MiniMax: MiniMax M2-her vs Google: Gemma 4 31B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MiniMax: MiniMax M2-her (minimax/minimax-m2-her) vs Google: Gemma 4 31B (google/gemma-4-31b-it). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MiniMax: MiniMax M2-her (minimax/minimax-m2-her) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MiniMax: MiniMax M2-her: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.15/1M) or route via asynchronous Batch API for 50% off standard token rates.
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.
In Etuki October 2026 FinOps telemetry comparing MiniMax: MiniMax M2-her (API ID: minimax/minimax-m2-her) vs Google: Gemma 4 31B (API ID: google/gemma-4-31b-it), Google: Gemma 4 31B offers lower standard input pricing at $0.2/1M tokens ($0.4/1M output), while Google: Gemma 4 31B leads in cached prefix economics at $0.1/1M tokens (50% discount). MiniMax: MiniMax M2-her 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 | MiniMax: MiniMax M2-her | Google: Gemma 4 31B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | minimax/minimax-m2-her | google/gemma-4-31b-it | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $1.2 | $0.2 / $0.4 | Google: Gemma 4 31B |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $0.1/1M (50% Off) | Google: Gemma 4 31B |
| Context Window / Max Output | 64K In / 2K Out | 256K In / 16K Out | Google: Gemma 4 31B |
| P90 First-Token / Edge Latency | 280ms | 280ms | MiniMax: MiniMax M2-her (-0ms) |
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in
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
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 MiniMax: MiniMax M2-her to Google: Gemma 4 31B
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 Verified P90 latency of 280ms.