Gemini 3.5 Flash: Release, Pricing, and Tooling

Google has released Gemini 3.5 Flash with pricing that is 3x higher than Gemini 3 Flash Preview and 6x higher than Gemini 3.1 Flash-Lite, landing at $1.50/million input tokens and $9/million output tokens [1]. Despite the cost increase, Google is deploying the model across its flagship consumer and enterprise products, including the Gemini app and AI Mode in Google Search [1]. Benchmark testing found that running Gemini 3.5 Flash (high) cost $1,551.60 — more than the $892.28 for Gemini 3.1 Pro Preview [1]. Tooling support followed quickly: the llm CLI plugin gained a gemini-3.5-flash identifier in version 0.32, and an alpha (0.32a0) added streaming reasoning token support [2][3].

Gemini 3.5 Flash's pricing nearly matches Gemini 3.1 Pro despite carrying the 'Flash' (efficiency-tier) branding, blurring the traditional cost/capability segmentation [1]. Analyst commentary frames this as part of a coordinated industry shift: all three major AI labs appear to be simultaneously testing how much API customers will pay, signaling a possible end to the race-to-zero pricing dynamic that defined early LLM competition [1].

Google announced Gemini 3.5 Flash as its latest efficiency-tier model, but the pricing tells a different story than the 'Flash' branding implies. At $1.50 per million input tokens and $9 per million output tokens, Gemini 3.5 Flash costs 3x more than Gemini 3 Flash Preview and 6x more than Gemini 3.1 Flash-Lite [1]. Crucially, those figures nearly match Gemini 3.1 Pro ($2/$12), collapsing the price gap between Google's mid-tier and pro offerings. An independent benchmark run illustrated the real-world impact: the Artificial Analysis benchmark cost $1,551.60 on Gemini 3.5 Flash (high), compared to just $892.28 for Gemini 3.1 Pro Preview [1].

At the same time, Google announced it is deploying Gemini 3.5 Flash across its highest-traffic consumer and enterprise surfaces, including the Gemini app and AI Mode in Google Search [1]. This creates an immediate tension: a more expensive model is being rolled into products that are free to end users, raising questions about how Google is accounting for the cost differential and what signal this sends to API customers being asked to absorb higher prices.

Observer Simon Willison frames the pricing shift as part of a broader industry realignment rather than a Google-specific move, noting that Google, OpenAI, and Anthropic all appear to be testing the price tolerance of their API customers simultaneously [1]. This suggests the aggressive discounting that characterized earlier LLM competition may be giving way to a new phase of margin recapture across the industry.

On the tooling side, the llm CLI plugin for Gemini (maintained by the open-source community) moved quickly to add support for the new model. Version 0.32 added the gemini-3.5-flash model identifier [2], and a concurrent alpha release (0.32a0) introduced the ability to stream reasoning tokens from Gemini models, requiring llm>=0.32a0 to function [3]. These releases are incremental infrastructure updates rather than capability analysis, but they mark the point at which Gemini 3.5 Flash became accessible via the standard llm CLI workflow.