Every developer has now pasted code into ChatGPT or watched GitHub Copilot autocomplete a function. If that’s your only exposure, it’s easy to conclude that coding with large language models (LLMs) isn’t “there yet.” In practice, model quality and specialization are moving so fast that the experience you had even eight weeks ago is already out of date. AI code generators are evolving rapidly, becoming essential tools in modern development workflows. OpenAI, Anthropic, and Google have each shipped major upgrades this spring, and OpenAI quietly added an “o-series” of models aimed at reasoning.

Below is a field report from daily production use across five leading models. Treat it as a snapshot, not gospel—by the time you read this, a point release may have shuffled the rankings again.

OpenAI GPT-4.1: UI whisperer, not my main coder

OpenAI’s GPT-4.1 replaces the now-retired GPT-4.5 preview, offering a cheaper, lower-latency 128k-token context and better image-to-spec generation. It’s still solid at greenfield scaffolding and turning screenshots into code, but when the task is threading a fix through a mature code base, it loses track of long dependency chains and unit-test edge cases.

When to call it: Design-system mock-ups, API documentation drafts, converting UI comps into component stubs.
When to skip it: After your initial scaffold.

Anthropic Claude 3.7 Sonnet: The dependable workhorse

Anthropic’s latest Sonnet model is still the model I reach for first. It strikes the best cost-to-latency balance, keeps global project context in its 128k window, and rarely hallucinates library names. On tough bugs, it sometimes “cheats” by adding what it calls “special case handling” to the code under test (watch for if (id==='TEST_CASE_1 data')-style patches). Sonnet also has a habit of disabling ESLint or TypeScript checks “for speed,” so keep your linter on.

Sweet spot: Iterative feature work, refactors that touch between five and 50 files, reasoning over build pipelines.
Weak spot: Anything visual, CSS fine-tuning, unit test mocks.
Tip: grep your code for the string “special case handling”.

Google Gemini 2.5 Pro-Exp: The UI specialist with identity issues

Google’s Gemini 2.5 release ships a one-million-token context (two million promised) and is currently free to use in many places (I’ve yet to be charged for API calls). It shines at UI work and is the fastest model I’ve used for code generation. The catch: If your repo uses an API that changed post-training, Gemini may argue with your “outdated” reality—sometimes putting your reality in scare quotes. It also once claimed that something in the log wasn’t possible because it occurs in the “future.”

OpenAI o3: Premium problem solver, priced accordingly

OpenAI’s o3 (the naming still confuses people who expect “GPT”) is a research-grade reasoning engine. It chains tool calls and writes analyses, and it will pore over a 300-test Jest suite without complaint. It is also gated (I had to show my passport for approval), slow, and costly. Unless you’re on a FAANG-scale budget or you’re unable to resolve a bug yourself, o3 is a luxury, not a daily driver.

OpenAI o4-mini: The debugger’s scalpel

The surprise hit of April is o4-mini: a compressed o-series variant optimized for tight reasoning loops. In practice, it’s 3-4× faster than o3, still expensive via the OpenAI API, but throttled “for free” in several IDEs. Where Claude stalls on mocked dependencies, o4-mini will reorganize the test harness and nail the bug. The output is terse, which is surprising for an OpenAI model

Automated contract tests, incremental linting, and commit-time diff review remain mandatory. Treat models as interns with photographic memory. They’re excellent pattern matchers, terrible at accountability. (Author’s note: Ironically, o3 added this part when I asked it to proofread but I liked it so much I kept it.)

Bottom line

If you tried GitHub Copilot in 2024 and wrote off AI coding, update your tool kit. Claude 3.7 Sonnet delivers day-to-day reliability, Gemini 2.5 nails front-end ergonomics, and o4-mini is the best pure debugger available—provided you can afford the tokens or you have a lot of patience. AI code generators now offer specialized strengths, making it easier to tailor tools to specific development needs. Mix and match. You can always step in when a real brain is required.

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Source: https://www.infoworld.com/article/3980273/sizing-up-the-ai-code-generators.html