Model and Coding Assistant Comparisons

Model and Coding Assistant Comparisons | Generated by AI

Home 2026.05

Question: Does GPT-5.5 beat Claude Opus 4.7? And in the coding assistant space, does Codex beat Claude Code?

Answer:

The honest answer to both: it depends on the axis you’re measuring. Neither is a clean winner. Here’s the breakdown as of late May 2026.

GPT-5.5 vs Claude Opus 4.7

Both dropped in the same week in April 2026 — Opus 4.7 on April 16, GPT-5.5 on April 23. The benchmarks split along workflow type:

Opus 4.7 leads:

Opus 4.7 leads on 6 of the 10 shared benchmarks — specifically the reasoning-heavy and review-grade tests: GPQA Diamond, HLE (with and without tools), SWE-Bench Pro, MCP Atlas, and FinanceAgent v1.1.
Opus 4.7 wins the coding benchmarks — SWE-bench Pro (64.3%), SWE-bench Verified (87.6%), CursorBench, GPQA Diamond.

GPT-5.5 leads:

GPT-5.5 leads on the long-running tool-use tests: Terminal-Bench 2.0, BrowseComp, OSWorld-Verified, and CyberGym. It leads BrowseComp by +5.1pp and CyberGym by +8.7pp.
GPT-5.5 wins the agentic and knowledge-work benchmarks: Terminal-Bench, GDPval, OSWorld, and Tau2-bench.

Token efficiency: GPT-5.5 uses 72% fewer output tokens than Claude Opus 4.7 on equivalent coding tasks — which matters significantly as a cost and architecture decision at scale.

Pricing: Both are $5.00/1M input tokens. For output, Opus 4.7 is $25/1M vs GPT-5.5 at $30/1M — so Opus 4.7 is actually cheaper on output.

TL;DR on models: Opus 4.7 = better at deep reasoning, large-codebase architectural tasks, SWE-bench Pro. GPT-5.5 = better at autonomous terminal loops, browser-based agents, and token efficiency. GPT-5.5 holds a slight edge on precise tool use and file navigation; Opus 4.7 performs better on broad architectural reasoning across large codebases.

Codex CLI vs Claude Code

This is architecturally a different comparison — they’re not just model swaps, they have fundamentally different designs.

Architecture difference: Claude Code runs as a CLI tool operating directly on your local files. Codex is a fully agentic cloud coding environment — it runs tasks in sandboxed cloud containers, not on your local machine, integrated into ChatGPT alongside browsing and image generation.

Benchmarks as of May 2026:

Codex CLI wins on raw SWE-bench Verified (88.7% vs 87.6%) and Terminal-Bench 2.0 (82%, #1). Claude Code wins on SWE-bench Pro (64.3% vs 58.6%) — the harder, contamination-resistant benchmark — and on multi-file refactoring and large-codebase work with 1M context on Opus 4.7.

Benchmark contamination caveat: OpenAI itself stated in early 2026 that SWE-bench Verified is increasingly unreliable due to contamination concerns, and recommended SWE-bench Pro as the more trustworthy option. That shifts the credible score to Opus 4.7/Claude Code by ~5.7 points.

Open source: Codex CLI is fully open-source (Apache-2.0, Rust-native, 82,900+ GitHub stars). Claude Code (124,000+ stars) ships more frequently but is proprietary.

Multi-agent architecture: For greenfield tasks that are independent of each other, Codex’s isolation model wins. For complex refactors where subtasks have dependencies, Claude Code’s coordinated agent teams win — spawning researcher, implementer, and test-writer agents with dependency ordering, each with their own context window.

My Take For Your Stack

Given your workflow (CLI-first, large codebases, AI engineering, building agents):

Model choice: Use Opus 4.7 for deep codebase reasoning and agent orchestration. Use GPT-5.5 when you need autonomous terminal loops or browser-based agents (CyberGym-style tasks).
Tool choice: Claude Code fits better for your vibe — local-first, terminal-native, works on your actual files. Codex shines for async background tasks (fire-and-forget PRs while you work on something else).
Pragmatic: A growing pattern in 2026 is subscribing to both ChatGPT Plus and Claude Pro (~$40/month total) and using each for what it’s best at — cross-checking hard problems between models is where you learn the most.

References:

Back Donate