StepFun: Step 3.5 Flash vs Qwen: Qwen3 14B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing StepFun: Step 3.5 Flash (stepfun/step-3.5-flash) vs Qwen: Qwen3 14B (qwen/qwen3-14b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
StepFun: Step 3.5 Flash (stepfun/step-3.5-flash) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for StepFun: Step 3.5 Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.05/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3 14B (qwen/qwen3-14b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 14B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.06/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing StepFun: Step 3.5 Flash (API ID: stepfun/step-3.5-flash) vs Qwen: Qwen3 14B (API ID: qwen/qwen3-14b), StepFun: Step 3.5 Flash offers lower standard input pricing at $0.1/1M tokens ($0.3/1M output), while StepFun: Step 3.5 Flash leads in cached prefix economics at $0.05/1M tokens (50% discount). StepFun: Step 3.5 Flash 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 | StepFun: Step 3.5 Flash | Qwen: Qwen3 14B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | stepfun/step-3.5-flash | qwen/qwen3-14b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.3 | $0.12 / $0.24 | StepFun: Step 3.5 Flash |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.06/1M (50% Off) | StepFun: Step 3.5 Flash |
| Context Window / Max Output | 256K In / 64K Out | 40K In / 16K Out | StepFun: Step 3.5 Flash |
| P90 First-Token / Edge Latency | 280ms | 280ms | StepFun: Step 3.5 Flash (-0ms) |
Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parame
Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between
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 StepFun: Step 3.5 Flash to Qwen: Qwen3 14B
Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between Verified P90 latency of 280ms.