Google prices Gemini 3.6 Flash lower than 3.5 Flash, at $1.50/1M input tokens and $7.50/1M output tokens, and Gemini 3.5 Flash-Lite at $0.30/1M and $2.50/1M
and Gemini 3.5 Pro has enough anti-hype around it already [embedded post]
9to5GoogleAbner Li
Context & Ripple Effects
The coverage trail records the same Gemini 3.6 rate card a day earlier, while May’s Gemini 3.5 Flash pricing had set output-token pricing at $9 per million. The latest schedule therefore reverses part of that increase without raising the input rate.
Google has repeatedly segmented Flash around lower cost and speed, from the original lightweight Gemini 1.5 Flash to smaller variants. The new Flash-Lite tier continues that product-line separation at a markedly lower entry price.
First-order effects
Gemini 3.6 Flash customers keep a $1.50-per-million input-token rate while output tokens fall from $9 to $7.50 versus Gemini 3.5 Flash, directly reducing bills for output-heavy workloads.
Gemini 3.5 Flash-Lite is listed at $0.30 per million input tokens and $2.50 per million output tokens, giving cost-sensitive users a distinct lower-price option within the Gemini family.
Second-order effects
Developers that route requests among Gemini models can revisit those rules: the lower 3.6 Flash output price narrows the cost penalty for workloads that need its tier rather than Flash-Lite.
The clearer price spread between Flash and Flash-Lite makes effective inference cost, not just model naming, a more explicit purchasing criterion for Google’s API customers.
Third-order effects
If repeated across model updates, this pricing pattern would make AI API competition increasingly about maintaining differentiated performance tiers while lowering the marginal cost of serving generated output.
It also reinforces model routing as an application-layer capability: buyers may increasingly combine premium and lightweight models rather than standardize on one endpoint.
The trend: This is one data point in the shift toward tiered AI inference pricing, where providers use cheaper lightweight models and selective price cuts to expand API usage.
Gemini 3.6 Flash benchmarks are out, and it's... beaten by other models on code tasks, and is only really consistently SoTA on vision and context benchmarks. But hey, 3.1 Pro is now so old 3.6 Flash outperforms it across the board 😭 [image]
While I didn't have early access to this one, I'm excited to try it! Big Gemini 3.5 Flash fan, but GPT 5.6 Luna stole me away because it was cheaper and faster Gemini 3.6 Flash seems to maybe bring it back to Gemini!
A lot of people are saying Google is falling behind after Gemini 3.6 Flash. I think they're reading it the wrong way. To me, Google has changed its strategy. Yes, Gemini is behind GPT-5.6 Luna, Grok 4.5, and Claude Sonnet 5 in coding. But it leads in computer use, visual
Gemini 3.6 Flash is live in Antigravity! ⚡️ Building on 3.5 Flash feedback, it consumes up to 17% fewer output tokens while completing complex workflows in fewer reasoning steps and tool calls. [image]
Frontier labs don't generally announce when they are starting pre-training. Among other things it serves as a kind of anti-hype for your current-generation models — and Gemini 3.5 Pro has enough anti-hype around it already [embedded post]
Introducing Gemini 3.6 Flash and 3.5 Flash-Lite! ⚡3.6 Flash fixes key issues and improves token efficiency, all at a lower price point than 3.5 Flash 💡3.5 Flash-Lite is a major leap over 3.1 FL, making it the perfect choice for ultra-cheap, high-throughput tasks
⚡ Google built an AI that finds and patches software vulnerabilities on its own, and it's capable enough that Google won't release it publicly. In testing, “Gemini 3.5 Flash Cyber” found more new bugs than rival models and even wrote a working exploit that slipped past standard […
GOOGLE JUST LAUNCHED THREE NEW GEMINI MODELS. Google $GOOGL announced Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber. Pricing: Gemini 3.6 Flash: $1.50 per million input tokens, $7.50 per million output tokens Gemini 3.5 Flash-Lite: $0.30 per million input [im…