DeepSeek Raised Billions. I’m Still Asking It to Answer in the Right Language.

A powerful reasoning model. An unexpected language switch. And the most expensive three words in the conversation: “Please try again.”

DeepSeek can tackle difficult math, generate code, and produce impressive reasoning.

Sometimes, it also gives you an unexpected side quest:

To be clear: Chinese is not the problem. An assistant ignoring the requested language is the problem. The same criticism would apply if it unexpectedly switched to English, French, or Klingon.

And this is not just a hypothetical complaint. Users have reported DeepSeek answering in Chinese during English conversations, including cases where the visible reasoning, the final answer, or both change language. github

Apparently, “follow the conversation” is still on the roadmap.

The reports are not limited to one place.

These are user reports, not a controlled benchmark. They establish that people have encountered the behavior—not how frequently it happens across all users.

An intermittent failure is still a failure when your workflow depends on the output.

The R1 paper describes reinforcement-learning methods for developing reasoning capabilities, alongside results on mathematical and programming tasks. It also openly discusses language mixing as a limitation. nature

The paper describes mixed English–Chinese reasoning in R1-Zero and a language-consistency reward used during R1 training to reduce the problem. nature

In other words, this is not merely an outsider inventing a complaint. Language consistency is already a recognized engineering concern.

But recognizing a problem and reliably preventing it are different milestones.

And the R1 paper does not, by itself, explain every language-switching incident in later products.

“Users say the model keeps switching languages.”

“No. To remind the first model which language the conversation is in.”

Excellent. We have invented the bilingual supervisor agent.

Or—and stay with me here—we could treat language consistency as a product requirement rather than a prompting competition.

User: “Please answer in English.” Model: “Of course.” User: “Then why are you still using Chinese?”

The corrective loop is not: users have described repeatedly asking the system to return to English. reddit

When an answer arrives in the wrong language, the interaction can become:

Request → Unusable response → Language correction → Regenerated response → Check whether it worked Enter fullscreen mode Exit fullscreen mode We should not invent a dollar figure or an energy estimate for that loop. The reports do not provide the measurements needed.

More output. More correction. More attention spent checking whether the tool followed a basic instruction.

A thousand-token answer is not productive merely because it contains a thousand tokens. If the user must regenerate it to use it, output volume has become a misleading proxy for value.

Reuters reported that DeepSeek raised roughly $7.4 billion in June, followed by reporting on another large fundraising round in the autumn. reuters

Funding does not magically eliminate bugs. Nor should every product defect become an argument that research is worthless.

But enormous investment makes a reasonable question harder to dismiss:

Are we improving useful intelligence—or just financing more impressive output that users must supervise?

A model can be brilliant at solving the problem and unreliable at delivering the solution.

Evaluate whether the assistant completes the user’s task—not just whether it can produce a strong answer somewhere inside a recoverable interaction.

Language consistency belongs in that evaluation.

So do correction frequency, retry burden, and whether the final output is usable without another model cleaning it up.

DeepSeek has real strengths. That is precisely why the rough edges deserve serious criticism.

But before generating another thousand tokens, make sure the user can actually use the first hundred. created by: https://almodarresieh.github.io/