By Sagar Shankaran, Founder of CallSphere
Gemini Nano on Android vs Apple Intelligence on iOS — capabilities, performance, and the battle for on-device user data. Lens: education. A 2026 builder briefing.
Key takeaways
On-device AI is the next mobile platform war. Gemini Nano and Apple Intelligence are the two contenders.
This is a builder briefing — not a press release recap.
Industry lens — education. Education teams use the long-context models for personalized tutoring agents, curriculum generation, and assessment grading — but data privacy (FERPA, COPPA) constrains deployment paths.
Google's April 2026 cadence around the Gemini 3 family, Antigravity, and the AgentSpace surface is the most coherent product narrative the company has put together in years. The pieces fit: a frontier model (Gemini 3 Pro), a fast variant (Gemini 3 Flash), an on-device tier (Gemini Nano), an IDE (Antigravity), an agent runtime (Vertex Reasoning Engine), an agent catalog (Agent Garden), an enterprise hub (AgentSpace), and a consumer notebook (NotebookLM Pro). For builders, the practical impact is that you can pick a Google story for almost any agent shape and have a credible delivery path from prototype to production.
This is the short version; the full vendor documentation has more nuance, particularly on rate limits and regional availability.
On SWE-bench Verified, Gemini 3 Pro scores 71.8% — within striking distance of Claude Opus 4.7's 72.9% and ahead of GPT-5.5's 69.4%. On tau-bench retail, the new model lands at 95.1%, a meaningful jump from Gemini 2.5's 88.6%. MMMU sits at 84.0%. The numbers matter less than the spread: for the first time, the three frontier labs are within 3 percentage points of each other on most benchmarks that builders cite.
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Gemini 3 Pro is priced at $1.25 / $10.00 per million input/output tokens up to 200K context; long-context (>200K) tier kicks in at $2.50 / $15.00. With prompt caching at a 75% discount and a 50% Batch API discount on async workloads, the realized cost for many production agents lands closer to $0.80 per million blended tokens. Compared to Claude Opus 4.7 ($15/$75) and GPT-5.5 ($10/$30), Gemini 3 Pro is positioned as the price-aggressive frontier option.
The recommended path is prototype in AI Studio, then promote to Vertex AI for production. Vertex provides regional availability (12 regions globally, including europe-west4 and asia-southeast1), VPC-SC, CMEK, audit logging, and the new Reasoning Engine managed runtime. AI Studio's prompt IDE got a major refresh — versioned prompts, side-by-side eval, and one-click deployment to Vertex are now first-class.
For education teams specifically, the quickest path to value is the chat or voice agent surface — the cost-per-conversation math has improved by 3-5x since Q1 2026.
Google's open-protocol bet is real: A2A 1.0 ships as an open spec for agent-to-agent communication, complementing MCP 1.0 for tool integration. Vertex AI Agent Builder ships first-class A2A support, and Agent Garden's 80+ pre-built agents all advertise A2A endpoints. For builders, this means a Google-built sales agent can hand off to a third-party fulfillment agent (running on AWS or self-hosted) without custom integration glue.
This is the short version; the full vendor documentation has more nuance, particularly on rate limits and regional availability.
Before you commit a roadmap quarter to this, run these checks:
Q: Is Gemini 3 Pro available in my region?
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A: Gemini 3 Pro is generally available in 12 Vertex AI regions as of May 2026, including us-central1, europe-west4, asia-southeast1, and asia-northeast1. Check the Vertex AI region availability docs for the latest list.
Q: How does Gemini 3 Pro pricing compare on a real workload?
A: Headline price is $1.25 / $10.00 per million tokens up to 200K context. With 75% prompt cache discount and 50% Batch API discount, realized blended cost on long-running agent workloads typically lands at $0.80-$1.20 per million tokens.
Q: Can I use Antigravity with Claude or GPT-5.5?
A: Yes. Antigravity is unusually open — Claude Opus 4.7, GPT-5.5, and Gemini 3 Pro are all first-class providers in the IDE settings.
Q: What is the difference between A2A and MCP?
A: MCP is the agent-to-tool protocol; A2A is the agent-to-agent protocol. They are complementary, not competitive — most production agent stacks will use both.
Last reviewed 2026-05-05. Pricing and benchmarks change frequently — check primary sources before relying on numbers in this article.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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