Kimi vs DeepSeek (and Qwen): Which Model to Pick for Which Task

Use Kimi K3 for long documents, large code dumps and reasoning over images. Start with DeepSeek V4 Pro for everyday coding and reasoning when cost matters. Use DeepSeek V4 Flash or Qwen 3.8 Flash for bulk drafts and high-volume requests. If a hard problem beats both Kimi and DeepSeek, try Qwen 3.8 Max and its extended thinking mode before you give up.

Below you'll find where each model stops being the right tool.

How we compared: reasoning, long context, input types, cost per run

We used four criteria:

• Reasoning. Is the model positioned as a reasoning model, or does it have a dedicated thinking mode?
• Long context. How much text can you send in one go? Kimi K3 is the only model here with a context figure we can stand behind: 1M tokens. For the others, check the vendor's documentation.
• Input types. What can you attach to a request on Moleculs: images, files, audio, video?
• Cost per run. Credits per request on Moleculs, as of November 2026. Prices change, so check the full model catalogue with per-run costs before you plan a big job.

We don't quote benchmark scores. They go out of date fast and rarely match your actual prompt. The four-step test at the end shows you how to get your own numbers.

Kimi vs DeepSeek vs Qwen at a glance: comparison table

Kimi K3 and K2.6 are the long-context models here, but on Moleculs no Kimi model takes file attachments. DeepSeek and Qwen Flash take files, audio and video. If your "long document" is a PDF, check how you'll get it into the model before you pick one.

Kimi K3: long documents and multimodal reasoning

Who it's for: people who need one model to hold a lot of material at once. That means a whole contract, a book chapter, months of meeting notes, or a big chunk of a codebase.

What it does well: Moonshot AI describes K3 as its flagship multimodal reasoning model, with a 1M-token context. You can paste in the full text and ask questions that span the whole thing: "list every deadline", "where does section 4 contradict the appendix", "which functions call this one". It also accepts up to 10 images per request, so you can add a scanned diagram or a screenshot alongside the text.

Where it's limited: at 15 credits per run, it's the priciest model in this comparison and costs more than seven times as much as DeepSeek V4 Pro. On Moleculs it takes text and images but not file attachments, so long documents go in as pasted text.

Skip it when: your prompt fits on one screen. Short tasks don't use the extra context, and you pay for it anyway.

To check its answers yourself, try Kimi online with a document you already know well.

Kimi K2 Thinking and Kimi K2.6: the lighter Kimi options

Kimi K2 Thinking (3 credits) is Moonshot AI's open reasoning model. Moonshot says it extends the K2 series into agentic, long-horizon tasks. Use it for problems that take many dependent steps: planning a migration, debugging something that only shows up three calls deep, or working through a proof. On Moleculs it's text only.

Kimi K2.6 (4 credits) is a long-context Kimi model that also accepts up to 10 images. Pick it when you need a lot of context plus a screenshot or two, and K3's price isn't worth it for the task.

Where they're limited: neither takes file attachments on Moleculs. Neither has a context figure in our catalogue, so if you plan to paste something huge, check the limit in Moonshot's docs first.

Skip them when: you need audio or video input. No Kimi model here accepts either.

For a wider look at the Kimi family, see our breakdown of what Kimi can actually do.

DeepSeek V4 Pro: everyday reasoning and code at a low per-run cost

Who it's for: anyone who writes code, debugs, or reasons through problems daily and wants a model they can call often.

What it does well: it's positioned as a reasoning-focused model. At 2 credits per run, you can send the same prompt several times, change the wording and compare, and still spend less than one Kimi K3 call. It also takes the widest range of inputs here: images, files, audio and video. You can attach a log file or a short screen recording of a bug directly.

Where it's limited: our catalogue doesn't list its context window. If you plan to feed it something book-length, check DeepSeek's documentation first. For very long single inputs, Kimi K3 is the safer choice.

Skip it when: the task depends on holding a huge amount of text at once, or a cheaper Flash model already gives you good enough answers.

You can run DeepSeek in the browser without setting anything up. If you're also weighing it against OpenAI's models, we cover how DeepSeek compares to ChatGPT for coding in a separate guide.

DeepSeek V4 Flash: fast drafts and high-volume requests

Who it's for: people running lots of small requests, like rewriting product descriptions, tagging support tickets, or producing first drafts you'll edit anyway.

What it does well: at 1 credit per run, it's among the cheapest text models in the catalogue. It accepts the same inputs as V4 Pro, so you can switch between the two without changing how you send material.

Where it's limited: on a problem that needs a long chain of reasoning, expect to check its work more closely.

Skip it when: a wrong answer costs more than the credits you save. In that case, step up to V4 Pro.

Qwen 3.8 Max: extended thinking mode for harder problems

Who it's for: people stuck on a problem where the first answer from other models looks plausible but turns out wrong. Tricky logic, multi-constraint scheduling and maths that needs real working fit here.

What it does well: it runs Alibaba Qwen's extended thinking mode, which spends more effort working through the problem before it answers. Treat it as a second opinion when Kimi and DeepSeek disagree, or both get it wrong.

Where it's limited: on Moleculs it takes text only, so screenshots and files have to be described or pasted. At 7 credits per run, it costs more than any DeepSeek model, though less than half as much as Kimi K3.

Skip it when: the task is routine. Extended thinking adds time and cost without changing the answer to an easy question.

Qwen 3.8 Flash: the cheapest way to handle mixed inputs

Who it's for: people who need to throw mixed material at a model: a photo of a receipt, an audio memo, a short clip, a spreadsheet.

What it does well: it costs 1 credit per run and accepts images, files, audio and video. If you're sorting or summarising a pile of mixed inputs, it's the cheapest option in this comparison.

Where it's limited: it's a light model, so use it to extract and sort material rather than to draw conclusions from it.

Skip it when: the input is plain text and the task is hard. DeepSeek V4 Pro costs one credit more and fits better.

Moleculs: running Kimi, DeepSeek and Qwen under one subscription

Who it's for: people who want to test several model families without opening a separate account with each vendor.

What it does: Moleculs is an aggregator. It gives you access to Kimi, DeepSeek, Qwen and other models through one interface. Each request costs credits from your plan's quota, the quota refreshes every paid period, and the cost per run shows next to each model. A guest can send one text request without signing up. After that you need an account. For plan details, see the pricing page.

Limits:

• We don't make any of these models. Moonshot AI, DeepSeek and Alibaba do. We provide access.
• A model here may not match the vendor's own app. Settings and the way the model is wrapped can differ.
• There's no side-by-side view. You compare models by running the same prompt in each one in turn.
• There's no public API and no mobile app. Everything runs through the web interface.

Skip it when: you've already settled on one model and need its vendor-specific features or API access. Then go to that vendor directly.

How to test them on your own task in four steps

1. Pick one real prompt. Use a real task from your work, not a toy example. For code, use a function from your project. For documents, use a file you already know the answers to.
2. Start with the cheap pair. Run it through DeepSeek V4 Pro and Qwen 3.8 Flash first. If either is good enough, you're done and you've spent 3 credits.
3. Escalate on purpose. Use Kimi K3 if the input is long, Kimi K2 Thinking or Qwen 3.8 Max if the reasoning is hard. Change only the model, never the prompt.
4. Count the corrections. Note how many fixes each answer needed. The model with the fewest corrections per credit wins.

Need a neutral starting point? Send this coding prompt. It asks for a function that merges overlapping time intervals, a complexity explanation and three edge cases. You can check all three yourself in about a minute.

Which one to pick by situation

• Only text, on a budget: DeepSeek V4 Pro.
• A contract, book or codebase in one go: Kimi K3.
• A long chain of dependent steps: Kimi K2 Thinking, with Qwen 3.8 Max as the second opinion.
• Hundreds of small requests: DeepSeek V4 Flash for text, Qwen 3.8 Flash if the inputs are mixed.
• Audio, video or file attachments: the DeepSeek models or Qwen 3.8 Flash. Kimi doesn't take them here.
• Lots of context plus a few screenshots: Kimi K2.6.
• Testing all of them without separate accounts: Moleculs, as long as you can live without vendor-specific settings and an API.