How much does Pika 2.0 (Official) cost via Railwail?

Per-call: €0.20. No monthly minimum, no subscription. Start with €5 free credits.

What is the context window of Pika 2.0 (Official)?

Pika 2.0 (Official) supports a unknown context window — enough for typical AI workloads.

How fast is Pika 2.0 (Official)?

Latency depends on prompt length and load — typically 200ms to 2s for short prompts. We measure p50/p95 in real-time on /rankings.

Is Pika 2.0 (Official) better than Google Veo 2?

It depends on your use case. Pika 2.0 (Official) (Pika) and Google Veo 2 (Google DeepMind) are both strong choices in video generation. Compare them side-by-side at /compare/pika-2-official-vs-google-veo-2.

Does Pika 2.0 (Official) support image input (vision)?

Yes — Pika 2.0 (Official) accepts image inputs in addition to text. Send images via the standard OpenAI-compatible `messages` array with `image_url` content blocks. Supported formats: text, image.

Pika 2.0 (Official)

Name: Pika 2.0 (Official)
Brand: Custom
SKU: pika-2-official
Price: 0.2 EUR
Availability: InStock

Pika

Video Generation

Pika Labs' 2.0 release. Cinematic text/image-to-video with scene composition controls.

Queue video with Pika 2.0 (Official)

Video generation runs asynchronously — we'll queue a job and you can track it in your history.

Generates as an async job — typically 30 s to 2 min.

TL;DR·Last updated May 16, 2026

Pika 2.0 (Official) is video generation AI model from Pika, priced at €0.000 per 1M input tokens with a unknown context window.

Try Pika 2.0 (Official)

Prompt

Duration

Aspect Ratio

Direct API access coming soon

Pricing

Price per Generation

Per generation€0.20

API Integration

Use our OpenAI-compatible API to integrate Pika 2.0 (Official) into your application.

Install

npm install railwail

JavaScript / TypeScript

import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

// Simple — just pass a string
const reply = await rw.run("pika-2-official", "Hello! What can you do?");
console.log(reply);

// With message history
const reply2 = await rw.run("pika-2-official", [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);

// Full response with usage info
const res = await rw.chat("pika-2-official", [
  { role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);

Specifications

Price

€0.20

Developer

Pika

Deep dive — Pika Labs's Pika 2.0 (Official)

About Pika Labs

Founded 2022 · Palo Alto, USA

Pika Labs was founded in 2022 by Demi Guo (CEO) and Chenlin Meng (CTO), both Stanford PhD candidates in generative AI (Meng is a co-author of foundational diffusion-model papers including SDEdit and Diffusion ODE samplers). The company shipped its initial Discord-based product in mid-2023, launched the Pika web app and Pika 1.0 in November 2023, and rolled out Pika 2.0 in late 2024 with Scene Ingredients (compose a clip from several reference images) and Pikadditions / Pikaffects (object insertion and visual effects). Investors include Lightspeed Venture Partners, Spark Capital, Homebrew and Greycroft, with total funding above $135M and a $470M valuation in 2024. Pika is widely used by short-form social-media creators.

Visit Pika Labs →

Architecture

Latent video diffusion model (transformer denoiser)

Pika 2.0 is a closed latent-video-diffusion model with a transformer-based denoiser operating on a learned spatio-temporal latent space. Conditioning includes a multilingual text encoder, image embeddings for image-to-video, and the headline 'Scene Ingredients' system that composes a single output clip from multiple reference images of distinct subjects, props and scenes -- a richer multimodal-conditioning surface than Pika 1.0. The model generates 5-second clips at 720p with optional upscaling, and supports special-effect modes (Pikaffects: melt, squish, levitate, etc.) trained as conditioned variations of the base diffusion model. Pika has published no formal paper; product behaviour and team papers (e.g., Meng's diffusion sampler work) suggest a DiT-style architecture with classifier-free guidance and a curated training corpus of stock and web video.

Parameters: Undisclosed
Context: unknown

What it can do