Where to start
Do not infer serving cost from the active parameter count. Measure your exact deployment with representative image sizes, prompt lengths and simultaneous requests.
- Multimodal tasks
- General assistance
- Open-weight evaluation
What to check before deployment
Its total expert weights differ from Scout even though the active-parameter label is similar. Review deployment and license requirements.
Confirm the exact model ID, checkpoint revision and endpoint in your test record. A base model name does not establish a provider's quantization, accepted inputs, context limit or current availability.
Measure useful results
Build a small evaluation from the workflow you already run. Define the expected answer before testing. Track correct results, formatting errors, retries, complete request time and reported input and output usage.
Count rejected answers and manual corrections. A low token rate can still produce a high cost per accepted result when the workflow needs retries or repair.
Estimate your token budget →Sources and scope
This page is an editorial evaluation guide, not a benchmark result or a claim that this model is served by Flonno.
Official publisher model card ↗License / access: other. Read the linked license or service terms for the full conditions. Model card identity and license metadata checked 2026-10-10.