ChatGPT alternatives
By Daniel Reeve · tested and updated September 2026
For coding, the best AI is not the one that writes the longest answer. It is the one that understands constraints, asks clarifying questions, produces testable code and explains tradeoffs.
Quick answer
Do not choose on context window alone, tempting as the numbers are. A model that fits your whole repository but invents an API is worse than a smaller one that asks a clarifying question.
Decision map
Here is the failing function and stack trace. Explain the bug, propose the smallest fix and add tests.
Imports, edge cases, performance, security, migrations and backward compatibility.
Ask one model to write, another to review, and your test suite to decide.
By coding task
| Area | Useful for | Watch out for |
|---|---|---|
| Debugging | Good models can explain likely causes and isolate failing assumptions. | They can hallucinate APIs or miss project context. |
| Code generation | Useful for boilerplate, tests, refactors and examples. | Never merge untested generated code. |
| Architecture | Claude and ChatGPT can reason through tradeoffs well. | Ask for risks, alternatives and migration steps. |
| Comparison | MultipleChat helps compare implementations from several models. | Pick the simplest correct version, not the flashiest. |
Verified specifications
For code, the context window decides how much of a repository you can show the model at once. Figures read from vendor documentation on 2026-09-10.
| Model | Context window | Published API price | Note |
|---|---|---|---|
| GPT-6 Astra | 1.05M tokens | see OpenAI docs | 128K max output. |
| Claude Opus 5 | 1M tokens | see Anthropic docs | 128K max output. |
| Claude Sonnet 5 | 1M tokens | see Anthropic docs | 128K max output. |
| Grok 4.6 | 500K tokens | $2.00 in / $6.00 out per 1M tokens | Knowledge cutoff 1 Feb 2026; needs search tools for anything later. xAI docs |
| Claude Haiku 4.5 | 200K tokens | see Anthropic docs | 64K max output. Cheapest Claude tier. |
The workflow that works
Have one model produce the change and a different model review it against the original requirement, without telling the second what the first was trying to do. Models trained differently fail differently, so the reviewer catches what the writer invented.
Neither model gets to merge anything. A green test suite is the only evidence that counts, and it is the one piece of the workflow that is not probabilistic.
Sources
Every figure on this page was read from the official documentation below on 2026-09-10. Prices, limits and model names change without notice — the source is authoritative, this page is not.
FAQ
On published figures, GPT-6 Astra lists 1.05M tokens and Claude Opus 5 and Sonnet 5 list 1M (checked 2026-09-10). As a rough feel, 200K tokens covers a large file with its tests; 1M covers a small service. Anything bigger and you are better off giving the model a map of the codebase and the three files that matter than dumping everything in.
No, and this is the most common misreading of these numbers. Context is how much the model can see, not how well it reasons about it. A model with half the window and better instruction-following will produce fewer broken diffs.
xAI publishes Grok 4.6 at $2.00 per million input tokens and $6.00 per million output tokens, with a 500K context window and a training cutoff of 1 February 2026 (xAI documentation, checked 2026-09-10). That cutoff matters for code: for any library that shipped after it, you need search tools enabled or the model is working from a stale API surface.
No. Every model on this page can produce code that reads correctly, imports a function that does not exist, and fails on the first edge case. Generated code is a draft that has to pass your test suite, your linter and your review before it is merged.
Have one model write the change and a different one review it against the original requirements. Models trained differently fail differently, so the second model tends to catch what the first invented. Your test suite still casts the deciding vote.