This page compares two open-weight models on one job: turning documents into structured fields. It looks at reading scans, field accuracy on clean text, how each handles a value that is absent or ambiguous, schema control, cost per document and self-hosting.
Jul 29, 2026 · 12 min read
Kimi K3 is the safer model-level default for extraction quality: it accepts images natively, constrains output to a JSON schema directly, and has a materially better public signal for refusing to invent an unknown value. DeepSeek V4 Pro is the economic default, dramatically cheaper per document, faster through its first-party API and easier to license and self-host.
The comparison is cleanest for text that is already digital or already through OCR, where both models see the same input. For page images it is inherently uneven, because one model is a vision-language model and the other takes text only1, 6. The OCR or parsing stage that closes that gap belongs to your pipeline, not to either model, which is why it does not decide the verdict here.
For a staged pipeline, run DeepSeek V4 Pro in its non-thinking mode for routine high-volume extraction with strict validation, then escalate automatically: illegible pages, conflicting values, failed schemas and any important absent field go to Kimi K3, followed by human review wherever an error is expensive.
You are building the pipeline, so the answer is a route rather than a model. Cheap pass first, strict validation, then escalate the illegible and the ambiguous to the model that reads pages.
Thousands of clean, routine documents a week and a person checking exceptions. At roughly an eighth of the cost per document, the savings pay for a much better validator.
Scans, stamps, handwriting and tables where position carries meaning. Only one of these models can see any of that, so the choice is made before cost enters the discussion.
A confidently invented value written to a database is the failure that matters. Require a status and an evidence span on every field, and never let one empty value stand for five different failures.
This page compares the two models through their API in one neutral setup, and it treats document conversion as part of the harness rather than as one model's feature.
The parts that matter are whether the model can read the page, field-level accuracy on clean text, how it treats a value that is absent or ambiguous, whether the output holds a schema, the cost per document and what running it yourself takes. Licence terms matter here in a way they do not for hosted-only models.
One asymmetry is real and worth stating plainly rather than hiding: Kimi K3 reads images and DeepSeek V4 Pro does not. That is a model capability, not an app feature. The OCR step you would add in front of DeepSeek is a harness choice, so when you test the two on text, give both the exact same converted text.
The model facts that actually affect an extraction pipeline. Upload interfaces and parsers are left out, since they belong to the pipeline rather than the model.
Figures from Moonshot AI and DeepSeek documentation, checked July 2026. The cost examples on this page assume 10,000 uncached input tokens and 500 billed output tokens per document and exclude retries, OCR and infrastructure.
The answer changes with what your documents look like when they arrive. Read the evidence column closely: no public benchmark scores field-level extraction for this exact pair, so several rows are proxies.
Better-choice calls map to what the sources actually evaluated. The vendor-reported document results were assembled by Moonshot, and the abstention figures come from a knowledge benchmark rather than an extraction test.
Extraction is the one task where scoring can be fully mechanical, so make it mechanical. Then judge what your team actually pays for: field accuracy, correct nulls, evidence that points at the right place, and the cost per document that passes validation rather than per call.
Invoices, renewal notices, contracts and tables, and include the hard cases deliberately: absent fields, crossed-out values, conflicting dates and low-quality scans. A clean sample tells you nothing about the failures that cost money.
One shared instruction, the same schema and identical source material. If you are testing text extraction, feed both the exact same OCR text so the parser is not silently part of the result.
API and self-hosted results can differ for open-weight models, because inference engines and decoding settings change behaviour. Moonshot built a vendor verifier precisely because it found quality differences between third-party deployments.
Exact match or normalised F1 per field, precision and recall on absent, accuracy of ambiguous escalations, evidence-span correctness, schema validity, manual corrections per accepted document, and cost and latency per document that passes validation.
This is the gap to know about before you trust anyone's ranking: the public benchmarks measure adjacent things. Here is what each source helps judge.
Prices, serving recipes and model revisions move quickly for open-weight models, and both of these launched in July 2026. Re-check the numbers before committing a pipeline to either one.
The best prompt is not the same for both, and one rule holds for both: never let an empty string stand for every kind of failure.
Kimi K3 does best with the page image supplied directly where layout matters, a low reasoning setting for routine work, and a status plus evidence required on every field. Its output price and its always-on reasoning both argue for short answers, so cap the completion length and ask for values rather than commentary2, 3.
DeepSeek V4 Pro does best with clean OCR text carrying page markers, its non-thinking mode for the first pass, and a strict function schema rather than unconstrained JSON, so types and required keys are validated as the output is generated8. Rerun the failures and the ambiguous cases at a higher effort rather than raising the effort for everything.
For both models, distinguish absent, illegible, ambiguous, invalid and unsupported instead of collapsing them into one empty value. Downstream systems need to apply different policies to a field that is genuinely blank and a field nobody could read.
A Kimi K3 prompt: status and evidence on every field
Extract the invoice fields using the supplied JSON Schema.
For each field return:
status: present | absent | ambiguous
value
page
evidence: an exact string from the document
Use null when the status is absent or ambiguous.
Never infer a value from another field.A DeepSeek V4 Pro prompt: one strict function call
Extract only facts explicitly present in the OCR text.
Call the extraction function exactly once.
If evidence is missing, return absent and null.
If two plausible values conflict, return ambiguous with
both evidence spans and no selected value.
Do not normalise or reformat values beyond the schema.The failure modes here are different in kind, which is what makes the two models complementary. The useful question is what to change in the prompt or the pipeline.
One question first. Does the model itself need to read page images? Everything else follows from that answer and from what an error costs you.
A starting point, not a rule. Score your own fields before you commit a pipeline.
If the model itself has to read scans, handwriting, stamps or visually aligned tables, choose Kimi K3. That is not a preference, it is the only one of the two that can do it without a converter in front1, 6.
If reliable OCR or structured text already exists, ask how expensive an error is. When errors are costly and fields are often missing, choose Kimi K3 with evidence validation and human escalation. When fields are routine, documents are clean and volume is high, choose DeepSeek V4 Pro in non-thinking mode with strict validators8, 10.
If the governing constraint is the token budget or a permissive self-hosting licence, choose DeepSeek V4 Pro7, 9. If documents approach the context limit, test both, because the published capacities match while practical self-hosted limits and recall do not. And whichever you pick, keep a fallback route for the pages the first model cannot handle rather than accepting whatever it returns.
One case sits outside everything above: if the actual goal is to run either model on your own GPUs behind your own infrastructure, a hosted chat workspace is not the tool for that job. Playgram gives your team a fast way to try both models and run the eventual pipeline's prompts, but it does not stand up a private deployment for you.
Kimi K3 is the better extraction model and DeepSeek V4 Pro is the better extraction engine for cheap, clean, repetitive text. Kimi's native vision, stronger general benchmark result and better if still imperfect behaviour on unknown answers make it the safer choice for messy documents. DeepSeek's API economics, throughput, MIT licence and smaller serving footprint make it compelling when OCR and validation already exist.
The evidence gap is specific and worth remembering: no public benchmark reports field-level precision, recall and null accuracy for these exact models under identical extraction prompts15. The vendor document benchmarks use different harnesses, the hallucination figures come from a knowledge test rather than an extraction test, and one of them was measured on a different configuration of the DeepSeek model10. Prices, serving recipes and revisions all move quickly.
The safest final step is to test the shape of your own documents, not a generic prompt from the internet. A fair test needs the same setup for both models: the same source material, the same prompt 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, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first field comes back. The cleaner the setup, the more the difference you see is really Kimi K3 vs DeepSeek V4 Pro, and not just which one happened to be easier to reach that day.
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