- 20
- July
Claude vs ChatGPT is the coding-assistant debate development teams argue about most in 2026. The shortest honest answer is that the two are very close, and neither wins outright — leaderboard leads flip by benchmark and by month. What should drive your decision is not a single benchmark score but how well the tool fits your team's workflow. This article compares the two vendors' coding agents — Claude Code from Anthropic and Codex from OpenAI — honestly, separating "numbers the vendor reports itself" from "independent evaluations", and points out what a Thai business should weigh before committing (for the wider model rankings, see the latest AI model comparison 2026).
In one line: Claude Code (Anthropic) shines on complex multi-file refactor quality and IDE depth; Codex (OpenAI) shines on async PRs, sandboxing, and lower per-task cost — they are roughly evenly matched, so choose on workflow fit and pilot on your team's real tasks, not on a single benchmark score.
The two vendors' coding tools: Claude Code and Codex
First, be precise about what we are comparing. "Claude" and "ChatGPT" are general chatbots, but for serious software work each vendor ships a dedicated coding agent. Anthropic's is Claude Code; OpenAI's is Codex. Neither is just a chat box — both are agents that do real work: they open Pull Requests, run tests, fix bugs, and refactor across many files, working from a CLI, an IDE plugin, or the cloud (for a deeper head-to-head, see Codex vs Claude Code compared).
Each vendor's recommended coding model as of mid-2026: Claude Opus 4.8 (claude-opus-4-8, released 28 May 2026) is Anthropic's recommended coding model, and GPT-5.5 (released 23 April 2026) is OpenAI's default in Codex and ChatGPT. Both vendors also have a specialist tier — Anthropic offers Fable 5, a top coder priced above Opus, and OpenAI offers GPT-5.3-Codex — but this article keeps the two flagships as the main axis.
| Workflow dimension | Claude Code (Anthropic) | Codex (OpenAI) |
|---|---|---|
| Working style | CLI + IDE plugin + cloud, leans toward real-time pairing with the developer | CLI + IDE + cloud, leans toward delegating async work and reviewing results later |
| Commonly cited strength | Complex multi-file refactors, readable code quality, IDE depth | Async PRs, many parallel tasks, isolated sandboxes, low per-task cost |
| Running tests / opening PRs | Yes — runs commands, edits files, summarises for review | Yes — designed to run many tasks in parallel sandboxes |
| Default model | Claude Opus 4.8 (Fable 5 as top tier) | GPT-5.5 (GPT-5.3-Codex as code-specialist) |
| Plugins / extensions | Plugin and subagent system (see Claude Code vs plugins) | Integrates with OpenAI tooling and ChatGPT |
The common summary developers land on: Claude Code for hard multi-file refactor quality and IDE depth; Codex for async PRs, sandboxing, and per-task cost. That is a workflow heuristic, not a hard rule — both tools move very fast.
How good are they — separate "vendor numbers" from "independent evals"
This is the point people miss most when reading AI benchmark news. Many widely-quoted figures are vendor-reported — measured by the vendor in an environment the vendor chose — not neutral third-party numbers. The table below adds an explicit column stating which figure is vendor-reported and which varies by source.
| Benchmark | Claude Opus 4.8 | GPT-5.5 | Number status |
|---|---|---|---|
| SWE-bench Verified (real bug fixes) | ~88.6% | Varies by source (~82.7% to ~88.7%) | Opus = vendor-reported · GPT-5.5 = conflicting across sources / harnesses — do not cite as one number |
| Terminal-Bench 2.0 (agentic terminal tasks) | — | 82.7% | OpenAI-reported (consistent across sources) |
| SWE-bench Pro (secondary comparison) | Claude Opus 4.7 = 64.3% | ~58.6% | From a secondary comparison — directional, not definitive |
The key nuance: Opus 4.8's ~88.6% on SWE-bench Verified is reported by Anthropic itself (surfaced via secondary sources and shown as a chart image in the primary announcement), not produced by an independent body. For GPT-5.5, the SWE-bench Verified figure conflicts across sources (roughly 82.7% in some, roughly 88.7% in others) because different eval harnesses are used — so it should not be stated as a single number. The figure you can quote with more confidence is Terminal-Bench 2.0, which OpenAI reports at 82.7% and which is consistent across sources.
Don't decide on one score: leaderboard leads flip by benchmark and by month, and the same metric shifts with the harness used to measure it. The lesson — which ties to our honesty principle — is that a benchmark is not your codebase. A model with a higher SWE-bench score will not automatically work better with your team's architecture, languages, and tooling. Choose on workflow fit, not on a score.
The independent eval every team should read: METR — "felt faster, measured slower"
If there is one study every development team should read before deciding, it is the randomized controlled trial from METR, an independent nonprofit, published in July 2025. It had 16 experienced open-source developers complete 246 real tasks in mature repositories they already knew well, randomly assigning which tasks were allowed to use AI tools and which were not.
The counter-intuitive result: when allowed to use AI, these developers were about 19% slower, yet afterward they estimated that AI had made them about 20% faster. That gap between "perceived" and "measured" is the most important takeaway, and it comes from an independent body — not a vendor claim. Read it with its caveats, though: a small sample, highly experienced developers on codebases they know deeply, and early-2025 tooling (Cursor + Claude 3.5/3.7), which is older than today's. Results for teams on newer code, or for less-experienced developers, may differ.
Before assuming AI speeds up your team: (1) Felt fast ≠ measured fast — per METR, gut-feel estimates can run opposite to measured time, so measure on your team's real tasks rather than trusting a feeling or a demo clip. (2) Data governance — both Claude Code and Codex are cloud SaaS that process on US servers by default. Sending source code or trade secrets to the cloud needs a policy. For Claude's Team/Enterprise plans, data is not trained on by default, retention is 7 days, and Zero Data Retention (ZDR) is available — check each plan's policy before connecting it to an internal codebase.
Cost and quality in real work: read with caveats
Beyond benchmarks, one documented case had a developer refactor the same Express.js project on Codex for about $15 versus about $155 on Claude Code — yet when blind reviewers rated the output, they judged Claude's cleaner (about 67% to 25%). These figures come from a single blogger's write-up, so treat them as directional, not a broadly-verified statistic. What they echo from the overview above: Codex tends to win on per-task cost, while Claude Code tends to score on code quality in complex work (for using AI to review code, see code review with Claude Code).
When Claude fits vs when Codex/ChatGPT fits
Instead of "which is better," the question with a usable answer is "which task suits which tool." The table below sorts by work and team type.
| Claude Code fits when... | Codex / ChatGPT fits when... |
|---|---|
| Multi-file refactors that demand high quality and consistency | You want to delegate many sub-tasks async and collect PRs later |
| The team mainly pairs with AI in real time inside the IDE | You want isolated per-task sandboxes to bound the blast radius |
| You prioritise readability / maintainability of the resulting code | Per-task budget is the main constraint and you want low cost per task |
| Your org already uses ChatGPT/Claude and wants Anthropic-side depth | Your team already lives in the OpenAI/ChatGPT ecosystem |
How to actually choose — pilot on your own tasks: don't decide from benchmark scores or demo clips. Pick 5–10 real items from your team's backlog (bugs, small features, and refactors), run the same set on both tools in your real codebase, and measure three things: (1) actual time taken — not perceived time, per the METR lesson; (2) code quality that passes review; and (3) cost per task. Two weeks of this kind of pilot gives a more accurate answer than a month of reading reviews, because it measures on your team's own context.
Pricing and access (USD)
Prices change often and should be checked on the official pages at purchase time, but the access tiers in 2026 look like this. The point teams get wrong most: Claude Code is included with every paid plan (Pro, Team Standard, Team Premium, Max, Enterprise). The difference between Team Standard ($20/seat) and Team Premium ($100/seat) is not whether you get Claude Code but how much usage you get (Premium is about 5× Standard).
| Vendor / tool | Plan that unlocks the coding agent | Notes |
|---|---|---|
| Claude Code (Anthropic) | Every paid plan: Pro ($20/mo), Team Standard ($20/seat), Team Premium ($100/seat/mo annual), Max, Enterprise | Standard vs Premium differ by usage volume (Premium ~5×), not feature access — see Claude Team Premium |
| Codex (OpenAI) | Comes with ChatGPT Plus ($20/mo) and up | ChatGPT Business ~$25–30/seat/mo · Enterprise custom pricing |
For buyers in Thailand, the figures above are USD list prices and do not include the 7% VAT a local buyer adds. Grand Linux Solution (GLS) sells and supplies Claude for Thai organisations, issuing a quote and a tax invoice in Thai baht and coordinating PO and tax paperwork to fit local procurement. Onboarding, training, integration, and support are paid optional add-ons — not bundled free — and an annual Claude contract is a 12-month upfront commitment with no mid-year refund, so it is worth thinking it through carefully rather than rushing (for alternatives on the Anthropic side, see Hermes Agent vs Claude Code).
Where ERP fits in (stated plainly)
The coding-model leader can change every few months — today it might be Claude, tomorrow GPT or another model. What pays off no matter which tool wins is your own clean, structured operational data. That is where a back-office system like ERP fits in — not because ERP is AI, but because ERP is the layer that keeps your accounting, inventory, partner, and budget data "clean, organised, and yours," ready to plug into whichever AI you choose.
To be plain about scope: Saeree ERP is not a coding tool and does not embed Claude or Codex. Its in-product AI assistant is still in development (training phase) — not yet a usable feature. What ERP does today, and what matters most, is getting your data ready so you can choose any AI tool from a position of leverage, without locking yourself to one vendor.
"Don't pick a coding assistant on a single benchmark score — the lead flips every month, and a number on paper is not your codebase. Pilot on your team's real work, measure the time and quality you can actually verify, and let the results decide."
- The Saeree ERP Team
References
- Anthropic — Claude Opus 4.8 (official announcement + vendor-reported benchmark figures)
- METR — Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity (independent RCT)
- The Register — coverage of the METR study (developers slower yet felt faster)
- llm-stats — SWE-bench Verified leaderboard (multi-model comparison)
- OpenAI — Codex documentation (coding agent; GPT-5.5 / GPT-5.3-Codex)
- Terminal-Bench — leaderboard (source of the Terminal-Bench 2.0 figure)
Information last verified on 20 July 2026 — benchmark figures and prices change often; re-check the official sources before deciding.
Considering Claude for your development team?
Grand Linux Solution sells and supplies Claude for Thai organisations, issuing a quote and tax invoice in Thai baht and coordinating PO and tax paperwork to fit your procurement. An annual contract is an upfront commitment — think it through, no need to rush. Talk to us to pick the plan that includes Claude Code and matches your team.
Request a QuoteTel 02-347-7730 | sale@grandlinux.com
