Runway
Gen-4 video generation API — cinematic quality text-to-video and image-to-video
Runway is a creative AI company best known for its Gen series of video generation models. Gen-4 and Gen-4 Turbo are available via API, producing cinematic-quality video from text or image prompts. Runway is widely used in professional film and advertising production. Pricing is per second of generated video.
Gen-4 is Runway's cinematic-quality video generation model, trusted by professional film and advertising studios — with Gen-4 Turbo offering a faster, lower-cost alternative via the same API.
- Gen-4 produces cinematic-quality video used in professional film and advertising
- Gen-4 Turbo at $0.050/second offers faster generation at lower cost
- Strong motion quality and temporal consistency
- Established track record in professional creative production
- Supports text-to-video, image-to-video, and video-to-video
- Video generation only — no text, image, or audio LLM models
- Gen-4 at $0.100/second is among the higher-priced video generation APIs
- Generation latency is higher than text inference
Runway Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
gen4-turbo Gen-4 | Video | advanced | $0.05 per second EST Aug 2026 | — | 60.0s | 0 t/s | — | 0 | ||
gen4 Gen-4 | Video | frontier | $0.10 per second EST Aug 2026 | — | 90.0s | 0 t/s | — | 0 |
Runway — Frequently Asked Questions (FAQs)
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Runway is a proprietary AI lab that develops and hosts its own large language models. Runway is a creative AI company best known for its Gen series of video generation models. Gen-4 and Gen-4 Turbo are available via API, producing cinematic-quality video from text or image prompts. Runway is widely used in professional film and advertising production. Pricing is per second of generated video. Common use cases include Video generation, Film production, Advertising, Creative content. Headquartered in New York, NY, founded 2018. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Runway vs other LLM providers
Runway competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Gen-4 is Runway's cinematic-quality video generation model, trusted by professional film and advertising studios — with Gen-4 Turbo offering a faster, lower-cost alternative via the same API. Use the LLM provider comparison tool to see Runway token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Runway?
Runway stands out because: Gen-4 is Runway's cinematic-quality video generation model, trusted by professional film and advertising studios — with Gen-4 Turbo offering a faster, lower-cost alternative via the same API.. The API currently offers 2 models, spanning 1 frontier, 1 advanced tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Runway token pricing
Runway charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Runway's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Runway context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Runway's largest context window is 0 tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Runway makes sense
Managed inference APIs like Runway eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Runway API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Runway token price data is available in the LLM price history charts.
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