Kimi K3 vs DeepSeek V4 Pro

How we compared them
One task at a time

We are not claiming one of these two is the better model. What separates them is what arrives on the input, so the answer changes with the document in front of you.

One job has a full write-up behind it, and seven further articles put one of these two models against a different one. The card below opens that job, and the index further down lists all seven, so nothing here has to be taken on trust. No public benchmark compares these two on field-level accuracy at all, and the page says so rather than filling the gap with a verdict.

What we compared is set out underneath. First the published prices, licences and limits both models bring to any job, then the model-level dimensions where public evidence does separate them. Those hold whatever you are doing. Which of the two to reach for does not, which is why the job comes first.

One thing to be clear about before the tables. This page compares the two models through their APIs in one neutral setup, so an OCR tool or a document pipeline built around either one is not part of anything here.

By the job
Which model wins which work

Neither model wins in general, so this pair is settled one job at a time. The card names the job we tested, says which model took it and why, and opens the full test behind that answer. One job on this pair has that test so far, and the index further down carries the rest of the library.

The shared facts
What each one costs and takes

The published figures both models bring to any job. DeepSeek's rates carry a peak and an off-peak tier, and off-peak is half of peak.

Head to head
How they compare beyond one task

The general layer, underneath the jobs above. These are model-level dimensions, so they hold whatever the job is. No public benchmark compares these two on field-level accuracy, so that row is absent rather than guessed.

Everything we tested
Both models across the library

Every article on this site that puts one of these two models under a graded test, grouped by model. The one on this exact pair is the card higher up the page.

What this cannot tell you
The limits of the comparison above

The strongest thing on this pair is the price gap, and the price gap is the thing that just moved.

DeepSeek's rates on this page replaced figures that turned out to be a lapsed promotion, which is a fair warning about how fast this changes. No public benchmark compares these two on extracting fields from a document, so the accuracy rows above are proxies. The OCR figure is vendor-assembled and measured through an agent harness. And both models invent answers often enough that a schema with required absent and ambiguous states is not optional.

Playgram is not the right buy for everyone either. If you are running one open-weight model on your own hardware and nothing else, self-hosting is the cheaper answer and a workspace adds little.

The safest last step is to test the shape of your own documents rather than a generic prompt from the internet. A fair test needs the same setup on both sides: the same files, the same schema and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, because most teams end up running one model in one place and the other somewhere else, which tilts the comparison before the first field comes back. The cleaner the setup, the more the difference you see is really Kimi K3 against DeepSeek V4 Pro.

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