Recursive: Flint (free) vs Qwen: Qwen3.5-Flash: Live Promos, Pricing, Hidden Fees & Latency
Recursive: Flint (free) (recursive/flint-v1.0:free) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Recursive: Flint (free): Activate ephemeral prefix caching to slash input costs by 50% (down to 0/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-Flash (qwen/qwen3.5-flash-02-23) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to 0.013/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Recursive: Flint (free) (API ID: recursive/flint-v1.0:free) vs Qwen: Qwen3.5-Flash (API ID: qwen/qwen3.5-flash-02-23), Recursive: Flint (free) offers lower standard input pricing at $0/1M tokens ($0/1M output), while Recursive: Flint (free) leads in cached prefix economics at $0/1M tokens (50% discount). Recursive: Flint (free) achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
Auto-discovered via live global telemetry registry.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | Recursive: Flint (free) | Qwen: Qwen3.5-Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | recursive/flint-v1.0:free | qwen/qwen3.5-flash-02-23 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0 / $0 | $0.065 / $0.26 | Recursive: Flint (free) |
| Cache Read Price (per 1M) | $0/1M (50% Off) | $0.013/1M (50% Off) | Recursive: Flint (free) |
| Context Window / Max Output | 256K In / 230K Out | 977K In / 64K Out | Recursive: Flint (free) |
| P90 First-Token / Edge Latency | 280ms | 280ms | Recursive: Flint (free) (-0ms) |
Flint is an open-weight mixture-of-experts model from Recursive, with about 2.5B active parameters out of 40B total, built by Recursive's autoresearch system Eureka Machine for cod
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi
Real-Time Stack Cost & Hidden-Fee Simulator (MAU / Token / Egress / GMV)
Calibrated with Reddit r/FinOps & r/devops telemetry: Includes Context Caching, Bandwidth Egress, and MoR Tax
🛰️Switch from Recursive: Flint (free) to Qwen: Qwen3.5-Flash
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi Verified P90 latency of 280ms.