Choosing Between Claude, Codex, and GLM‑5.1 for Code Generation: When to Use Each

Claude for Complex Projects

Claude Opus (Opus 4.8) consistently ranks near the top of benchmarks such as SWE‑bench, handling large‑scale refactoring and multi‑file changes with stable results. It is praised for reliability in long‑running code modifications, but its cost is high (input $5 / M tokens, output $25 / M tokens) and it can be blocked in China, which adds operational risk.

Codex: Fast and IDE‑Integrated

Since OpenAI released GPT‑5.5 in April, Codex runs on that model. Its strongest feature is the agent loop that writes code, runs tests, and fixes bugs, achieving 58.6% on SWE‑bench Pro and 82.7% on Terminal‑Bench 2.0, comparable to Cursor and VS Code extensions. Pricing is $5 / M input tokens and $30 / M output tokens; a “Fast” mode speeds generation by 1.5× at 2.5× the cost, making it suitable for urgent tasks.

GLM‑5.1: Domestic Open‑Source Breakthrough

Zhipu’s open‑source GLM‑5.1, released in early April, surpassed GPT‑5.4 and Claude Opus on SWE‑bench Pro, ranking third globally and first among open‑source models. It can run a single task continuously for eight hours and, as of May 22, a “high‑speed” version outputs 400 tokens per second, setting a new speed record for large‑model APIs.

The cache‑token price rose 10% to align with Claude Sonnet 4.6, reflecting confidence in its performance. Its strengths include stable domestic network access, native Chinese understanding, and ecosystem compatibility, making it a strong choice for projects targeting Chinese users.

Recommendation

Instead of searching for a single “best” model, assign tasks by difficulty: use Claude Opus for complex architecture and security‑critical core code, Codex for rapid daily coding and IDE‑adjacent workflows, and GLM‑5.1 for domestic, Chinese‑language projects where cost‑effectiveness and network stability matter. Mature teams can implement a routing layer that directs simple jobs to cheaper models while reserving the expensive Opus for the hardest problems.