DeepSeek vs ChatGPT for Coding: Which One to Use and When

The short answer: DeepSeek, ChatGPT or something else

Use DeepSeek V4 Pro for high-volume coding: quick fixes, boilerplate, tests and many small iterations. On Moleculs it costs 2 credits per run, while ChatGPT-5.5 costs 28. ChatGPT-5.5 is the all-rounder to try when a review or a messy debugging session is worth the extra credits. For long refactors that touch many files, test Claude Sonnet 5 or Claude Opus 5.5 first. Whichever you lean towards, send one real prompt from your own codebase to two or three models before you decide.

How to judge a coding model: the criteria that matter

For day-to-day coding, five things decide which model you should use:

1. Quality on your task. Benchmarks will not tell you how a model handles your stack, your conventions or your legacy code. Only a test on your own code can show that, and the last section explains how to run one.
2. Cost per run. Coding is iterative. You rarely send one prompt. You send ten, and a model that costs 14 times more per run adds up by the end of the day.
3. Input. Can you attach a file, a stack trace screenshot or a screen recording of the bug? Or do you have to paste everything as text?
4. Context size. A 40-line function fits into any model. A whole module plus its tests plus a log needs a model built for long inputs.
5. Workflow. A web chat means you copy code in and out. If you need autocomplete inside your editor, you need a different kind of tool.

The table below is sorted by cost per run on Moleculs, because that is the one number we can state exactly. The numbers come from the Moleculs catalogue as of September 2026. The sections start with the two models you came to compare. After that, the alternatives are grouped by the job they suit. If you want to jump straight in, you can try DeepSeek online and come back to the rest later.

Comparison table: DeepSeek, ChatGPT, Claude, Gemini, Grok, GLM and Kimi

DeepSeek V4 Pro and V4 Flash: cheap runs for high-volume coding

Who it suits: developers who send dozens of prompts a day and treat the model as a fast pair programmer, not an oracle.

What it does well: DeepSeek V4 Pro costs 2 credits per run, so you can afford to iterate. Ask it for a function, then ask for tests, then for a version without a dependency, and it still costs less than one ChatGPT-5.5 run. Both DeepSeek models accept images, video, audio and files, so a screenshot of a stack trace is fine. V4 Flash costs 1 credit and handles the small questions you would otherwise search for: a regex, a date format string, a forgotten flag.

Where it is limited: Flash is a light model. When a task needs several connected steps, move it to Pro or to a heavier model rather than retrying Flash five times.

When not to use it: for a final review of security-sensitive code, don't rely on a single cheap model. Run the same diff through a second model and compare the findings. If you want a head start on phrasing, there are ready-made DeepSeek prompts for common coding requests.

ChatGPT-5.5 and ChatGPT 6 Sol: the familiar all-rounder

Who it suits: people who already know how ChatGPT answers, and teams whose prompts were written with it in mind.

What it does well: ChatGPT-5.5 accepts images (up to 10), video, audio and files. You can attach a failing test file, a screenshot of the browser console and a short recording of the UI bug in one request. That's useful when the code alone doesn't explain the bug and you need to show the surrounding context. ChatGPT 6 Sol sits in the middle at 10 credits per run. It accepts images and files, but not video or audio.

Where it is limited: at 28 credits per run, ChatGPT-5.5 is the most expensive model in this comparison. Using it for every small question burns through credits quickly.

When not to use it: for rapid back-and-forth on small snippets. Use ChatGPT 6 Luna (1 credit) or DeepSeek for that, and bring in ChatGPT-5.5 for the review. All versions are listed on the ChatGPT models page on Moleculs.

Claude Opus 5.5 and Claude Sonnet 5: long, multi-step code tasks

Who it suits: developers doing refactors, migrations and reviews where the answer has to hold together across several files.

What it does well: the catalogue describes Claude Opus 5.5 as Anthropic's flagship for complex reasoning, code and long agentic tasks. Both Claude models accept files and up to 20 images, the most in this list, which helps when a bug report comes with a stack of screenshots. Sonnet 5, at 10 credits per run, is the one to try first on a review. Escalate to Opus 5.5 (20 credits) when Sonnet misses something.

Where it is limited: there is no video or audio input. A screen recording has to become screenshots.

When not to use it: for a quick syntax question. Paying 10 to 20 credits for an answer a 1-credit model gives just as well makes no sense.

Gemini 3.1 Pro and Grok 4.7: large inputs and long context

Who it suits: developers whose bug reports arrive as a pile of evidence rather than a clean snippet.

What it does well: Gemini 3.1 Pro accepts up to 16 images plus video, audio and files. That covers a log file, a set of screenshots and a recording of the crash in one request. At 12 credits per run it costs less than ChatGPT-5.5. Grok 4.7 is billed in the catalogue as xAI's flagship for code, agentic tasks and long context. At 6 credits it is one of the cheaper ways to paste in a large module and ask questions about it.

Where it is limited: Grok 4.7 takes images and files but not video or audio, and it has 10 image slots.

When not to use them: if your inputs are small and text-only, their strengths go unused. DeepSeek V4 Pro or Claude Sonnet 5 will be the better value.

GLM 5.2 and Kimi K3: alternatives worth a test run

Who it suits: developers who want a second opinion from a model family outside the usual three, or who work with very long inputs.

What it does well: GLM 5.2 from Z.ai is described as a flagship for complex and agentic tasks, and it costs 4 credits per run. That makes it a cheap second opinion next to DeepSeek. Kimi K3 from Moonshot AI is a multimodal reasoning model with a 1M-token context listed in the catalogue. That's useful when you paste a long file and need the model to keep track of all of it.

Where they are limited: the catalogue lists no input options for GLM 5.2 (so paste code as text), and Kimi K3 accepts images but no files.

When not to use them: if your workflow relies on attaching files, both will slow you down.

Moleculs: every model above on one subscription

Who it suits: developers who want to test and switch between DeepSeek, ChatGPT, Claude, Gemini, Grok, GLM and Kimi without a separate account and bill for each.

What it does: Moleculs gives you access to third-party models through one web interface and one credit balance. Each model shows its cost per run next to it, so the numbers in the table are what you actually spend. For plan details, see the pricing page.

Where it is limited: Moleculs doesn't build models. It gives you access to other companies' models, and the settings or wrappers may differ from the vendor's own app, so answers can vary slightly. There is no public API, so nothing plugs into your IDE. You paste code or attach files, then copy the answer back into your editor. There is also no side-by-side comparison window: to compare models, you send the same prompt to each one separately.

When not to use it: if you need inline autocomplete while you type, use an IDE assistant instead.

How to test them on your own code in 5 steps

1. Pick a real task. Choose a function with a known bug, a module that needs refactoring or a failing test. Toy tasks flatter every model.
2. Freeze one prompt. Include the code, the error, your constraints (language version, allowed libraries) and the output format you want. Use the exact same text for every model.
3. Run three tiers. One cheap model (DeepSeek V4 Pro), one mid-priced (Claude Sonnet 5 or ChatGPT 6 Sol) and one heavy (ChatGPT-5.5 or Claude Opus 5.5). Save each answer to a file as you go.
4. Check, don't just read. Run the code and the tests. Note whether it passed, what it missed, and how many follow-up prompts it took to get there.
5. Multiply by volume. Cost per run times your runs per day. Pick the cheapest model that passed, and keep a heavier one for the tasks where the cheap one failed.

A good first prompt is one where you can check the answer yourself. Send this ISO 8601 parsing prompt: it asks for a function, type hints, pytest tests and a list of edge cases the code doesn't handle. That last part shows you which model is honest about its gaps. There's more on using AI to write Python code if Python is your main stack.

Pick by situation

• Lots of small questions all day: DeepSeek V4 Flash or ChatGPT 6 Luna, 1 credit each.
• Daily coding with tests and fixes: DeepSeek V4 Pro.
• Reviewing a large diff: Claude Sonnet 5 first, Claude Opus 5.5 if it misses something.
• A bug with logs, screenshots and a recording: Gemini 3.1 Pro, or ChatGPT-5.5 if you prefer its style.
• A huge file pasted as text: Kimi K3 or Grok 4.7.
• A cheap second opinion: GLM 5.2 next to DeepSeek.
• Autocomplete inside your editor: an IDE assistant, not a web chat.
• All of the above on one balance: Moleculs.