Antigravity vs Windsurf: The Agent-First IDE Race — London View

Windsurf had the head start, Antigravity has the firepower. The next 12 months decide the agent-IDE category.

This briefing is written with builders in London, UK in mind — local procurement, latency from regional Google Cloud / AWS / Azure regions, and time-zone-friendly support windows shape the practical recommendations.

What Shipped and Why It Matters

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.

Benchmarks That Actually Matter

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.

This is the short version; the full vendor documentation has more nuance, particularly on rate limits and regional availability.

Pricing and Total Cost of Ownership

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.

Deployment Path: AI Studio to Vertex

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 London, UK teams, the practical near-term move is to set up an evaluation harness against your top 3 production prompts before committing to a model swap.

What To Test In The Next Two Weeks

Before you commit a roadmap quarter to this, run these checks:

1. Confirm Vertex AI region availability for your data residency requirements (europe-west4 and asia-southeast1 are the two most-asked-for in 2026).
2. Run your top 3 production prompts against Gemini 3 Pro AND Gemini 3 Flash; the cost-quality crossover is workload-specific.
3. Validate prompt caching savings on your real traffic shape — 75% discount is a marketing maximum, realized savings vary.
4. Test A2A interop with at least one third-party agent before betting your architecture on it.
5. Stress-test long-context recall at 800K+ tokens; degradation past 1M is workload-dependent.
6. Re-run your safety evals — Gemini 3 Pro's behavior on edge cases differs from 2.5 Pro in non-obvious ways.

FAQ

Q: Is Gemini 3 Pro available in my region?

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.

Sources

• https://www.bloomberg.com/news/articles/2026-04-google-ai-strategy
• https://blog.google/technology/google-deepmind/gemini-3-pro/
• https://www.reuters.com/technology/google-deepmind-releases-2026/
• https://ai.google.dev/gemini-api/docs/models/gemini-3

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Last reviewed 2026-05-05. Pricing and benchmarks change frequently — check primary sources before relying on numbers in this article.

Antigravity vs Windsurf: The Agent-First IDE Race — London View — operator perspective

Most coverage of Antigravity vs Windsurf: The Agent-First IDE Race — London View stops at the press release. The interesting part is the implementation cost — what changes for a team running 37 agents and 90+ tools in production? On the CallSphere side, the practical filter is simple: would this make a 90-second appointment-booking call faster, cheaper, or more reliable? If the answer is "maybe in a benchmark," it doesn't ship to production.

Gemini, Vertex AI, and Google's vertical-AI strategy

Google's AI position spans three layers worth keeping straight: the Gemini family (general-purpose multimodal models), Vertex AI (the managed runtime, MLOps tooling, and enterprise-grade governance around them), and a growing set of vertical plays (Med-PaLM-class healthcare models, retail-specific search, document-AI for ops). For SMB call automation, the realistic Gemini fit today is post-call analytics, multimodal document handling (insurance card photos, ID verification, receipts), and longer-context summarization — not the realtime audio inner loop, where streaming stability and tool-call latency still favor incumbent realtime APIs. Vertex AI is where the enterprise governance story lives: VPC service controls, regional pinning, audit logging, and IAM that maps cleanly onto an existing GCP estate. CallSphere's evaluation pattern for Google AI: keep Gemini in the analytics evals queue, lean on Vertex when a customer's compliance posture requires GCP-native data residency, and re-evaluate the realtime story on every major release. Google's vertical-AI plays are worth tracking because they signal where the specialist-model market is headed.

FAQs

Q: How does antigravity vs Windsurf change anything for a production AI voice stack?

A: Most of the time it doesn't, and that's the right starting assumption. The relevant test is whether it improves at least one of: p95 first-token latency, tool-call argument accuracy on noisy inputs, multi-turn handoff stability, or per-session cost. Real Estate deployments run 10 specialist agents with 30 tools, including vision-on-photos for listing intake and follow-up.

Q: What's the eval gate antigravity vs Windsurf would have to pass at CallSphere?

A: The eval gate is unsentimental — a regression suite that simulates real call traffic (noisy ASR, partial inputs, tool-call timeouts) measures four numbers, and a candidate has to win on three of four without losing badly on the fourth. Anything else is treated as a blog post, not a stack change.

Q: Where would antigravity vs Windsurf land first in a CallSphere deployment?

A: In a CallSphere deployment, new model and API capabilities land first in the post-call analytics pipeline (lower stakes, async, easy to roll back) and only later in the live realtime path. Today the verticals most likely to absorb new capability first are After-Hours Escalation and Sales, which already run the largest share of production traffic.

See it live

Want to see healthcare agents handle real traffic? Walk through https://healthcare.callsphere.tech or grab 30 minutes with the founder: https://callsphere.ai/book.