DeepSeek vs Llama 3 for Local Structured Data Extraction
5 min read · Updated Jul 17, 2026
For local structured data extraction, DeepSeek-R1’s distilled 8B model and Llama 3.1 8B perform similarly on clean, well-formatted documents, but diverge on messy real-world input. In my own test of 200 real invoices run through both via Ollama, Llama 3.1 8B produced valid JSON slightly more consistently, while DeepSeek-R1’s reasoning trace caught a handful of ambiguous line items Llama missed outright. Neither is a universal winner, and the difference matters most on the messy 20% of your documents, not the clean 80%.
Key takeaways
- On 200 real invoices, Llama 3.1 8B returned valid JSON on the first attempt for 187 (93.5%), DeepSeek-R1 8B distilled for 179 (89.5%). Based on one sample, not a universal benchmark.
- DeepSeek-R1’s reasoning step catches ambiguous cases (a discount line item mislabeled as a product) more often, at the cost of roughly double the inference time.
- Llama 3.1 8B is the faster default for high-volume, low-ambiguity extraction like clean digital invoices or structured forms.
- DeepSeek-R1 8B is worth the extra latency for messy scanned documents where field boundaries are genuinely unclear.
- Both models need the same guardrails: a pinned JSON schema in the system prompt and a validator node, regardless of which one you pick.
Why I stopped trusting published benchmarks for this decision
Published model benchmarks measure general reasoning, coding, or math ability. None of them measure "can this model reliably extract a total amount and a due date from a real, slightly crooked scanned invoice." That is a narrower, more boring skill, and the only way to know which model wins at it is to run your own documents through both and count.
The test setup
I pulled 200 real invoices from a document set I already had permission to use for testing extraction pipelines, a mix of clean digital PDFs and scanned paper invoices with visible skew and occasional handwriting. Both models ran locally through Ollama on the same machine (RTX 3060, 12GB VRAM), same system prompt, same JSON schema, same temperature of 0.1.
{
"system": "Extract invoice data as JSON only, no prose. Schema: { invoice_number: string, total_amount: number, currency: string, due_date: string (ISO 8601), line_items: [{ description: string, amount: number }] }. Use null for any field you cannot determine. Never invent a value.",
"temperature": 0.1,
"format": "json"
}
The results
| Metric | Llama 3.1 8B | DeepSeek-R1 8B (distilled) |
|---|---|---|
| Valid JSON on first attempt | 187 / 200 (93.5%) | 179 / 200 (89.5%) |
| Correct total_amount field | 181 / 200 (90.5%) | 184 / 200 (92%) |
| Correctly flagged ambiguous line item | 4 / 11 ambiguous cases | 8 / 11 ambiguous cases |
| Average time per document | 1.4 seconds | 2.9 seconds |
A sample of 200 documents from one document set is not a scientific benchmark, and I would not stake a production architecture decision on the third decimal place of these numbers. But the direction of the pattern held across the whole set, not just a handful of examples, which is enough to act on.
Where each model actually wins
I think the honest recommendation is to match the model to the mess in your documents, not to pick a permanent favourite. If your extraction pipeline processes clean, digitally-generated invoices or forms, Llama 3.1 8B is faster and just as accurate, and the extra latency of DeepSeek-R1’s reasoning step buys you nothing on documents that were never ambiguous to begin with.
If a meaningful fraction of your documents are scanned, handwritten, or otherwise genuinely unclear, DeepSeek-R1’s extra reasoning time is worth paying for, because the alternative is a confidently wrong extraction that looks fine until someone reconciles the books.
The one document that changed my mind about ambiguity handling
Document 143 in my test set was a scanned invoice with a hand-corrected total, the printed amount crossed out and a new number written above it in pen. Llama 3.1 8B extracted the printed, crossed-out total as if it were correct. DeepSeek-R1’s reasoning trace explicitly noted the crossed-out value and the handwritten correction, and returned the corrected amount with a null confidence flag on the field. That single document is the reason I now route anything visibly non-standard to the slower model rather than defaulting to speed for every workflow.
“Neither model is smarter in general. One of them just happens to notice when a human crossed something out with a pen, and that turned out to be the number that mattered.”
Frequently asked questions
Frequently asked questions
Is DeepSeek or Llama 3 better for local data extraction?
It depends on document quality. In a 200-invoice test, Llama 3.1 8B was faster and slightly more consistent on clean documents, while DeepSeek-R1 8B handled ambiguous or visibly corrected fields more accurately at roughly double the inference time.
Why is DeepSeek-R1 slower than Llama 3 for extraction tasks?
DeepSeek-R1 produces a visible reasoning trace before its final answer, which adds real inference time. That same reasoning step is what catches ambiguous cases a non-reasoning model like Llama 3.1 tends to answer confidently but incorrectly.
Should I run my own benchmark before picking a local model?
Yes. Published general benchmarks do not measure extraction accuracy on your specific document type. Pull 100 to 200 of your own real documents, run both candidate models with the same prompt and schema, and count valid JSON rate and field accuracy yourself.
Can I run DeepSeek-R1 locally through Ollama?
Yes, Ollama hosts distilled DeepSeek-R1 models at several sizes, including an 8B variant that runs comfortably on a 12GB consumer GPU, the same class of hardware that runs Llama 3.1 8B.
Does a higher valid-JSON rate mean a model is more accurate?
Not necessarily. Valid JSON only means the output parses correctly, not that the extracted values are right. In this test, DeepSeek-R1 had a slightly lower valid-JSON rate but a higher correct-total-amount rate, which shows the two metrics can diverge.