Gemini 3.8 Flash Online: Build Long-Horizon Agents Across 1M Tokens

Try Gemini 3.8 Flash on Lorka AI to plan, code, call tools, and verify complex workflows across multimodal projects at Flash-tier pricing.

Gemini
Ask anything...
Reasoning
💡💡💡💡💡
Frontier
Speed
⚡⚡⚡⚡⚡
Fast
Context Handling
🧠🧠🧠🧠🧠
Excellent
Input
Text, Image, Audio, Video
Output
Text

Designed for speed and cost-efficiency, Google’s Gemini 3.8 Flash is its most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, advanced reasoning, multimodal understanding, and complex enterprise workflows.

Context window1M tokens
Max output tokens65K tokens
Knowledge cutoffMarch 2026

Chat With Gemini 3.8 Flash for Coding and Agents

Move from quick answers to sustained workflows that plan, use tools, verify results, and refine the final deliverable.

Long-horizon coding

Reported 90.8% on Terminal-Bench 2.1, up from 81.6% for Gemini 3.7 Flash.

Autonomous agents

Plans, calls tools, adapts, verifies, and refines across multi-step workflows instead of stopping at first-pass answers.

Professional reasoning

Reported 61.4% on Vals Finance Agent V2 and 10.0% all-pass on Harvey's Legal Agent Benchmark.

Deliberate execution

Low, medium, and high effort levels trade additional reasoning steps for stronger work on difficult tasks.

Workhorse value

Keeps 3.7 Flash's introductory $0.75 input and $3.75 output pricing per million tokens through 2026.

What Is Gemini 3.8 Flash Cyber ?

Gemini 3.8 Flash Cyber is a restricted Google AI model for defensive cybersecurity organizations that need advanced vulnerability discovery and patching capabilities.

It is not a general consumer model, meaning access to Gemini 3.8 Flash Cyber is limited through Google's Fairwind Program to trusted government authorities, critical infrastructure operators, and software maintainers.

For general coding, analysis, and agentic workflows, the Gemini 3.8 Flash model is the broader workhorse model.

Gemini 3.8 Flash Coding, Analysis, and Development Workflow Examples

These illustrative examples show the type of professional output the Google model can produce.

User

Analyze this repository, identify the most serious reliability issue, implement the highest-priority fix, and add tests that prevent regression.

Gemini
Gemini 3.8 FlashLong-horizon engineering task

Root cause The incident queue negates its creation timestamp before calling min(). This can select newer, low-severity incidents before older critical incidents.

PYTHON
1SEVERITY_WEIGHT = {
2 "critical": 4,
3 "high": 3,
4 "medium": 2,
5 "low": 1,
6}
7
8def incident_priority(incident):
9 return (
10 -SEVERITY_WEIGHT[incident.severity],
11 -int(incident.customer_impact),
12 incident.created_at,
13 )
14
15def next_incident(queue):
16 return min(queue, key=incident_priority) if queue else None
17
18#Regression test
19def test_critical_incident_has_priority():
20 queue = [
21 incident("low", True, "2026-09-17T10:04:00Z"),
22 incident("critical", True, "2026-09-17T10:08:00Z"),
23 ]
24
25 assert next_incident(queue).severity == "critical"

$ pytest tests/triage/test_priority.py -q 4 passed in 0.21s Next step: Replay the previous 30 days of incidents before enabling the corrected ranking policy in production.

User

Use this product brief, user feedback, and interface screenshots to define the data model, key flows, and launch validation plan.

Gemini
Gemini 3.8 FlashMulti-step product development

Product direction Build a release-readiness workspace that connects product requirements, user feedback, screenshots, and approval evidence in one reviewable launch record. Core data model

SQL
1create table validation_items (
2 id uuid primary key,
3 project_id uuid not null,
4 requirement text not null,
5 state text check (
6 state in ('open', 'passed', 'failed', 'waived')
7 ),
8 evidence_asset_id uuid,
9 reviewer_id uuid
10);

Key flows - Import a brief and confirm extracted requirements. - Group feedback by user problem and link it to requirements. - Review screenshots or test evidence against each requirement. - Block launch approval while critical checks remain unresolved. Validate before launch Confirm role permissions, audit logs, file handling, accessibility, interrupted-import recovery, and mobile behavior.

User

Review these financial reports and planning notes. Identify the largest opportunities and risks, then provide an executive recommendation.

Gemini
Gemini 3.8 FlashFinancial and strategy analysis

Recommendation Prioritize expansion within the existing enterprise customer base for two quarters. Retention is strengthening, while acquisition efficiency makes a broad growth push premature. Evidence - Revenue increased from $48.2M to $57.1M, or 18.5%. - Gross margin improved from 72.4% to 74.1%. - Enterprise net revenue retention reached 119%. - CAC increased by 27.8%, while payback rose from 17.2 to 22.6 months. - The five largest customers represent 28% of annual recurring revenue. Actions 1. Redirect 20% of incremental growth spending toward customer expansion. 2. Build downside scenarios for the two largest renewals. 3. Increase acquisition spending only when CAC payback falls below 19 months.

User

Review these policies and contract summaries, identify material obligations and ambiguities, and prepare questions for legal review.

Gemini
Gemini 3.8 FlashLegal and document analysis

Material issues - The master agreement requires security-incident notice within 72 hours; the data addendum requires 24 hours. - Customer data must be deleted within 30 days, but backup deletion has no final deadline. - Subprocessor changes require notice, but the documents provide no clear objection remedy. - Scheduled maintenance is excluded from uptime without a defined annual limit. - Governing-law provisions conflict across the order form and master agreement. Questions for counsel 1. Which security-notice period controls? 2. Should backup copies have a fixed deletion deadline? 3. What remedy applies when a customer objects to a subprocessor? 4. Which governing-law clause takes precedence? This output organizes review priorities and is not legal advice.

Try Gemini 3.8 Flash With Other Leading Models in a Single Platform

Lorka AI keeps model comparison and project context in one workspace, helping teams choose the right capability for each task.

Compare leading models

Access Gemini 3.8 Flash, Opus 5, GPT-6, Qwen 3.8 Max, and many more models with identical source material.

Match capability to difficulty

Use a workhorse model routinely, then switch when a specialized capability is required.

Keep context centralized

Organize prompts, documents, screenshots, research, and outputs without rebuilding the project.

Test before standardizing

Test coding quality, reasoning, tool use, speed, and output style before scaling a workflow.

Control costs

Reserve expensive frontier models for tasks that clearly require their higher capability ceiling.

Google Gemini 3.8 Flash Context Window, Capabilities, and More

01

Model type

  • Released September 2, 2026
  • Google’s strongest Flash workhorse for coding, autonomous agents, and professional knowledge work
  • Designed for sustained workflows rather than isolated answers
02

Context and output

  • Context window: 1,048,576 tokens
  • Maximum output: 65,536 tokens
  • Suitable for large repositories, reports, document collections, logs, and extended project histories
03

Modalities

  • Inputs: text, images, video, audio, and PDFs
  • Output: text
04

Reasoning and development

  • Low, medium, and high reasoning-effort levels
  • Iterative planning, tool use, verification, and refinement
  • Reported 61.6% on SWE-Bench Pro, 51.9% on SWE-Atlas, and 73.7% on DeepSWE v1.1
05

Pricing and availability

  • Through December 31, 2026: $0.75/M input and $3.75/M output tokens
  • From January 1, 2027: $1.50/M input and $7.50/M output tokens
  • Minimal reasoning effort is unsupported
  • implementations should use low, medium, or high

Put Google Gemini 3.8 Flash to Work Across Professional Teams

Repository debugging for software engineers

Trace failures across services, implement targeted fixes, and verify behavior with regression tests.

GeminiTry this prompt
"

Trace this failure across the repository, identify the root cause, implement a safe fix, and add tests covering the failure path.

"

Agent evaluation design for AI platform teams

Build practical evaluations that measure whether an agent completes tasks reliably, uses tools correctly, and recovers from failures.

GeminiTry this prompt
"

Design an evaluation suite for this agent, covering task completion, tool selection, recovery behavior, cost, latency, and failures requiring human review.

"

Architecture planning for technical founders

Evaluate service boundaries, scaling constraints, dependencies, and migration risks before committing engineering resources.

GeminiTry this prompt
"

Review this architecture and growth forecast, identify failure points, and propose a phased migration with rollback and validation gates.

"

Research synthesis for technical teams

Connect papers, datasets, logs, and notes while separating confirmed findings from assumptions.

GeminiTry this prompt
"

Synthesize these papers and experiment results, identify contradictions and evidence gaps, and recommend the next decisive experiment.

"

Financial analysis for strategy teams

Connect operating metrics, filings, and market research to material risks and decisions.

GeminiTry this prompt
"

Analyze these reports and operating metrics, identify the main value drivers and risks, and write an evidence-based executive recommendation.

"

Operating-plan stress testing for finance and operations teams

Test a plan against changing demand, pricing, staffing, and supplier conditions before resources are committed.

GeminiTry this prompt
"

Stress-test this operating plan against demand, pricing, staffing, and supplier shocks; quantify key sensitivities and define early-warning indicators for each scenario.

"

Gemini 3.8 Flash vs. Qwen 3.8 Max, Opus 5, and More

Compare the Gemini model with models like Qwen 3.8 Max, Fable 5.1, and other top models that can be used on Lorka AI’s all-in-one AI platform.

Legend:
💡Reasoning
⚡Speed
🤖Multimodality
🧠Context
(1: Poor – 5: Very good)
Gemini

Gemini 3.8 Flash

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Rapid, extended coding projects, multimodal investigation, self-directed agents, and reasoning-intensive professional work at scale.

OpenAI

GPT-6 Astra

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Difficult programming, computer-operated tasks, million-token project review, independent agents, technical investigation, and consequential professional analysis.

Claude

Claude Fable 5.1

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Frontier-level engineering, prolonged agent activity, complex research, system architecture, and precision-focused knowledge work.

Claude

Claude Opus 5

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

High-end programming, rigorous reasoning, enterprise investigation, complex decision-making, and autonomous processes spanning interconnected stages.

Qwen

Qwen 3.8 Max

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Extended multimodal development, agent-driven workflows, and visually assisted productivity tasks.

Grok

Grok 4.6

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Long-running agents, technical inquiry, interactive app creation, codebase assessment, and research workflows involving multiple stages.

Kimi

Kimi K3

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Large software initiatives, multimodal building, system evaluation, interface creation, and structured agents operating across extensive project material.

Meta

Muse Spark 1.3

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Codebase-wide engineering, difficult debugging, durable technical agents, multimodal product creation, and tool-assisted development across different environments.

Claude

Claude Sonnet 5

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Efficient business evaluation, application development, automated processes, project planning, and routine technical work balancing capability with speed.

Z.ai

GLM-5.3

Reasoning
💡💡💡💡💡
Speed
⚡⚡⚡⚡⚡
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Complete software delivery, process automation, major codebase modernization, technical agent development, and budget-conscious work with open-weight models.

Strengths and Limitations of Gemini 3.8 Flash and Leading Models

Gemini

Gemini 3.8 Flash

Strengths

Long-horizon coding, multimodal input, autonomous agents, professional reasoning, and low introductory pricing.

Limitations

Difficult prompts can consume more reasoning tokens and tool calls than efficiency-first workflows require.

Claude

Claude Opus 5

Strengths

Strong premium coding, reasoning, and agentic performance for demanding daily work.

Limitations

Premium proprietary access and a lower frontier ceiling than Fable 5 in specialized areas.

Claude

Claude Fable 5

Strengths

Top-tier long-horizon reasoning and coding for the hardest public workflows.

Limitations

Higher cost and stronger safety interventions reduce its practicality for routine high-volume work.

Claude

Claude Sonnet 5

Strengths

Balanced speed, coding quality, planning, and everyday agent capabilities.

Limitations

Lower capability ceiling than Opus 5, Fable 5, and other frontier models.

OpenAI

GPT-5.6 Sol

Strengths

Deep reasoning, advanced coding, scientific work, and specialized defensive-security capabilities.

Limitations

Restricted access and tighter safety controls can limit general workflow flexibility.

Grok

Grok 4.6

Strengths

Fast long-running agents, coding, research, and interactive product work.

Limitations

Its 500K-token context is smaller than 1M-token alternatives, with no native visual output.

Kimi

Kimi K3

Strengths

Combines 1M context, multimodal input, structured outputs, and long-horizon coding.

Limitations

Its global ecosystem remains less mature than larger Western AI platforms.

MetaAI

Muse Spark 1.3

Strengths

Strong agentic engineering, multitasking, tool efficiency, and instruction following.

Limitations

Proprietary, with maximum reasoning and planned open weights unavailable at launch.

Z.ai

GLM-5.3

Strengths

Strong long-context coding, structured outputs, and custom deployment options.

Limitations

Text-only operation can require more integration work than managed multimodal alternatives.

How to Access Gemini 3.8 Flash on Lorka AI

Try Gemini 3.8 Flash on Lorka AI and combine it with models like GPT-6, GLM-5.3, and more models in a single AI chat.

Select Gemini 3.8 Flash

Choose Gemini 3.8 Flash in Lorka’s AI chat model dropdown list.

Write a prompt

Type in a prompt and attach files to add more context.

Start your workflow

Get your output and combine Gemini with other AI frontier models in the same chat.

Try Gemini 3.8 Flash Now

Create an account with Lorka AI in minutes and get started using the Gemini 3.8 Flash chat.

Gemini 3.8 Flash Model FAQs

You can access Gemini 3.8 Flash through the Gemini API, Google AI Studio, Android Studio, Antigravity, Gemini Enterprise, the Gemini app, AI Mode in Google Search, Google Sheets, or Stitch. You can also access Gemini 3.8 Flash on Lorka AI and use it alongside AI image tools like Nano Banana 2 and other top LLMs from Anthropic, OpenAI, and more.