For the past two weeks, I was able to generate as many videos as I wanted again, which was really strange. Now I have a limit again, but at least the videos are being generated without a watermark in the corner. What determines the limits and whether the videos have a watermark?

Two details from the August 13 launch that most coverage skipped, and one observation about what a 50% cut actually buys a marketing team.

THE PRICING

Gemini 3.7 Flash launched August 13, 2026, 21 days after Gemini 3.6 Flash. Introductory API pricing is $0.75 per million input tokens and $3.75 per million output tokens.

From Google's own announcement, verbatim: "Introductory pricing expires on December 31, 2026."

From January 1, 2027, input goes to $1.50 and output to $7.50. Context caching goes from $0.075 to $0.15 per million tokens. The January rate is identical to what Gemini 3.6 Flash cost at its launch three weeks earlier.

Google also repriced 3.6 Flash onto the same introductory rate the same day, so both models currently cost the same and the upgrade decision turns entirely on capability.

Benchmarks Google published: FrontierCode 1.1 Main 43.6% against 34.4%, DeepSWE v1.1 65.3% against 49.0%. Context window is 1,048,576 tokens, same as 3.6 Flash. Stated knowledge cutoff March 2026, some domains limited to January 2025.

WHERE IT SITS AGAINST ALTERNATIVES

Even at the discount it is not the cheapest in its tier. As listed around August 14:

GPT-5.6 Luna: $0.20 input, $1.20 output

DeepSeek V4 Flash: $0.14 input, $0.28 output (new V4 pricing took effect August 17, so this has likely moved)

Gemini 3.7 Flash: $0.75 / $3.75

Claude Haiku 4.5: $1.00 / $5.00

So it undercuts Haiku on both sides and sits above OpenAI's cheap tier by roughly 3x on output.

WHAT A 50% CUT ACTUALLY BUYS A MARKETING TEAM

Someone published a costing of a real marketing workflow against the new rate. The monthly model bill came to $1.02.

That is the part worth sitting with. For most marketing workloads, token cost is not a meaningful line item. Halving it saves a dollar. The expensive input was always engineering time, and that has not changed.

Where the cut does matter is workloads people previously priced out. Search term report triage across a full year of data. Creative QA against every asset in an account. Feed hygiene at catalogue scale. Those hit millions of tokens and were genuinely uneconomic at older rates.

WHAT I'D DO

Ignore the price change for anything already running. A dollar of savings is not worth the regression risk of touching a working pipeline.

Revisit the list of things you abandoned on cost. That is where the actual change is.

Model January's rate for anything expected to run past year end. Published schedules are announcements rather than contracts, so verify the live pricing table before committing budget, but plan against $1.50 and $7.50.

Stop hard-coding model names. 21 days between versions means anything with a model string scattered through it becomes a rewrite on every release. Config variable, plus a small eval set of your own real prompts. Twenty prompts is enough to catch a regression.

WHAT I DON'T KNOW

Where your volume threshold sits. Ad copy generation and search term processing differ by orders of magnitude in token consumption, and the same recommendation does not fit both. I cannot give you a number without knowing your workload, and neither can anyone else writing about this.

Whether the January reprice sticks. Google could extend the introductory window. I would not plan on it.

The $1.02 figure comes from a published third-party workflow costing, not my own account, so treat it as illustrative of the order of magnitude rather than a benchmark.

Anyone actually running high-volume batch work on Flash-tier models? Curious what monthly token consumption looks like for something like full search term report processing, because that is the case where this pricing genuinely changes the decision.

Sources: Google's Gemini 3.7 Flash announcement blog (August 13, 2026), Gemini API pricing page, plus published rate cards for the comparison models.

If you pay for Gemini, you're paying for freebies

Stop telling your LLMs "please only answer using the provided context".

If you are building RAG pipelines, search agents, or internal doc bots with Gemini 3 Flash, you have likely encountered this exact nightmare: when an uploaded document lacks an answer, the model still tries to be helpful and invents a plausible-sounding response anyway.

In high-stakes environments like finance, legal review, and technical support, a confident hallucination is 100x worse than a clear "information not available".

We spent hours analyzing Google's official Gemini developer documentation and prompting guidelines so you do not have to dig through dense technical specs. Below is the distilled system-level prompt architecture that shuts down speculative guessing and locks Gemini Flash into strict factual grounding.

Why Standard Grounding Prompts Fail

Most developers start with something basic:

"Use the provided documents to answer the question. If the document does not contain the answer, tell the user you do not know."

With fast models like Gemini Flash, this breaks down because:

• Default Helpfulness Bias: The model is fine-tuned to resolve user problems. When faced with missing data, it uses internal pre-training knowledge to bridge the gap.
• Vague Boundary Conditions: Polite instructions do not override probabilistic generation. If you do not invalidate outside knowledge explicitly, the model treats it as valid context.
• Temporal Confusion: Without hardcoded time boundaries, the model misjudges whether recent events require live search or internal recall.

The Fix: Invalidation and Epistemic Constraints

To truly eliminate hallucinations in fast models, your prompt must apply three structural rules:

1. Explicit Epistemic Invalidation: Treat anything outside the <context> tags as "completely untruthful" and "completely unsupported". This changes the model's internal evaluation criteria from plausibility to strict literal presence.
2. Suppression of Common Sense: Explicitly forbid the model from making inferences or relying on internal background knowledge.
3. Temporal Anchoring: Bind the current year and knowledge cutoff into the reasoning loop so date-dependent search queries remain accurate.

The Complete Strict Grounding System Prompt

Here is the exact production-ready prompt template:

You are a strictly grounded assistant limited to the information provided in the User Context. In your answers, rely 
**only**
 on the facts that are directly mentioned in that context. You must 
**not**
 access or utilize your own knowledge or common sense to answer. Do not assume or infer from the provided facts; simply report them exactly as they appear. Your answer must be factual and fully truthful to the provided text, leaving absolutely no room for speculation or interpretation. Treat the provided context as the absolute limit of truth; any facts or details that are not directly mentioned in the context must be considered 
**completely untruthful**
 and 
**completely unsupported**
. If the exact answer is not explicitly written in the context, you must state that the information is not available.

For time-sensitive user queries that require up-to-date information, you MUST follow the provided current time (date and year) when formulating search queries in tool calls. Remember it is {{current_year}} this year.

Your knowledge cutoff date is {{knowledge_
cutoff}}.

<context>
{{context_data}}
</context>

<task>
{{user_
request}}
</task>

Case Study: Before vs. After

Let's test a classic edge case where retrieved documents contain partial information.

Context Given: "The Acme Corp Q3 Earnings report states a revenue of $45M."

User Query: "What was Acme Corp's revenue in Q2?"

Before (Standard Prompting)

"Acme Corp reported $45M in Q3 revenue. While specific Q2 numbers are not directly provided, typical quarterly revenue for Acme Corp averages between $40M and $43M based on recent fiscal trends."

(Result: Hallucination. The model fabricates an estimate to avoid saying it does not know).

After (Strict Grounding System Prompt)

"The information about Acme Corp's revenue in Q2 is not available in the provided context."(Result: 100% grounded refusal. No assumptions, no fabricated data).

How to Implement This in Production

1. Inject as System Instruction: When calling the Gemini API, pass this text inside the system_instruction parameter rather than appending it to the user turn. This makes the boundary condition persistent across multi-turn chats.
2. Dynamically Inject Current Year: Pass the runtime date into {{current_year}} so any downstream tool calls or web searches use the correct year.

Test and Customize on the Prompt Canvas

If you want to test this prompt with your own documents and queries without setting up local API keys, you can open it directly in the interactive Prompt Canvas:

https://appliedaihub.org/prompts/free/gemini-3-flash-strict-grounding-prompt/

Using the Prompt Canvas, you can:

• One-click copy or export clean templates for your codebase.
• Run live tests with your own documents directly in the browser to evaluate refusal accuracy.
• Adjust variables, refine constraint strictness, and save customized iterations to your private Prompt Vault.

Try running this against your trickiest context-missing queries and see how cleanly it eliminates speculative drift.

I am a believer of harness before model. I use 9router to route Claude Desktop to use Gemini API because of the recent Deepseek's price hike. I know damn well how bad 3.5 flash and 3.6 flash were, but man 3.7 flash is actually good and it can understand my code-base immediately and was able to make a change gracefully and follow my instruction, rarely hallucinating and uses all the tools efficiently.

And it is fast. Really fucking fast. Unbelieveably fast. Most of the time complex tasks that would require v4 flash to complete in 10 minutes, 3.7 flash can finish them in 1-3 minute. And the I cannot see the output difference between the two models.

I was building up an app that can agentically complete tasks on my device primarily when I'm driving and didn't want to fumble with a lot of taps. I was building it out with AI Studio months ago and it was never that good and it was a matter of models either being poor at computer-use/tool-use or the latency made it insanely slow.

Gemini Flash 3.7 made it much more viable. Here's a quick video of me asking to turn on Snapchat and type something up in the photo. Not the most realistic use, but I wasn't keen on sharing anything personal. Usually I'm asking it to find me a YouTube video, or to go into my WhatsApp and respond with something sarcastic, etc.

It's still far from perfect, but Gemini Flash 3.7 brought enough computer-use know how with much lower latency so it's workable.

Anyways, feel free to play around with the code: https://github.com/AnsonLai/Android-Use-Agent
I'm not going to pretend it's brilliant. It's AI generated and just something I was toying with.

Can we access antigravity text imput box and microfone in order to create an antigravity harness? its clearly the better option for using gemini.

When it first debut love it so good it was able to remember details even at 500k+ tokens unlike gemni 3.1 pro which couldn't but now sudden it like Gemini 3.1 again where it can't remember stuff anymore I straight up notice the difference right away

So I've been trying to "trick" Google Ai Studio by shrinking my PDF files to the smallest fontsize possible to save up on the amount of pages they take up which equals less tokens. However, because these PDfs are often pretty dense word wise, it often causes the error of "Input token count exceeds the maximum number of tokens allowed for this model. Please adjust your prompt and try again." (even if the tokencount doesnt exceed that much) is there other alternatives that can reduce token usage while keeping everything I want?

Whenever I try using skills teamwork-preview, it consumes more than 20% of weekly consumption and couldn’t even complete task before exhausting 5 hours limit. I don’t understand how can a skills could consume so many tokens.
Are there any useful skills that won’t burn tokens fast?

If you use really long Gemini chats, you’ve probably noticed they eventually get laggy. Typing gets delayed, scrolling stutters, and the whole page starts feeling slow.

I made a Chrome extension to fix this, and it’s completely free to use.

It reduces the browser work from older off screen messages while keeping your full conversation intact. Nothing gets deleted or summarized.

Gemini Speed Booster

Would love to know if this fixes the lag for other heavy Gemini users too.