Mancer: Weaver (alpha) vs Qwen: Qwen3.5-122B-A10B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Mancer: Weaver (alpha) (mancer/weaver) vs Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Mancer: Weaver (alpha) (mancer/weaver) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Mancer: Weaver (alpha): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.2/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-122B-A10B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.13/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Mancer: Weaver (alpha) (API ID: mancer/weaver) vs Qwen: Qwen3.5-122B-A10B (API ID: qwen/qwen3.5-122b-a10b), Qwen: Qwen3.5-122B-A10B offers lower standard input pricing at $0.26/1M tokens ($2.08/1M output), while Qwen: Qwen3.5-122B-A10B leads in cached prefix economics at $0.13/1M tokens (50% discount). Mancer: Weaver (alpha) 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 | Mancer: Weaver (alpha) | Qwen: Qwen3.5-122B-A10B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | mancer/weaver | qwen/qwen3.5-122b-a10b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.4 / $0.75 | $0.26 / $2.08 | Qwen: Qwen3.5-122B-A10B |
| Cache Read Price (per 1M) | $0.2/1M (50% Off) | $0.13/1M (50% Off) | Qwen: Qwen3.5-122B-A10B |
| Context Window / Max Output | 8K In / 6K Out | 256K In / 64K Out | Qwen: Qwen3.5-122B-A10B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Mancer: Weaver (alpha) (-0ms) |
An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations.
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving
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 Mancer: Weaver (alpha) to Qwen: Qwen3.5-122B-A10B
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving Verified P90 latency of 280ms.