Gemini 3.5 Flash
Gemini 3.5 Flash is Google DeepMind's fastest frontier AI model, designed specifically for agentic tasks, coding, and long-horizon reasoning. Announced at Google I/O 2026, it delivers 289 tokens per second — four times faster than comparable frontier models — while scoring within two points of Anthropic's flagship on coding benchmarks at one-third the cost. The model achieves 76.2% on Terminal-Bench 2.1, 1656 Elo on GDPval-AA, 83.6% on MCP Atlas (tool use), and 84.2% on CharXiv Reasoning (multimodal). It introduces a multi-agent architecture with Antigravity harness for parallel subagent execution, making it exceptionally capable at complex, multi-step software engineering tasks. With a 1 million token context window, it handles entire codebases, long documents, and extended conversations without context degradation. Priced at $1.50 per million input tokens and $9.00 per million output tokens, it offers frontier-level intelligence at Flash-tier pricing. Available through Google AI Studio and the Gemini API with general availability from day one.
Key Highlights
Fastest Frontier Model
289 tokens per second output — 4x faster than other frontier models, ideal for agentic tasks.
Multi-Agent Architecture
Parallel subagent spawning and coordination with Antigravity harness — simultaneous coding, testing, and review.
Frontier Quality, Flash Pricing
Performance within 2 points of Anthropic's flagship at one-third the cost — 1M token context window included.
Superior Tool Use
83.6% on MCP Atlas — effective interaction with external tools, APIs, and development environments.
About
Gemini 3.5 Flash is Google DeepMind's most capable Flash-tier model, unveiled at Google I/O on May 19, 2026. Positioned as the fastest frontier AI model available, it targets the growing demand for AI agents that can autonomously execute complex, multi-step tasks in software engineering, data analysis, and business automation.
The model's headline metric is speed: 289 tokens per second output, making it approximately four times faster than other frontier models. This speed doesn't come at the expense of quality — Gemini 3.5 Flash scores within two points of Anthropic's flagship model on major coding benchmarks while costing roughly one-third the price. On Terminal-Bench 2.1, a rigorous software engineering benchmark, it achieves 76.2%. Its GDPval-AA Elo rating of 1656 places it among the top coding models globally.
A key innovation is the multi-agent architecture with the Antigravity harness system. This enables Gemini 3.5 Flash to spawn and coordinate parallel subagents for concurrent task execution. In practice, this means the model can simultaneously research documentation, write code, run tests, and review results — dramatically accelerating complex software development workflows. The architecture is purpose-built for agentic scenarios where the model must plan, execute, observe results, and iterate autonomously.
The model excels in tool use scenarios, scoring 83.6% on MCP Atlas, which evaluates how effectively models interact with external tools and APIs. Combined with its 84.2% on CharXiv Reasoning for multimodal understanding, it can process technical diagrams, charts, and visual documentation alongside code — a critical capability for real-world engineering work.
With a 1 million token context window, Gemini 3.5 Flash can ingest entire codebases, lengthy technical specifications, and extended conversation histories without losing coherence. This is particularly valuable for code understanding tasks where the model needs to reason about the relationships between files spread across a large project.
Compared to its predecessor Gemini 2.5 Flash, the 3.5 version shows 10-20% improvement in low-reasoning coding performance, significantly better handling of long-horizon tasks, and enhanced multi-agent coordination. It also produces richer, more interactive web UIs and graphics when given generation tasks.
Pricing follows Google's aggressive Flash-tier strategy: $1.50 per million input tokens and $9.00 per million output tokens — roughly 5x cheaper than input and 3.6x cheaper than output compared to frontier Pro-tier models. The 1M token context window is included at no additional premium. This pricing makes it economically viable for always-on agent scenarios that would be prohibitively expensive with other frontier models.
Gemini 3.5 Flash is generally available from launch through Google AI Studio and the Gemini API, with integration support for all major AI development frameworks. The 3.5 Pro variant is in internal testing with public release planned for the following month.
Use Cases
Autonomous Software Development
Autonomously executing code writing, test generation, debugging, and code review with parallel subagents.
Large Codebase Analysis
Understanding entire projects with 1M token context, dependency analysis, and generating refactoring recommendations.
Data Analysis and Reporting
Processing large datasets, performing statistical analysis, and generating interactive visualizations.
Workflow Automation
Automating API integrations, data transformations, and multi-step business processes through tool use.
Pros & Cons
Pros
- Fastest frontier model at 289 tokens/s — critical speed advantage for agent workloads
- Delivers frontier quality at Flash pricing — one-third the cost of competitors
- Multi-agent architecture with parallel task execution and coordination
- 1M token context window for comprehending entire codebases
- Superior tool use capability with 83.6% on MCP Atlas
Cons
- 5x more expensive input and 3.6x more expensive output pricing compared to 2.5 Flash
- Pro variant not yet publicly available — full potential pending
- Closed source — weights not downloadable, local execution not possible
- Very new — community ecosystem and integration tools still developing
Technical Details
Parameters
undisclosed
Architecture
Gemini Transformer
Training Data
proprietary
License
Proprietary
Features
- 289 tokens/s Output Speed
- 1M Token Context Window
- Multi-Agent Architecture (Antigravity)
- Advanced Tool Use (MCP)
- Code Generation and Review
- Multimodal Understanding
- Long-Horizon Task Execution
- Interactive Web UI Generation
- Parallel Subagent Coordination
Benchmark Results
| Metric | Value | Compared To | Source |
|---|---|---|---|
| Terminal-Bench 2.1 | 76.2% | Gemini 2.5 Flash: ~65% | Google DeepMind |
| GDPval-AA (Elo) | 1656 | — | Google DeepMind |
| MCP Atlas (Tool Use) | 83.6% | — | Google DeepMind |
| CharXiv Reasoning | 84.2% | — | Google DeepMind |
| Output Speed | 289 tokens/s | Gemini 2.5 Flash: 219.7 t/s | Google DeepMind |
| Context Window | 1M tokens | — | Google DeepMind |