dream-machine-v1.6

dream-machine-v1.6 is a frontier-tier language model from Luma AI. dream-machine-v1.6 is a proprietary model available exclusively through Luma AI's API. The Dream Machine family is Luma AI's model series for this capability tier.

dream-machine-v1.6 is priced at $0.19 per second. This is a video model billed per second rather than per token — token input/output pricing does not apply.

Its 0-token context window is standard for this tier — adequate for most chat and completion tasks, but may require chunking for long-document workloads. Median latency (P50 time-to-first-token) is approximately 90000ms — better suited to batch or async workloads where latency is less critical.

dream-machine-v1.6 is best suited to video-generation, quality, text-to-video. Supported capabilities include vision inputs (images alongside text). Vision support enables document analysis, screenshot understanding, diagram interpretation, and visual question answering — images are converted to tokens at the standard input rate based on resolution. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how dream-machine-v1.6 compares to similar models across these dimensions.

dream-machine-v1.6 sits in the frontier tier — the highest-capability models available, used when quality is the primary constraint and cost is secondary. Unlike open-weights alternatives, dream-machine-v1.6 is only available via Luma AI's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing dream-machine-v1.6 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (0/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.