An in-depth technical overview, architectural breakdown, and benchmark suite for Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, Google DeepMind's newest state-of-the-art AI models engineered for long-horizon agentic workflows, autonomous software engineering, and frontier-level cybersecurity defense.
| Metric / Feature | Specification | Impact & Capability |
|---|---|---|
| Introductory Input Price | $0.75 / 1M Tokens | Matches 3.7 Flash; 5.3x - 6.6x cheaper than Opus 5 & Sol |
| Introductory Output Price | $3.75 / 1M Tokens | Ultra-low compute overhead for iterative agent loops |
| Context Window | 1M Input / 64K Output | Massive document, video, and codebase ingestion |
| Long-Horizon Engineering | 71.0% DeepSWE v1.1 | Beats Claude Sonnet 5 (53.8%) & GPT-5.6 Terra (69.6%) |
| Financial Agent Evals | 61.4% Vals Finance v2 | 🥇 #1 Leader across all frontier models |
| Legal Agent Evals | 10.0% Harvey's Legal | 🥇 #1 Leader; 4x higher pass rate than GPT-5.6 Sol |
| Multidisciplinary Reasoning | 54.9% HLE-Verified | Outperforms Claude Opus 5 (54.4%) & GPT-5.6 Sol (54.5%) |
| Cyber Vulnerability Discovery | >70% Discovery Rate | Frontier detection across 20 programming languages |
| Automated Cyber Patching | 47.2% CWE-Bench | Pareto frontier performance at fraction of frontier cost |
Gemini 3.8 Flash introduces a fundamental shift in model execution: "The model works harder." On complex tasks, it dynamically scales reasoning depth, executes extra verification steps, and calls tools iteratively in long-horizon loops.
[ Multimodal Context Input (Text, Vision, Audio, Video - up to 1M Tokens) ]
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ Dynamic Effort Level Harness │
│ (Adjustable Reasoning Depth: Low Efficiency ◄──► High Diligence) │
└────────────────────────────────┬───────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ Recursive Agentic Reasoning Loop │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Tool Call Exec │───►│ Context Refine │───►│ Self-Correction │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
└────────────────────────────────┬───────────────────────────────────────┘
│
┌───────────────────────┴───────────────────────┐
▼ ▼
┌───────────────────────────────┐ ┌───────────────────────────────┐
│ Gemini 3.8 Flash Standard │ │ Gemini 3.8 Flash Cyber │
│ • 71% DeepSWE Coding │ │ • CWE-Bench Pareto Patching │
│ • Vals Finance & Legal #1 │ │ • CyberGym Vulnerability Ops │
│ • Google Antigravity Native │ │ • Fairwind Defender Program │
└──────────────┬────────────────┘ └──────────────┬────────────────┘
│ │
▼ ▼
[ High-Precision Output (Up to 64K Tokens) ] [ Verified Defensive Security Patches ]
- Iterative Tool Calling & Work-Harder Loops: Rather than single-pass generation, 3.8 Flash executes continuous self-correction and multi-step tool interactions to solve complex engineering and research tasks.
- Customizable Reasoning Effort: Developers can calibrate effort levels to balance latency/cost against peak benchmark performance for efficiency-first or autonomy-first workloads.
- Cyber-Hardened Core: Trained directly on rigorous cybersecurity vulnerability detection, accelerating both standard code reasoning and defensive security patching.
Below is the official DeepMind evaluation matrix comparing Gemini 3.8 Flash against previous generations and direct market competitors (September 2026):
| Benchmark Category | ⚡ Gemini 3.8 Flash | 🔹 Gemini 3.7 Flash | 🟣 Claude Opus 5 | 🟣 Claude Sonnet 5 | 🟢 GPT-5.6 Sol | 🟢 GPT-5.6 Terra | Gemini Win |
|---|---|---|---|---|---|---|---|
| Input Price ($/1M) | $0.75 | $0.75 | $5.00 | $2.00 | $4.00 | $2.00 | ⚡ Best Value |
| Output Price ($/1M) | $3.75 | $3.75 | $25.00 | $10.00 | $20.00 | $12.00 | ⚡ Best Value |
| DeepSWE v1.1 (Software Eng) | 71.0% | 65.3% | 74.0% | 53.8% | 72.7% | 69.6% | ⚡ Near Frontier |
| Vals Finance Agent v2 | 61.4% | 59.0% | 58.6% | 53.9% | 53.8% | 54.4% | 🥇 #1 Leader |
| Harvey's Legal Agent Benchmark | 10.0% | 8.8% | 6.7% | 5.0% | 2.5% | 0.8% | 🥇 #1 Leader |
| Terminal-bench 2.1 (CLI Coding) | 89.4% | 85.8% | 89.1% | 80.4% | 88.8% | 87.4% | 🥇 #1 Leader |
| Terminal-bench 4.0 (Agent Ops) | 19.1% | 11.2% | 51.8% | 12.4% | 37.3% | 23.6% | ⚡ +70% vs 3.7 |
| CharXiv Reasoning (Charts) | 86.2% | 84.5% | 83.7% | 70.1% | 85.8% | 85.9% | 🥇 #1 Leader |
| LVBench (Long Video Understanding) | 87.8% | 85.4% | 75.4% | 68.5% | 82.1% | 78.9% | 🥇 #1 Leader |
| HLE-Verified (Expert STEM/Hum) | 54.9% | 53.6% | 54.4% | 31.0% | 54.5% | 51.1% | 🥇 #1 Leader |
| OSWorld-2.0 (Computer Use) | 59.0% | 50.6% | 75.4% | 42.6% | 62.6% | 50.2% | ⚡ +8.4pp vs 3.7 |
| BioMysteryBench (Bio Research) | 88.8% / 56.5% | 87.1% / 43.5% | 90.1% / 49.4% | 87.5% / 34.1% | 79.5% / 44.7% | 83.8% / 49.4% | 🥇 #1 Leader (Diff) |
| LABBench2 (Biology Tasks) | 86.2% | 82.1% | 84.2% | 80.1% | 82.1% | 81.2% | 🥇 #1 Leader |
Gemini 3.8 Flash Cyber provides a specialized defense advantage for security professionals, government entities, and critical infrastructure maintainers via Google's Fairwind Program:
- Automated Patching: Reaches a pass@1 of 47.2% on CWE-Bench (Pareto frontier vs leading frontier at 47.8%, at 5x lower cost).
- Chrome Security Team Impact: Delivered 2.6x more correct vulnerability patches than significantly larger commercial frontier models.
- Enterprise Penetration Testing (Wiz): Demonstrated +7.5% to +9.7% higher recall at 2.3x - 5.2x lower cost.
- Cloud Security Discovery: Google Cloud Vulnerability Research discovered a critical foundational vulnerability in under 2 hours (a process usually taking months).
Gemini 3.8 Flash is available immediately across the developer and enterprise stack:
- Google Antigravity: Native agentic workflows with live looping instructions.
- Google AI Studio & Gemini API: Available today with model ID
gemini-3.8-flash. - Gemini Enterprise & App: Available to Google AI Pro and Ultra subscribers.
- Fairwind Defender Program: Prioritized access to
gemini-3.8-flash-cyberfor verified security teams.
import google.generativeai as genai
genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-3.8-flash")
response = model.generate_content(
"Build a 3D visualizer in Three.js decomposing hardware components",
generation_config={"thinking_budget": 2048} # Customizable effort level
)
print(response.text)Gemini 3.8 Flash Gemini 3.8 Cyber Google DeepMind Google Antigravity AI Studio DeepSWE v1.1 Harvey Legal Benchmark Vals Finance Agent Cybersecurity AI Fairwind Program Agentic Workflows Long Horizon Coding CWE Bench 1M Context Window