/api/v1/models/traits",
    params={"type": "text"}
).json()
# e.g. {"default": "zai-org-glm-4.7", "fastest": "qwen3-4b", "uncensored": "venice-uncensored"}

# Use trait as model ID for automatic routing
response = client.chat.completions.create(
    model="fastest",  # Venice routes to the current fastest model
    messages=[...]
)

Error Handling

Error Codes

Abuse Protection

Sending >20 failed requests in 30 seconds triggers a 30-second IP block. Always implement backoff.

Retry with Exponential Backoff (Python)

import time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

def create_venice_session():
    """Create a requests session with automatic retry and backoff."""
    session = requests.Session()
    retry = Retry(
        total=3,
        backoff_factor=1,  # 1s, 2s, 4s
        status_forcelist=[429, 500, 502, 503, 504],
        allowed_methods=["POST", "GET"]
    )
    adapter = HTTPAdapter(max_retries=retry)
    session.mount("https://", adapter)
    return session

session = create_venice_session()
response = session.post(url, json=payload, headers=headers)

Retry with Exponential Backoff (JavaScript)

async function veniceRequest(url, options, maxRetries = 3) {
    for (let attempt = 0; attempt <= maxRetries; attempt++) {
        const response = await fetch(url, options);

        if (response.ok) return response;

        if ([429, 500, 502, 503, 504].includes(response.status)) {
            if (attempt < maxRetries) {
                const delay = Math.pow(2, attempt) * 1000;
                console.log(`Retry ${attempt + 1} in ${delay}ms (status ${response.status})`);
                await new Promise(r => setTimeout(r, delay));
                continue;
            }
        }

        throw new Error(`Venice API error: ${response.status} ${response.statusText}`);
    }
}

Rate Limit-Aware Client (Python)

import time
import requests

class VeniceClient:
    """Wrapper that respects rate limits using response headers."""
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.venice.ai/api/v1"
        self.session = create_venice_session()
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

    def request(self, method, path, **kwargs):
        resp = self.session.request(
            method, f"{self.base_url}{path}",
            headers=self.headers, **kwargs
        )
        remaining = resp.headers.get("x-ratelimit-remaining-requests")
        if remaining and int(remaining) <= 1:
            reset = resp.headers.get("x-ratelimit-reset-requests")
            if reset:
                wait = max(0, float(reset) - time.time())
                time.sleep(wait)
        resp.raise_for_status()
        return resp

Response Headers

Monitor these headers for production:

• x-ratelimit-remaining-requests — Requests left in window
• x-ratelimit-remaining-tokens — Tokens left in window
• x-ratelimit-reset-requests — Timestamp when request count resets
• x-venice-balance-usd — USD balance
• x-venice-balance-diem — DIEM balance
• x-venice-is-blurred — Image was blurred (safe mode)
• x-venice-is-content-violation — Content policy violation
• x-venice-model-deprecation-warning — Deprecation notice
• x-venice-model-deprecation-date — Sunset date
• CF-RAY — Request ID for support

Rate Limits by Model Tier

Text Models:

Other Endpoints:

API Key Management

# Create key programmatically (requires ADMIN key)
curl -X POST https://api.venice.ai/api/v1/api_keys \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"apiKeyType": "INFERENCE", "description": "My App", "consumptionLimit": {"usd": 100}}'

# Check rate limits and balance
curl https://api.venice.ai/api/v1/api_keys/rate_limits \
  -H "Authorization: Bearer $VENICE_API_KEY"

# List keys
curl https://api.venice.ai/api/v1/api_keys \
  -H "Authorization: Bearer $VENICE_API_KEY"

# Delete key
curl -X DELETE "https://api.venice.ai/api/v1/api_keys?id={key_id}" \
  -H "Authorization: Bearer $VENICE_API_KEY"

Reference Files

• references/chat-completions.md — Full chat API parameters
• references/image-api.md — Image generation, editing, upscaling details
• references/video-api.md — Video generation workflow and parameters
• references/models.md — Available models, tiers, pricing, and capabilities