Gemini 3.5 Flash: What Changes in Google's New Generation of AI Agents

Google took the next big step in the Gemini series with the Gemini 3.5 Flash, a model that presents itself not just as a faster chatbot, but as a basis for agentic workflows, coding, multimodal understanding and productive use within applications, APIs and enterprise environments. For those watching the AI ​​market, the shift is significant: the debate is moving from "which model answers best" to "which model can work quickly, with tools, in multiple steps, and at an acceptable cost."

This article is not based on screenshots or answers given by the model himself. It is based on the official Google DeepMind data, the model card, the developer docs and the official Gemini API pricing page. This matters, because hype quickly circulates around each new model: some present it as a complete human replacement, others as a failure because it doesn't beat every benchmark. Reality is more useful and practical.

What was released and when

The Gemini 3.5 Flash appears on the official page of Google DeepMind with model card published on May 19, 2026. As of May 20, 2026, at the time of this writing, it already appears in the Google AI developer docs as the “Current: Gemini 3.5” series model, with model id gemini-3.5-flash.

Availability is broad: Gemini App, Gemini API, Google AI Studio, Gemini Enterprise, Gemini Enterprise Agent Platform, Google AI Mode, Google Antigravity and Android Studio. This shows that Google is not keeping it as a closed lab demo. It pushes it directly to users, developers and businesses.

Important detail: we are currently talking about Gemini 3.5 Flash, not for official “Gemini 3.5 Pro” release. If Google releases a Pro version, it will be separate news. For now, the new generation starts with Flash, and that in itself says a lot about the direction of the market: models need to be fast, useful and cost-effective in real workloads, not just impressive in demos.

The basic features in simple words

Gemini 3.5 Flash is a natively multimodal model. It accepts text, images, video, audio and PDF as input, while the officially declared output is text. The official model information states up to 1 million input tokens and 64K output tokens. So if you see claims for 2M context, beware: in the official specs we checked, the limit is 1M.

Google positions it as a model for advanced reasoning, agentic coding, multimodal understanding and long context understanding. It supports function calling, structured output, Search as a tool and code execution. This is a practical point, not a marketing detail. A model that can call tools, work with structured output and handle large files is closer to a real work assistant than a simple text generator.

DeepMind's page also gives specific examples of use: iterative coding loops, creation of many UI concepts, long-horizon agentic execution, production of interactive web animations, organization of large file collections, multi-agent workflows and game improvement through agent loops. It doesn't mean that all of these will work perfectly in every use. But it does mean that Google is designing the model around performing tasks, not just answering questions.

What changes compared to the old Gemini?

The first big difference is direction. The older Gemini Flash models mainly had the “fast and economical” profile. Gemini 3.5 Flash tries to keep the Flash DNA, but get more seriously into tasks that until recently we expected from Pro models: agentic coding, multi-step workflows, tool use, UI control and large context analysis.

The second difference is that “Flash” should no longer be read as “just a small model”. In the official benchmarks, Gemini 3.5 Flash beats Gemini 3 Flash in many categories and in several places it even passes Gemini 3.1 Pro. For example, Terminal-bench 2.1 reports 76.2% for Gemini 3.5 Flash versus 58.0% for Gemini 3 Flash and 70.3% for Gemini 3.1 Pro. In the MCP Atlas, which concerns multi-step workflows with MCP, it gives 83.6% against 62.0% for Gemini 3 Flash and 78.2% for Gemini 3.1 Pro.

But it doesn't win everywhere. In long-context MRCR at 128K, the Gemini 3.1 Pro appears stronger. In Humanity's Last Exam, 3.1 Pro also ranks higher than 3.5 Flash. This is good to be clear: Gemini 3.5 Flash is not "best in everything". It's a model that seems designed for a very strong practical balance between speed, tools, agents and cost.

Why do we say it's an agent model?

The term "agentic" has been used so much that it often loses its meaning. In practice, agentic model means that it does not stick to one answer. Can participate in a work cycle: understands goal, takes intermediate steps, uses tools, writes or runs code, checks result, fixes, and continues.

This ties in with tools like Google Antigravity, but also with the broader market of AI agents, MCP servers, n8n workflows, browser automation and support assistants. In an e-commerce scenario, for example, we don't just want to ask “write me a report”. We want the system to read CSV or orders, clean wrong lines, make summary, find seasonality, suggest next action and leave audit trail.

This is where the real change lies. The old models were very good at generating text. New models are judged by whether they can enter procedures. This does not abolish man. Instead, it makes the right workflow more important: what action is done automatically, what goes through approval, what is recorded, and what should never be allowed without human control.

Multimodal and long context: what it means in practice

The 1M context window is no small detail. In practical terms, it means you can work with large documents, technical manuals, logs, PDFs, code, financial reports or a combination of many files. For a business, this opens up uses such as bid analysis, contract comparison, technical documentation, troubleshooting, knowledge base search and reporting.

Multimodal input is also important. If a model can understand images, video, audio and PDF, then it can help with workflows that until now required many different tools. A support desk can accept screenshots. A technical department can upload error logs and images. A marketing team can deliver creatives, landing pages and campaign reports in the same context.

But that doesn't mean we have to throw everything into the model without a plan. Long context costs money, can confuse the prompt, and requires clean structure. The correct use is to enter the right data, with clear instructions, and when it is used productively to have retrieval, caching, structured outputs and quality assessment.

Cost: powerful but not cheap like old Flash

In Gemini API pricing, Gemini 3.5 Flash is listed at Standard price $1.50 per 1M input tokens and $9.00 per 1M output tokens, with the output including thinking tokens. There are also lower priced Batch and Flex options, and higher priced Priority. In comparison, Gemini 3 Flash Preview comes in at $0.50 input and $3.00 output in Standard pricing.

So, it's not "cheap Flash" in the old sense. It is more expensive than Gemini 3 Flash, but cheaper than Gemini 3.1 Pro Preview for many typical text workloads up to 200K tokens. For developers and businesses, the right question is not only "how much does the token cost?". It's "how many steps do I save, how many retries do I need, how quickly do I get a reliable result, and how often do I need human correction?".

If a cheaper model needs three attempts and manual correction, while a more expensive one produces a correct structured result on the first try, the real cost changes. On the other hand, if the workload is simple classification, translation, or low-risk bulk processing, perhaps the Gemini 3.1 Flash-Lite or another more affordable model is a better choice.

What it means for developers and e-commerce

For developers, Gemini 3.5 Flash is mainly interesting for coding and agentic tools. Google shows it in iterative coding, UI generation, computer-use-like workflows and multi-agent orchestration. This makes it useful for rapid prototyping, refactoring, auditing large codebases, creating tests, analyzing logs, and internal tools.

For e-commerce, there are more practical scenarios. Order analysis, sales forecast, CSV cleaning, creation of product descriptions with SEO rules, identification of problematic products, support triage and reports from WooCommerce or PrestaShop. The model should not change prices or send emails uncontrollably. But it can propose, organize and prepare actions for approval.

In our context, this ties in with what we are already working around AI agents, n8n and MCP, but also with agent-to-agent internet that we saw in Moltbook. The common thread is simple: models become more capable, but value comes out when they are put into properly designed processes.

What to watch out for before using it productively

First, benchmarks are no guarantee. Official scores are useful, but each business needs its own little evals. If the use case is support, we create test tickets. If it's SEO, we make a quality checklist. If it's code, we run tests. If it is a financial report, we check numbers independently.

Second, agents need rights in moderation. Function calling and code execution are powerful tools, but in production they need sandboxes, logs, rate limits, approvals and clear scopes. The mistake is not to use an agent. The mistake is to give it access to real systems without guardrails.

Third, we need to calculate costs. 1M context is possible, but it is not always right to send huge files. Many times better architecture is retrieval, summaries, chunking and context caching. Good AI workflow is not "put everything in the prompt". It's "give the model exactly what it needs to do the job right".

What this means for AI agents

Gemini 3.5 Flash is one of Google's most interesting releases because it doesn't just try to win the chatbot battle. It tries to enter the battle of productive work: agents, coding, long context, multimodal input, structured output, search grounding and enterprise workflows. It's not a magic tool, and it's not automatically the best choice for every job. But it is a clear indication that the market is moving from simple prompts to systems that work with tools and processes.

For Greek businesses, the practical conclusion is not to chase the hype alone. To start from a specific problem: support, reporting, SEO, e-commerce operations, data cleanup or coding workflow. Then choose model, cost, guardrails and measurements. It remains to be seen whether Gemini 3.5 Flash is just a fancy new name or a real productivity tool.

Sources

• Google DeepMind: Gemini 3.5 Flash
• Google DeepMind: Gemini 3.5 Flash Model Card
• Google DeepMind: Evaluation methodology for Gemini 3.5 Flash
• Google AI for Developers: Gemini API models
• Google AI for Developers: Gemini API pricing
• Unsplash: Visualising AI with Google DeepMind
• Unsplash image: Google DeepMind Visualizing AI