Morph: Morph V3 Large vs Qwen: Qwen3.8 2.4T A95B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Morph: Morph V3 Large (morph/morph-v3-large) vs Qwen: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Morph: Morph V3 Large (morph/morph-v3-large) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Morph: Morph V3 Large: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.45/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.8 2.4T A95B (qwen/qwen3.8-2.4t-a95b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.8 2.4T A95B: 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.
In Etuki October 2026 FinOps telemetry comparing Morph: Morph V3 Large (API ID: morph/morph-v3-large) vs Qwen: Qwen3.8 2.4T A95B (API ID: qwen/qwen3.8-2.4t-a95b), Morph: Morph V3 Large offers lower standard input pricing at $0.9/1M tokens ($1.9/1M output), while Morph: Morph V3 Large leads in cached prefix economics at $0.45/1M tokens (50% discount). Morph: Morph V3 Large 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 | Morph: Morph V3 Large | Qwen: Qwen3.8 2.4T A95B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | morph/morph-v3-large | qwen/qwen3.8-2.4t-a95b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.9 / $1.9 | $2 / $6 | Morph: Morph V3 Large |
| Cache Read Price (per 1M) | $0.45/1M (50% Off) | $1/1M (50% Off) | Morph: Morph V3 Large |
| Context Window / Max Output | 256K In / 128K Out | 1024K In / 128K Out | Morph: Morph V3 Large |
| P90 First-Token / Edge Latency | 280ms | 280ms | Morph: Morph V3 Large (-0ms) |
Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out
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 Morph: Morph V3 Large to Qwen: Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out Verified P90 latency of 280ms.