GPT-5 vs Claude 4 for coding via Codex

Codex Key gives OpenAI-compatible access to GPT-5.4, GPT-5.5 and Codex. A frequent question: how do they stack up against Claude Sonnet 4 / Opus 4? Here's the honest breakdown — no marketing — on real coding tasks.

TL;DR

Scenario	Winner	Why
IDE autocomplete	Codex (codex-5.3)	Faster, cheaper (×0.9), code-tuned
Chat-mode function generation	GPT-5.4	Universal, ×1.0, fast streaming
Large module refactor	Claude Sonnet 4	Better at context-aware edits
Repo-wide architectural review	GPT-5.5 or Claude Opus 4	Reasoning parity; GPT-5.5 cheaper via Codex Key
Long context (300k+)	GPT-5.5	400k window, more stable focus

Benchmarks vs reality

On public benches (SWE-bench Verified, HumanEval+) GPT-5.5 and Claude Opus 4 trade blows within 2-3%. But benchmarks lie about real IDE work. In practice three things matter more:

1. First-token latency — decides whether autocomplete feels alive
2. Tool-use stability — how often the agent breaks JSON schema
3. Cost per merged ticket, not cost per million tokens

Coding via Codex CLI

codex --model gpt-5.4 "add a rate limiter to backend/app/api/routes.py"

Measurements on the same task (50 runs, refactor of ~400 lines of FastAPI):

Model	Correct-PR rate	Avg latency	Cost / PR*
codex-5.3	71%	4.2s	$0.018
gpt-5.4	78%	5.8s	$0.024
gpt-5.5	86%	12.4s	$0.11
claude-sonnet-4	81%	9.1s	$0.09**
claude-opus-4	87%	18.6s	$0.42**

*Team plan. **Direct Anthropic billing, for reference.

What to pick for what

Autocomplete in Cursor — codex-5.3. Latency wins; quality delta on short suffixes < 3%.

Endpoints, tests, migrations — gpt-5.4. Price/quality sweet spot.

Heavy multi-file refactor — gpt-5.5. In our runs it's more stable than Claude Sonnet 4 on long diffs touching >5 files.

Architectural review, design docs — gpt-5.5 with reasoning_effort: high. Output quality matches Opus 4 at a third of the price.

Example: routing inside one project

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["CODEX_KEY"],
    base_url="https://api.codexkey.ru/v1",
)

def route(task_type: str) -> str:
    return {
        "autocomplete": "codex-5.3",
        "function-gen": "gpt-5.4",
        "refactor-multi": "gpt-5.5",
        "review": "gpt-5.5",
    }.get(task_type, "gpt-5.4")

resp = client.chat.completions.create(
    model=route("refactor-multi"),
    messages=[{"role": "user", "content": "..."}],
    extra_body={"reasoning_effort": "high"},
)

Where Claude is genuinely better

Honestly: Claude Sonnet 4 wins on two classes of work:

• Long prose explanations of code for docs (more natural voice)
• Code review with soft-skill commentary ("why this approach is risky")

If those cases are critical, keep Claude in parallel. Codex Key doesn't try to replace Anthropic; we give cheap, fast access to the GPT-5 family without an OpenAI account or VPN.

Bottom line

For 90% of coding tasks, the codex-5.3 + gpt-5.4 + gpt-5.5 combo through Codex Key covers your needs more cheaply than a mixed OpenAI + Anthropic stack. Claude remains a fine choice for documentation and discursive review.

Start with gpt-5.4 as default, escalate to gpt-5.5 where it's visibly better, and switch to codex-5.3 inside the IDE for savings.