Qwen: Qwen3.5 397B A17B vs Fireworks: Ember-1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5 397B A17B (qwen/qwen3.5-397b-a17b) vs Fireworks: Ember-1 (fireworks/ember-1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5 397B A17B (qwen/qwen3.5-397b-a17b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5 397B A17B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
Fireworks: Ember-1 (fireworks/ember-1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Fireworks: Ember-1: Activate ephemeral prefix caching to slash input costs by 50% (down to $1.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5 397B A17B (API ID: qwen/qwen3.5-397b-a17b) vs Fireworks: Ember-1 (API ID: fireworks/ember-1), Qwen: Qwen3.5 397B A17B offers lower standard input pricing at $0.5/1M tokens ($3.6/1M output), while Qwen: Qwen3.5 397B A17B leads in cached prefix economics at $0.25/1M tokens (50% discount). Qwen: Qwen3.5 397B A17B 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 | Qwen: Qwen3.5 397B A17B | Fireworks: Ember-1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-397b-a17b | fireworks/ember-1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $3.6 | $3 / $15 | Qwen: Qwen3.5 397B A17B |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $1.5/1M (50% Off) | Qwen: Qwen3.5 397B A17B |
| Context Window / Max Output | 256K In / 230K Out | 1024K In / 922K Out | Fireworks: Ember-1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5 397B A17B (-0ms) |
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, ach
Ember-1 is a specialized reasoning model from Fireworks Research, built on [Kimi K3](https://openrouter.ai/moonshotai/kimi-k3). It is designed to make every token go further: it pr
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 Qwen: Qwen3.5 397B A17B to Fireworks: Ember-1
Ember-1 is a specialized reasoning model from Fireworks Research, built on [Kimi K3](https://openrouter.ai/moonshotai/kimi-k3). It is designed to make every token go further: it pr Verified P90 latency of 280ms.