black-forest-labs/FLUX.1-dev

black-forest-labs/FLUX.1-dev is a advanced-tier language model from Hugging Face. black-forest-labs/FLUX.1-dev is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through Hugging Face's API. The FLUX family is Hugging Face's model series for this capability tier.

black-forest-labs/FLUX.1-dev is priced at $0.025 per image. This is a image model billed per image 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 5000ms — better suited to batch or async workloads where latency is less critical.

black-forest-labs/FLUX.1-dev is best suited to image-generation, open-weights. It is a text-only model without vision or function calling support. 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 black-forest-labs/FLUX.1-dev compares to similar models across these dimensions.

black-forest-labs/FLUX.1-dev sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. As an open-weights model, black-forest-labs/FLUX.1-dev can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing black-forest-labs/FLUX.1-dev 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.