Luma AI
Dream Machine — high-quality video generation from text and image prompts
Luma AI develops Dream Machine, a video generation model accessible via API. Dream Machine v1.6 generates high-quality video clips from text or image prompts. The 4K variant produces higher-resolution output. Pricing is per second of generated video rather than per token.
Dream Machine v1.6 delivers high-quality text-to-video and image-to-video generation via a simple per-second API — with a 4K variant for production-quality output.
- Dream Machine v1.6 produces high-quality, temporally consistent video clips
- Supports both text-to-video and image-to-video generation
- 4K variant available for higher-resolution output
- Simple per-second pricing model
- Video generation only — no text, image, or audio models
- Per-second pricing can be expensive for long clips
- Generation latency is higher than text inference
- Maximum clip length limited compared to some competitors
Luma AI Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
dream-machine-v1.6 Dream Machine | Video | frontier | $0.19 per second EST Aug 2026 | — | 90.0s | 0 t/s | — | 0 | ||
dream-machine-v1.6-4k Dream Machine | Video | frontier | $0.75 per second EST Aug 2026 | — | 120.0s | 0 t/s | — | 0 |
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Luma AI is a proprietary AI lab that develops and hosts its own large language models. Luma AI develops Dream Machine, a video generation model accessible via API. Dream Machine v1.6 generates high-quality video clips from text or image prompts. The 4K variant produces higher-resolution output. Pricing is per second of generated video rather than per token. Common use cases include Video generation, Creative content, Marketing assets, Prototyping video concepts. Headquartered in San Francisco, CA, founded 2021. 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.
Luma AI vs other LLM providers
Luma AI 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. Dream Machine v1.6 delivers high-quality text-to-video and image-to-video generation via a simple per-second API — with a 4K variant for production-quality output. Use the LLM provider comparison tool to see Luma AI 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 Luma AI?
Luma AI stands out because: Dream Machine v1.6 delivers high-quality text-to-video and image-to-video generation via a simple per-second API — with a 4K variant for production-quality output.. The API currently offers 2 models, spanning 2 frontier 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 Luma AI token pricing
Luma AI 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 Luma AI'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.
Luma AI 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. Luma AI'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 Luma AI makes sense
Managed inference APIs like Luma AI 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 Luma AI 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 Luma AI token price data is available in the LLM price history charts.
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