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:

Learn Chinese to read the answer.

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 Bug That Comes With Free Language Lessons

The reports are not limited to one place.

• A GitHub issue describes Chinese responses in conversations conducted entirely in English. github
• An App Store review complains about repeatedly having to force the assistant back into English. apps.apple
• A French-language Trustpilot reviewer describes asking a question in French and receiving an answer in Chinese. fr.trustpilot

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

That distinction matters.

But so does this one:

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

Before the Pitchforks: DeepSeek Deserves Credit

DeepSeek’s research is substantive.

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

That transparency deserves recognition.

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.

Let’s Brainstorm Some Terrible Solutions

Imagine the product meeting.

“Users say the model keeps switching languages.”

“Have we tried adding another model?”

“To fix the answer?”

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

Excellent. We have invented the bilingual supervisor agent.

Other proposals:

• Rename “Regenerate” to “Same Answer, Correct Language.”
• Add an achievement: “You reminded the assistant five times!”
• Introduce Language Persistence Pro.
• Make the user write a system prompt longer than the original question.

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

用户：请用英语回答。

模型：当然可以。

用户：那你为什么还在用中文？

User: “Please answer in English.”

Model: “Of course.”

User: “Then why are you still using Chinese?”

That exchange is satire.

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

The Token Bill Nobody Wants

Here is the actual criticism.

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

Request
→ Unusable response
→ Language correction
→ Regenerated response
→ Check whether it worked

We should not invent a dollar figure or an energy estimate for that loop. The reports do not provide the measurements needed.

But the extra work is visible.

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

That is not additional intelligence.

That is rework.

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.

Billions Raise the Bar, Not the Accuracy

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.

Developers need both.

Ship the Answer, Not the Recovery Procedure

The lesson is bigger than DeepSeek.

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.

加油，DeepSeek。

Keep going, DeepSeek.

But before generating another thousand tokens, make sure the user can actually use the first hundred.
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