Google: Gemma 4 31B vs Qwen: Qwen3.5-9B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Google: Gemma 4 31B (google/gemma-4-31b-it) vs Qwen: Qwen3.5-9B (qwen/qwen3.5-9b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Google: Gemma 4 31B (google/gemma-4-31b-it) — Google AI Startup Credits ($350K) & 50% Cache Promo
Google DeepMind is currently subsidizing Gemini 4 Argon at 50% off standard rates ($2.00/1M input & $10.00/1M output instead of $4/$20) until November 30, 2026, alongside 95% off Context Caching ($0.10/1M) and up to $350,000 in Google for Startups Cloud/Vertex AI credits.
Qwen: Qwen3.5-9B (qwen/qwen3.5-9b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-9B: 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.
In Etuki October 2026 FinOps telemetry comparing Google: Gemma 4 31B (API ID: google/gemma-4-31b-it) vs Qwen: Qwen3.5-9B (API ID: qwen/qwen3.5-9b), Qwen: Qwen3.5-9B offers lower standard input pricing at $0.1/1M tokens ($0.15/1M output), while Qwen: Qwen3.5-9B leads in cached prefix economics at $0.05/1M tokens (50% discount). Google: Gemma 4 31B 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 | Google: Gemma 4 31B | Qwen: Qwen3.5-9B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | google/gemma-4-31b-it | qwen/qwen3.5-9b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.2 / $0.4 | $0.1 / $0.15 | Qwen: Qwen3.5-9B |
| Cache Read Price (per 1M) | $0.1/1M (50% Off) | $0.05/1M (50% Off) | Qwen: Qwen3.5-9B |
| Context Window / Max Output | 256K In / 16K Out | 256K In / 32K Out | Qwen: Qwen3.5-9B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Google: Gemma 4 31B (-0ms) |
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architectur
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 Google: Gemma 4 31B to Qwen: Qwen3.5-9B
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architectur Verified P90 latency of 280ms.