SpaceXAI: Grok 4.5 vs Qwen: Qwen3.5-122B-A10B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing SpaceXAI: Grok 4.5 (x-ai/grok-4.5) vs Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
SpaceXAI: Grok 4.5 (x-ai/grok-4.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for SpaceXAI: Grok 4.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) 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 SpaceXAI: Grok 4.5 (API ID: x-ai/grok-4.5) 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). SpaceXAI: Grok 4.5 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 | SpaceXAI: Grok 4.5 | Qwen: Qwen3.5-122B-A10B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | x-ai/grok-4.5 | qwen/qwen3.5-122b-a10b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2 / $6 | $0.26 / $2.08 | Qwen: Qwen3.5-122B-A10B |
| Cache Read Price (per 1M) | $1/1M (50% Off) | $0.13/1M (50% Off) | Qwen: Qwen3.5-122B-A10B |
| Context Window / Max Output | 488K In / 439K Out | 256K In / 64K Out | SpaceXAI: Grok 4.5 |
| P90 First-Token / Edge Latency | 280ms | 280ms | SpaceXAI: Grok 4.5 (-0ms) |
Grok 4.5 is a model from SpaceXAI with frontier performance on coding, knowledge work, and STEM.
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 SpaceXAI: Grok 4.5 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.