Reviewing AI models can feel performative — balanced takes that don't commit. So let me be direct: after three months of daily use, Gemini 3.1 Pro surprised me in more directions than I expected. More impressive than I thought in some areas. More limited than I'd hoped in others.
I develop and operate multiple iOS and Android apps independently, while also running AI-focused tech blogs on automated pipelines. I've been using Gemini 3.1 Pro since its release, and I'm writing this in May 2026 — roughly three months in. Mostly via the Gemini API from code, with 100–300 API calls per day.
What Changed from 2.5 Pro
My first impression of 3.1 Pro was that its baseline capability improved. In situations where 2.5 Pro stopped short, 3.1 Pro voluntarily goes deeper.
Here are the changes I noticed concretely:
Error diagnostics improved substantially. Where 2.5 Pro would often say "this line is the problem," 3.1 Pro traces back to why that line fails and which upstream call triggered it.
Consistency over long conversations improved. Partially the 2M context window, but across 30+ exchanges, 3.1 Pro reliably references premises established at the start.
Japanese natural language quality improved. This matters to me. 2.5 Pro sometimes generated Japanese that felt translated from English. 3.1 Pro writes more naturally.
Deep Think mode quality improved noticeably — not in raw depth, but in how reasoning is organized. The path to conclusions is cleaner and more readable.
Multimodal accuracy improved. Showing a screenshot or app UI and asking for feedback now yields more specific, actionable responses.
What didn't change: code generation speed. Roughly the same as 2.5 Pro. That was something I'd hoped would improve.
15 Tasks Where It Excels
Tasks that stood out over three months:
Swift performance analysis — asking "where would Instruments flag problems in this code?" produces specific observations across memory, CPU, and battery. Genuinely useful.
API design review — "will this endpoint design maintain compatibility through future versions?" gets substantive answers with concrete concerns identified.
Multilingual translation (especially English/Japanese) — Google's linguistic infrastructure shows. Japanese output is often more natural than competing models.
App Store review risk assessment — analyzing screenshots and metadata against Apple Guidelines, with specific guideline references, is surprisingly accurate.
Long document summarization — the 2M context window earns its value here. Summarizing 100+ page documents in one pass is not something most models can do.
ADK and Vertex AI agent design — natural advantage from the Google ecosystem alignment.
Beyond these: Firebase Security Rules design, Google Analytics 4 query building, AdMob placement optimization suggestions, Kotlin Coroutines debugging support, Google Apps Script automation design, NotebookLM integration workflow suggestions, Google Cloud cost estimation, marketing copy A/B generation, and user interview hypothesis design all performed well.
5 Tasks Where It Genuinely Struggled
Xcode and Android Studio IDE-specific issues were consistently difficult. "Build passes but doesn't run on simulator" or tangled Gradle dependency problems — situations where IDE context is central — often didn't resolve. Cursor or Claude Code works better here.
App Store review outcome prediction has inherent limits. It gives probabilistic answers based on past cases, but those guesses don't always land. This is partly the model's limitation and partly because Apple's guidelines are dynamic.
Newer Unity API versions (2025.x+) seem undertrained. On two or three occasions, I got suggestions using deprecated APIs that only revealed themselves as errors when I ran the code.
Long conversation context drift — despite the large context window, I noticed that design decisions made early in a conversation sometimes stopped being referenced 20+ exchanges later.
Fine-grained UI layout adjustments — in both SwiftUI and Jetpack Compose, Gemini handles the structure well but leaves details wanting. I get better results having it implement the skeleton and adjusting the details myself.
Splitting Work with Claude: What 3 Months Revealed
I've been using Claude Opus 4.6 alongside Gemini 3.1 Pro throughout. A stable division of labor emerged.
Gemini for: anything touching the Google ecosystem (Firebase, GCP, Android, Workspace), long document analysis and summarization, multilingual work especially English/Japanese.
Claude for: code design reviews and architecture discussions, high-quality English technical writing, complex reasoning that benefits from extended thinking.
Either model for: general research, code completion, basic explanations — I choose based on cost and context.
API Cost Comparison
Gemini 3.1 Pro costs roughly 30–50% less than Claude Sonnet 4.6 per task (depending on input/output balance). My monthly split has settled at about 40% Gemini, 60% Claude. Routing Google ecosystem and long-document work to Gemini has reduced overall API costs by around 20–30%.
For indie developers, that difference is real money.
Looking back: A Mature Partner for Google Ecosystem Developers
After three months, my honest summary: Gemini 3.1 Pro has become essential for developers working within the Google ecosystem. For anyone who regularly uses Firebase, GCP, or Android, it offers a clear advantage over alternatives.
As a general-purpose AI assistant, it's competitive with Claude and GPT-5. As a Google ecosystem specialist, it's a category ahead.
The struggles I described are less about being "bad at things" and more about "having a clear domain." Work with its strengths and the cost-effectiveness is excellent. Work against them and you'll feel the friction. Three months in, I know which is which.