Gemini 3.5 Pro Developer Guide: 2M Context Window and Deep Think Mode

Official Sources

Gemini 3.5 Pro is Google's next flagship model, now rolling into general availability in late June 2026 after an enterprise preview on Vertex AI. The headline numbers: a 2-million-token context window and a Deep Think reasoning mode that trades latency for accuracy on hard problems.

This guide covers what developers need to know before integrating: where the model is available, what it actually costs, how the context window and reasoning mode work in practice, and when Gemini 3.5 Pro is the right choice versus Flash or other providers.

Last updated: June 30, 2026

Model Specifications

The 2M context window is the largest production context available from any major provider as of this writing. Claude's current Opus 4.x and Fable 5 models cap at 200K tokens. GPT-5.x caps at 512K tokens in the extended context tier.

That scale difference matters for specific workloads. It does not mean Gemini 3.5 Pro is the right default for every task.

Availability and Access

Current access (June 2026):

• Vertex AI: Model ID gemini-3.5-pro-preview-06. Enterprise accounts can request allowlist access through their Google Cloud account team.
• Google AI Studio: Expected at GA launch.
• Gemini API (REST/SDKs): Expected at GA launch.
• OpenAI-compatible endpoint: Google AI Studio provides an OpenAI-compatible mode for migration.

At general availability, the model will appear in Google AI Studio and the Gemini API alongside the existing Gemini 3.x lineup.

Pricing (Expected)

Google has not published official Gemini 3.5 Pro pricing yet. Based on enterprise preview participant reports and historical Flash-to-Pro ratios, the expected range is:

These figures are estimates. Verify against the official pricing page before production deployment.

For comparison, Gemini 3.5 Flash is $1.50/$9.00 per million tokens, making Pro roughly 8 to 10 times more expensive. The trade-off is reasoning quality, not speed.

Context Window: What 2M Tokens Actually Holds

The 2-million-token context is large enough to hold entire codebases, document sets, or conversation histories that previously required retrieval augmentation.

The practical question is not whether the context fits. It is whether loading 2M tokens is worth the cost and latency versus chunked retrieval.

When massive context helps

• Whole-repository audits: Security scans, architecture reviews, or dependency analysis where cross-file relationships matter.
• Cross-document analysis: Comparing multiple legal filings, contracts, or policy documents directly without summarization loss.
• Long-running agent state: Multi-hour agent sessions where accumulated context would otherwise require expensive handoffs.
• Consistency-sensitive tasks: Content that must reference distant prior context without semantic drift.

When massive context does not help

• Single-file tasks: Code generation or editing scoped to one file does not benefit from 2M context.
• Retrieval-friendly workloads: If the answer exists in a small slice of the corpus, RAG is cheaper and faster.
• Latency-sensitive paths: Loading 2M tokens adds significant prefill time. Real-time applications should use Flash.

Deep Think Mode

Deep Think is Google's name for extended inference-time compute. The model spends more cycles reasoning before answering instead of pattern-matching to a quick response.

How to enable it

Deep Think is controlled via the thinkingConfig API parameter:

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);

const model = genAI.getGenerativeModel({
  model: "gemini-3.5-pro",
  generationConfig: {
    thinkingConfig: {
      thinkingLevel: "high"  // minimal, low, medium, high
    }
  }
});

const result = await model.generateContent({
  contents: [{ role: "user", parts: [{ text: "Your complex reasoning prompt" }] }]
});

The thinkingLevel parameter has four options:

Important: Reasoning tokens count against your context budget and appear to be billed at the output token rate. A problem that requires extensive reasoning can consume significant tokens before producing the final answer.

When to use Deep Think

• Mathematical proofs and formal reasoning
• Complex code architecture decisions
• Multi-constraint optimization problems
• Legal or policy analysis requiring careful interpretation

When not to use Deep Think

• Retrieval or lookup tasks
• Simple code generation
• Real-time or latency-sensitive applications
• High-throughput pipelines where cost per call matters

Integration Patterns

Python SDK

import google.generativeai as genai
import os

genai.configure(api_key=os.environ["GEMINI_API_KEY"])

model = genai.GenerativeModel(
    model_name="gemini-3.5-pro",
    generation_config={
        "temperature": 1.0,  # keep at default
        "max_output_tokens": 8192,
    }
)

response = model.generate_content("Analyze this codebase for security vulnerabilities...")
print(response.text)

TypeScript/JavaScript SDK

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!);

const model = genAI.getGenerativeModel({
  model: "gemini-3.5-pro",
  generationConfig: {
    temperature: 1.0,
    maxOutputTokens: 8192,
  }
});

const result = await model.generateContent("Your prompt here");
console.log(result.response.text());

cURL (REST API)

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-pro:generateContent?key=${GEMINI_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "parts": [{"text": "Your prompt here"}]
    }],
    "generationConfig": {
      "temperature": 1.0,
      "maxOutputTokens": 8192
    }
  }'

Caching for Cost Control

With long-context workloads, caching becomes essential. Cached input on Pro-tier models is typically 90% cheaper than standard input.

const cachedContent = await genAI.cacheContent({
  model: "gemini-3.5-pro",
  contents: [
    { role: "user", parts: [{ text: systemPromptAndContext }] }
  ],
  ttl: "3600s"  // 1 hour
});

const model = genAI.getGenerativeModelFromCachedContent(cachedContent);
const result = await model.generateContent("Your task-specific prompt");

For workloads that reuse the same large context across many calls, caching can reduce input costs from $15/M to $1.50/M or less.

Gemini 3.5 Pro vs Fable 5 vs GPT-5.x

The context window is Gemini 3.5 Pro's standout advantage. If your workload genuinely needs 500K to 2M tokens of live context, it is currently the only frontier option.

For shorter context workloads, the choice depends more on model behavior, API ergonomics, and existing integration.

My Take

Gemini 3.5 Pro is a specialized tool, not a general replacement.

The 2M context window solves real problems: whole-codebase security audits, cross-document legal analysis, and long-running agent sessions where context handoff is expensive or lossy. For those workflows, the context size alone makes it worth evaluating.

For most day-to-day coding and short-context tasks, Flash at $1.50/$9.00 is the better default. Pro's 8 to 10x cost premium only makes sense when the context or reasoning requirements justify it.

Deep Think is interesting but adds both latency and token cost. Use it deliberately for hard reasoning problems, not as a default.

The launch timing matters too. Gemini 3.5 Pro arrives shortly after Fable 5, which set a new bar for agentic coding quality. Google is positioning Pro as the context leader rather than trying to match Fable 5's agentic benchmarks directly. That is a reasonable trade-off if your workload is context-bound.

FAQ

What is the Gemini 3.5 Pro context window?

Gemini 3.5 Pro has a 2-million-token context window, the largest of any production frontier model as of June 2026. This is double the previous Flash generation and ten times larger than Claude Fable 5.

When will Gemini 3.5 Pro be generally available?

General availability is expected in late June 2026. Enterprise developers can currently access the preview via Vertex AI with allowlist approval.

How much does Gemini 3.5 Pro cost?

Official pricing has not been announced. Based on enterprise preview reports, expect $12 to $15 per million input tokens and $36 to $45 per million output tokens, with long-context surcharges above 200K tokens.

What is Deep Think mode?

Deep Think is Google's extended inference-time compute mode. The model spends more reasoning cycles before answering, improving accuracy on complex problems at the cost of higher latency and token usage.

Should I use Gemini 3.5 Pro or Flash?

Use Flash for most tasks. Use Pro when you genuinely need the 2M context window or Deep Think reasoning. Flash is 8 to 10 times cheaper.

How does Gemini 3.5 Pro compare to Claude Fable 5?

Gemini 3.5 Pro leads on context size (2M vs 200K tokens). Fable 5 has set higher benchmarks on agentic coding tasks. Choose based on whether your workload is context-bound or coding-quality-bound.

Can I use Gemini 3.5 Pro with existing OpenAI code?

Google AI Studio provides an OpenAI-compatible endpoint for migration. You can point existing OpenAI SDK code at the Gemini endpoint with minimal changes.

Is Deep Think worth the extra cost?

For complex reasoning tasks - mathematical proofs, architecture decisions, multi-constraint optimization - yes. For retrieval, simple generation, or latency-sensitive paths, no.

Sources

Verified June 30, 2026.

• Gemini API Pricing - Google AI for Developers
• Gemini API Models - Google AI for Developers
• Gemini 3 Developer Guide - Google AI for Developers
• Gemini Long Context Documentation - Google AI for Developers
• Vertex AI Agent Platform Pricing - Google Cloud
• Gemini 3.5 Pro: 2M Context, Deep Think, and the Post-Fable-5 Frontier - DEV Community