Qwen2.5 Coder 32B Instruct vs Google: Gemma 4 26B A4B : Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen2.5 Coder 32B Instruct (qwen/qwen-2.5-coder-32b-instruct) vs Google: Gemma 4 26B A4B (google/gemma-4-26b-a4b-it). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen2.5 Coder 32B Instruct (qwen/qwen-2.5-coder-32b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen2.5 Coder 32B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.33/1M) or route via asynchronous Batch API for 50% off standard token rates.
Google: Gemma 4 26B A4B (google/gemma-4-26b-a4b-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.
In Etuki October 2026 FinOps telemetry comparing Qwen2.5 Coder 32B Instruct (API ID: qwen/qwen-2.5-coder-32b-instruct) vs Google: Gemma 4 26B A4B (API ID: google/gemma-4-26b-a4b-it), Google: Gemma 4 26B A4B offers lower standard input pricing at $0.09/1M tokens ($0.3/1M output), while Google: Gemma 4 26B A4B leads in cached prefix economics at $0.045/1M tokens (50% discount). Qwen2.5 Coder 32B Instruct 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 | Qwen2.5 Coder 32B Instruct | Google: Gemma 4 26B A4B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen-2.5-coder-32b-instruct | google/gemma-4-26b-a4b-it | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.66 / $1 | $0.09 / $0.3 | Google: Gemma 4 26B A4B |
| Cache Read Price (per 1M) | $0.33/1M (50% Off) | $0.045/1M (50% Off) | Google: Gemma 4 26B A4B |
| Context Window / Max Output | 32K In / 29K Out | 256K In / 230K Out | Google: Gemma 4 26B A4B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen2.5 Coder 32B Instruct (-0ms) |
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Sig
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — deli
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 Qwen2.5 Coder 32B Instruct to Google: Gemma 4 26B A4B
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — deli Verified P90 latency of 280ms.