DeepSeek V4 Pro vs. Claude Sonnet 4.6 on 50 tasks: where it saves money and where it adds risk

Comparing DeepSeek V4 Pro and Claude Sonnet 4.6 on 50 typical tasks for a Russian developer shows something simple: a low price per token does not guarantee the best choice for production. On basic scenarios the models run nearly neck and neck, but on tasks with Russian specifics DeepSeek errs noticeably more often.

What was tested

The author compared the models not on academic benchmarks but on practical requests that local teams actually encounter: customer support, data extraction from documents, calculations under the Russian Labor Code and Tax Code, and decoding professional abbreviations. Testing ran through the standard web interfaces: Claude Sonnet 4.6 without adaptive thinking, and DeepSeek V4 in fast mode without deep thinking. There were 50 prompts in total, split into four blocks.

In April 2026 the price gap looked very aggressive in DeepSeek's favor: $1.74 per million input tokens and $3.48 for output, versus $3 and $15 for Sonnet 4.6. On a real workload that works out to roughly threefold savings, so the temptation to switch to the cheaper model is easy to understand.

• Classification of 20 support tickets into five categories
• Field extraction from 15 documents with OCR errors
• 10 reasoning tasks involving Russian law and calculations
• 5 tasks on local terminology such as EDO, UPD, OFD and KIZ

Where the models are even

On simple scenarios there was almost no difference. Both models classified support tickets without errors and handled typical questions about delivery, returns, payment and general inquiries equally well. Basic reasoning also ended in a tie: both systems correctly handled the statute of limitations, an advance refund and a case of dismissal during a probationary period, although they cited different legal provisions.

Ordinary document processing looked similar. Neither model confused an OGRNIP (sole proprietor registration number) with an INN (taxpayer ID). Both took the amount from the numeric line when the written-out amount contained an error, and both correctly extracted the date from an advance report. By the author's estimate, if 80% of a company's workload consists of such tasks, switching to DeepSeek can indeed cut the budget by about 75% without a noticeable drop in quality.

"English benchmarks won't help us choose a model for a

Russian task."

Where errors get expensive

Problems began where the job requires not general intelligence but knowledge of local context and care with edge cases. In a payroll test for an employee with a salary of 150,000 rubles, Sonnet returned the correct take-home pay of 130,500 rubles, while DeepSeek returned 110,550. The model effectively withheld 26.3% instead of the standard 13%, probably mixing up personal income tax (NDFL) with the employer's insurance contributions. In a demo that is just a miss; in an automated pipeline it is potentially hundreds of thousands of rubles in errors per month.

Another failure turned up in OCR normalization. Both models read the amount, INN and date on an invoice with mixed Russian and Latin characters correctly, but only Sonnet normalized the document number to its canonical form. DeepSeek left the letters O and l where digits belong. If that number is then matched against a 1C or ERP database by exact match, the document simply won't be found, even though the other fields already look right.

DeepSeek showed its most unpleasant type of error on a question about the education tax deduction for a 25-year-old son. The model opened its answer with "YES" and then itself explained why, under Article 219 of the Tax Code, the deduction does not apply after age 24. A human notices the contradiction immediately, but for a system that parses only the first word, the result is the wrong class. A similar problem surfaced in terminology: Sonnet correctly expanded KIZ as a control identification mark, while DeepSeek invented a variant about a "spare part identification code".

Overall, Sonnet scored 92% versus 88% on documents, 100% versus 60% on tasks with Russian legal specifics, and 100% versus 80% on local terms.

What this means

The practical conclusion: DeepSeek V4 Pro suits first-line support, templated replies, basic classification and MVPs, where price is critical and an error does not lead to a financial or legal action. But if the model takes part in money calculations, interpretation of the Tax Code and Labor Code, document normalization, or produces answers that a system parses directly, the premium for Claude Sonnet 4.6 looks like insurance against costlier consequences. Choose between them not by benchmarks but by 30–50 of your own real requests.