Claude Code vs Cursor vs Codex: Benchmarking the Models, Not the IDEs

By Promptster Team · 2026-05-05

Every "Claude Code vs Cursor vs Codex" comparison you've read is really a comparison of harnesses — the wrapper code, tool selection, context-assembly logic, and system prompts each tool layers over its underlying model. Swap the harness and you swap most of the perceived difference. Strip the harness entirely and you're left with a narrower question: which model actually performs best on coding tasks?

We ran the underlying-model benchmark. Four models on a real debugging prompt. No IDE tools, no retrieval, no agent loop. Raw model → raw answer.

What Each Tool Actually Uses

What the harnesses add: codebase search, symbol graph traversal, edit proposals with apply-logic, error loop with compile/test feedback, multi-turn context across files. All valuable. All orthogonal to "did the model understand the bug."

The Test

We asked four models to debug this subtly broken Python function:

def is_balanced(s):
    stack = []
    pairs = {')': '(', ']': '[', '}': '{'}
    for char in s:
        if char in '([{':
            stack.append(char)
        elif char in ')]}':
            if not stack or stack[-1] != pairs[char]:
                return False
            stack.pop()
    return True

The bug: return True at the end should check that the stack is empty. Inputs with unclosed brackets (e.g., "(((" or "([{}") return True incorrectly.

We asked for three labeled sections: BUG explanation, FAILING TEST assertion, FIXED function.

Results

Model Found the bug Quality of explanation Fix Latency Cost
Gemini 2.5 Flash Lite ✅ ✅ Clean, one paragraph, pinpointed ✅ return not stack 1,035 ms $0.000103
GPT-4o ✅ ✅ Clean ✅ return not stack 1,506 ms $0.002353
Claude Sonnet 4.5 ✅ ⚠️ Muddled — started explaining a different bug, pivoted ✅ return len(stack) == 0 5,541 ms $0.005160
OpenAI o4-mini ❌ Empty response — — 6,293 ms $0.003753

Gemini 2.5 Flash Lite produced the cleanest answer at 1/50th of Claude Sonnet's cost. Its explanation was the most focused, its failing test (assert is_balanced("([{}") == True — a test that currently passes but should fail) was the sharpest, and its fix was idiomatic Python.

Sonnet's explanation started by misdiagnosing a different bug (something about the pop logic being wrong) and then corrected itself mid-paragraph. It still arrived at a correct fix, but the reasoning path was confused. GPT-4o was competent and fast.

o4-mini returned nothing. This is the second benchmark in a row where OpenAI's reasoning tier produced an empty response field — 800 output tokens consumed (billed), 0 visible characters. We covered this in detail in reasoning tokens aren't free; the short version is that o-series reasoning summaries aren't always returned through the default API path.

What This Means for the Tool Choice

The harness is doing most of the work. If you switch Claude Code to use GPT-4o (not possible today, but hypothetically), you'd still get most of Claude Code's UX — the apply-diff loop, the codebase search, the multi-turn context. The model would answer this particular bug differently, but the product experience is 70% harness.

The model choice matters for price, not so much for quality on in-distribution tasks. On this debugging task, Gemini 2.5 Flash Lite's answer was indistinguishable in quality from GPT-4o's. The difference was a 23x price spread.

Reasoning-tier models are not the obvious default for coding. On our benchmark, o4-mini produced no visible output; in practice, reasoning models add latency (2-10 seconds of invisible thinking) and have API-parsing quirks that break in unexpected places. For code, a good non-reasoning model with a solid harness is usually faster and cheaper.

Picking a Tool by Model Access

If the underlying models matter to you, here's the practical guide:

All three are good. The "best" depends on your repo size, your budget, and your tolerance for different harness quirks.

The Bigger Claim

IDE benchmarks that compare Claude Code to Cursor by running "the same task in both" are measuring harness + model, not model. If your decision criterion is product experience, that's correct — you're choosing the product you'll use. If your decision criterion is model capability, benchmark the models directly via an API-level test like this one.

To benchmark your own task set across coding-relevant models, the Promptster comparison view takes about a minute to set up; the public API supports scripting it into a regression suite.

For the broader question of where AI coding tools are headed, see the best MCP tools for AI coding in 2026 and our upcoming Aider + Promptster tutorial.

Tests run 2026-04-19 via the Promptster MCP server. Temperature 0.1. Single-prompt, no harness, no retries.