Mid-Year Frontier IDE Comparisons (JUNE 2026)

May 28th, 2026

For context: I’ve been a heavy AI-assisted user before Cursor was actively a thing—since January of 2023. I’ve used multiple IDEs for evaluating and using before-agentic through current agentic code use since then. I’ve always found it easy to make something with gen-AI code, and I neither saw the “AI can’t do this programming…” conjecture as accurate—it’s more on the person who is trying to use it. Since then, my actual logic, reasoning, and systems thinking have vastly improved. Enough that my programming understanding has actually increased enough to where I can do things that even four years ago I would have had to sit and think a long time for.

Prior context: since 2024, I did have a gig with comparative AI evaluations, SVT/RLFT, qualitative analysis, and EQ-based qualitative analysis for judgements.

How I Use GenAI (Preview)

Back in 2025 I formalized how I debug and reason with GenAI: the Directive Hierarchy Framework. Three explicit tiers—FIX (local patch), RESOLVE (dependency / root cause), PROBLEM SOLVE (architecture)—so I know what kind of turn I am buying before I spend one. This is how I’ve been intuitively managing the GenAI in specific environments.

The important thing to remember is that unless it is a generic and trivial thing, context—references, an info-dump, a panel—matters more as you go up the hierarchy.

┌─────────────────────────────────────────────┐
│ PROBLEM SOLVE: Strategic/Architectural     │
├─────────────────────────────────────────────┤
│ RESOLVE: Structural/Systematic             │
├─────────────────────────────────────────────┤
│ FIX: Tactical/Local                        │
└─────────────────────────────────────────────┘

That matters for this post because a PROBLEM SOLVE turn and a FIX turn do not cost the same on capped subscriptions. I reserve planning-grade models for the top tier; burning them on FIX is how you end up with four turns and an empty weekly pool.

Full lab note (recipe card on /lab/notes): Directive Hierarchy Framework →

Early Impressions: Cursor, Manus, and Local Models

One of my earliest impressions was that Cursor is / was amazing and pivotal. Manus had the better overall agentic workflow and did helpful information gathering that could assist with research. Manus had the lead on that for a couple of years before OpenAI, Google / DeepMind, and Anthropic had it.

I was easily able to make a simple 2D game in Unity that feature-wise was slightly more complex—it had different ways of guessing a number or letter in two more Dict<Key,Pair> types between a BinaryAscii and old-phone numpad style input. The set up in Unity was the longest part of that. Then when Cursor was a thing, it made refactoring and iterative version control that much better. That was the main point of less friction with usage across different projects.

Also, local agentic assistance was attempted briefly with OpenLlama much later in 2025. The biggest issue is hardware resources that allows this—which is the same from a homelab to a workstation or desktop. That still holds in 2026, especially as GPU and RAM prices have increased, which makes the subscription route the better long-term plan. If I had a research stipend and funding, I’d have made my own local lab by now with an OpenLlama model, but server inference still remains the ideal even with my normal usage and workflow.

The $20 Baseline

The main state of current IDE usage sits between Anthropic and OpenAI’s $20 subscription value as the lowest common denominator for efficient and generous rate limits. This should be seen as a serious look into switching to another IDE rather than going from $100 ↔ $100 in between AI inference providers—not as a casual model preference swap.

tldr; OpenAI’s overall tiers are a better usage per value across the board than Anthropic, which makes both Claude 4.6 and Opus 4.7 only for planning.

I recently (as of May 27/28 of 2026) tried using two different AI models to compare usage and such—and holy shit, as a normal OpenAI user, I couldn’t get anything done at all for Anthropic’s normal 20 buck sub. OpenAI? That 20buck sub absolutely helped.

Anthropic hemorrhaged my usage immediately. That sounds vague until you look at how the cap is actually built.

The rate limit is hyperstructured now. There is a weekly rate limit that functions as the primary pool—effectively a per-month allowance separated into four weeks. There is also a five-hour rate limit that functions as a secondary gate on top of it. Both intervals consume against the same overall budget; the five-hour window does not protect the weekly pool. It only controls how fast you can spend it.

In practice, most of my usage was hemorrhaged by the five-hour window first—but the weekly cap is what actually ends your ability to work agentically for the rest of the period if you are on the $20 tier.

OpenAI is extremely generous in comparison and has become the standard. Opus 4.7 and Opus 4.6 are honestly great for planning and debugging; but all of the usage goes in a snap. Like four turns.

Anthropic Rate Limits vs OpenAI

Back to what that looked like in a real session.

The issue is that 4 turns and the 5 hour rate limit (secondary) usage of the weekly limit (primary usage of the per month rate limit separated into 4 weeks) was used up in less than 1 hour. As in with both its intervals in rate consumption of a cap of within 5 hours. So if you use the 5 hour limit in 4 hours you have to wait an hour to continue. That’s still using the primary usage pool of the weekly.

With the OpenAI at the same workflow it takes at least 3 hours of multiple turns to hit that. The key comparison metric is that the Anthropic model is nearly equal to marginally-to-mid better than the current OpenAI model. But it costs more in explicit token output. It has a thinking and then a route for deep thinking, most of the usage goes towards that, then the response it gives it’s very detailed and informative however the focus flow is interrupted.

That is the trade in plain terms: you are paying for depth on both sides of the turn—inference and visible answer—and the subscription caps punish that shape of work harder on Anthropic at $20 than OpenAI’s separated pools do.

The $100 Tier

I don’t see the point in paying the $100 / month Anthropic subscription for more rates. Because comparatively, that overall amount is substantially better usage and experience with OpenAI’s same price tier of $100—with a vastly heavier usage as rate restrictions lift as the user goes up per tier.

At the same nominal price, OpenAI’s ladder still reads like you are buying capacity. Anthropic’s ladder, in my usage, often reads like you are buying permission to burn faster on the same structural limits—especially if your workflow is agentic and multi-turn.

OpenAI’s Dual Usage Pools

The primary differentiator that makes OpenAI’s subscription plan work for engineering is that for $20, the general GPT Conversation usage and the Coding usage are two separate usage rate pools. It’s done in a way that I actually don’t see the point in using the general usage pool for coding contexts unless it’s about a one-shot question—something like:

“I’m considering a coding pattern implementation—which would it be better for [task usage] considering [constraints] + required space-time complexity?”

In the Code IDE usage context, it’s more like a RESOLVE-tier prompt from the Directive Hierarchy Framework:

“I’m having a specific issue with this part of the project. I need you to help me track the dependencies of where an issue may have occurred within these given git branch IDs—the main point of reference are these imports / usings for this specific class / function def. The stated clear point that this specific feature was working was at [specific git commit id]. Is there anything I may have missed?”

That second shape is what burns turns on any provider—and it is the shape I actually want for agentic coding, not a one-shot FIX. OpenAI’s separation means those turns preferentially hit the coding pool, which is why the $20 tier stays usable for day-to-day agentic work. Anthropic’s mixed surface means planning-grade models and coding-grade models are still competing for the same scarce intervals described above.

Google Antigravity

On the side, because I have used Google’s Antigravity and models: the best experience I had—where I’d use that over other models—was during and around 03/2025, where it was generally the preferred model for implementing. However 3.1 Pro / Low and 3.5 Flash are currently, in my usage experience, the 3rd—lacking but useful for trivial implementations. Flash is helpful, but its reliability and instruction interpretation is worse than normal 3.1 despite being faster overall.

High, Low, Medium are more for how much a model should use to produce an output. Gemini 3.1 Pro High means that it will potentially use a high amount of tokens for giving a response, which is after the user submits their turn for the conversation with the CLI / App Conversation Box.

Cursor vs Antigravity

Cursor’s Composer 2.5 and Mixed routing is legitimately a better value than Antigravity. The only pro in using Antigravity is that it gives a smidge of Anthropic use. There is no Terminal, and the IDE version of Antigravity was set to be deprecated in favor of a Coding-Chatbox app.

OpenAI separates it into ChatGPT (Conversation App) and Codex (Coding App). Anthropic has a mixed integration main app—that when I tried to use two chat instances inside of the Coding workflow section, it crashed the app, and that secondary chat history was missing despite having at least two turns.

That crash is not just inconvenience. It matches the billing-side experience: one pool of state, one pool of limits, one surface trying to be both conversation and coding workflow.

Higher Tiers and Business Usage

Of course, the important nuance is that with higher tiers this scales to vastly high usage rates across the board and could potentially be unlimited in capacity based on value.

For appropriate business usage, baseline lowest common denominator in pricing should be evaluated for skill. AI costs are higher if you go directly for Anthropic for Engineering and Security purposes, because the thinking models and Opus are great—but it’s substantially more expensive in a serious capacity compared to OpenAI and just hiring an Engineer with sufficient experience. A Junior Engineer or programmer with systems thinking using OpenAI’s plan could have the same efficiency overall as just using Anthropic—if the bottleneck is turns per dollar rather than raw model IQ on a single prompt.