Testing DeepSeek After GLM 5.2: A Model Comparison for Long-Form WordPress Translation

After completing the custom integration of SlyTranslate with Zhipu GLM 5.2, I originally thought that the current automatic translation quality had reached a fairly practical level.

GLM 5.2 can handle not only article titles, summaries, and body text, but after plugin customization, it can also translate an entire Gutenberg article at once. Combined with processing such as code block placeholders, HTML structure protection, and translation restoration, it basically meets my WordPress English site publishing workflow.

However, before deciding to use GLM 5.2 long-term, I still wanted to test another representative domestic large model: DeepSeek.

The goal of this test was not to immediately replace GLM 5.2, but to confirm:

• Whether the DeepSeek API can be integrated normally;
• How large the difference in basic translation quality is between DeepSeek and GLM 5.2;
• Whether it is worth continuing to develop a full-article translation feature for DeepSeek;
• If the room for improvement in domestic models is already limited, whether the next step should be to test foreign models like OpenAI.

1. Why Test DeepSeek

Among domestic large models, the main candidates I have actually engaged with and considered so far are Zhipu and DeepSeek.

GLM 5.2 is already running normally in my current WordPress translation workflow, but testing only one model makes it difficult to judge whether the current results are already good enough or if there is still obvious room for replacement.

DeepSeek has several reasons worth testing:

1. The API integration method is relatively standard;
2. Domestic servers can usually access it directly;
3. Model response speed and invocation cost may have advantages;
4. If the translation quality is better, the plugin can continue to be customized following the GLM 5.2 approach.

Therefore, I decided to apply for the DeepSeek API and conduct a basic comparison with GLM 5.2 using the exact same test content.

2. Applying for the DeepSeek API

First, I went to the DeepSeek open platform, registered, and logged into an account.

Then, on the API Keys page, I created a new key with the name set to:

SlyTranslate-DeepSeek-Test

SlyTranslate-DeepSeek-Test

The API Key is only displayed in full once after creation, so it needs to be saved securely at the time of creation.

It is important to note that the full API Key should not be sent to chat logs, screenshots, or logs. Subsequent test scripts should also read the key via hidden input or environment variables to avoid writing it directly into the code.

3. Topping Up the Test Balance

The DeepSeek API is billed based on Token usage. If a newly created account has no available balance, API calls may fail due to insufficient funds.

Since I was only conducting a few tests on titles, summaries, and body text this time, I topped up 10 RMB and did not invest more money.

For this kind of model validation test, making a small top-up first is more reasonable. Only after confirming that the model quality and actual usage method meet the requirements is it necessary to continue adding balance.

4. Integrating DeepSeek into SlyTranslate

SlyTranslate has already integrated the Zhipu API via Any LLM Provider, so there is no need to overwrite the original GLM configuration; a new independent Provider can be added.

In the WordPress backend, navigate to:

SlyTranslate → Settings → AI Providers → Any LLM

SlyTranslate → Settings → AI Providers → Any LLM

Click:

Add provider

Add provider

Then select the DeepSeek preset.

The automatically filled API address is:

https://api.deepseek.com/v1

https://api.deepseek.com/v1

After entering the DeepSeek API Key, the connection test succeeded, and SlyTranslate could recognize the DeepSeek model.

This indicates that the DeepSeek API can now be connected normally from the current WordPress server.

5. Why a Fair Comparison Cannot Be Done Directly in the WordPress Backend

Initially, I considered directly selecting GLM 5.2 and DeepSeek respectively in SlyTranslate and then translating the same article.

But after actual operation, I found that this comparison was not fair.

The current execution method is:

• Selecting GLM 5.2 triggers the already customized full-article one-time translation feature;
• Selecting DeepSeek only uses SlyTranslate’s default chunked translation method.

The results produced this way reflect not only differences between the models but also differences in the translation workflows:

• GLM 5.2 gets the full article context;
• DeepSeek only sees a portion of the content at a time;
• Chunking makes terminology inconsistencies more likely;
• References and transitions between paragraphs may be lost;
• The overall tone across the title, summary, and body text may also be inconsistent.

Therefore, directly comparing the final articles generated in the WordPress backend makes it impossible to determine whether the differences come from the models themselves or from the implementation methods of full-article translation versus chunked translation.

If DeepSeek’s basic translation quality is indeed better, a full-article translation feature can similarly be customized for it later. The current inability to do full-article translation is not a limitation of the model’s own capabilities, but rather because the existing plugin has not yet implemented the same processing workflow for it.

6. Conducting a Basic A/B Test Using a Local Script

To ensure both models received the exact same content, I added DeepSeek support based on my previous GLM test script.

The test script calls:

glm-5.2

glm-5.2

and:

deepseek-v4-pro

deepseek-v4-pro

Both use the same:

• Chinese source text;
• Translation prompts;
• Title, summary, and body text test fields;
• HTML and Gutenberg structure protection requirements;
• Output length settings;
• Rules for preserving code, domains, and product names.

The run command is as follows:

cd /home/wangqiang/下载/glm-deepseek-translation-ab-test-2026-07-15

python3 glm_deepseek_translation_ab_test.py

cd /home/wangqiang/下载/glm-deepseek-translation-ab-test-2026-07-15

python3 glm_deepseek_translation_ab_test.py

When the script runs, it will prompt for the input of:

1. Zhipu API Key;
2. DeepSeek API Key.

The input will not be displayed in the terminal, nor will it be written to the test results.

7. API Call Results

In this test, all requests for both models successfully returned HTTP 200.

The actual time consumed is as follows:

The output character counts are as follows:

In terms of speed, DeepSeek was faster than GLM 5.2 in all three test items, especially for the summary and body text, where the difference in response time was quite noticeable.

However, for article translation, response speed is not the only criterion. As long as the full-article translation can be completed within an acceptable time, accuracy, context integrity, and format stability are usually more important.

8. Translation Quality Comparison

1. Title

GLM 5.2’s title information is more complete, preserving WordPress, Polylang, English subdomain migration, the complete fix, and the specific scope of the issue.

DeepSeek’s title is more concise but omits some of the original information.

For general content titles, conciseness might be an advantage; but for a technical blog, including the specific platform, plugins, and issue scope in the title helps readers quickly determine whether the article is relevant to their own problems.

Therefore, in this round of title translation, GLM 5.2 is better in terms of accuracy and information completeness.

2. Summary

DeepSeek’s summary sentences are shorter, making the overall reading rhythm more relaxed.

GLM 5.2’s summary is slightly longer, but it preserves the fault causes, cache status, and handling process from the original text more completely.

That is to say:

• DeepSeek leans more toward reorganizing and compressing content;
• GLM 5.2 leans more toward faithfully preserving the original information.

For a technical blog summary, I value factual accuracy more, so GLM 5.2 still has a slight advantage.

3. Body Text

Both models were able to preserve the basic HTML and Gutenberg structures, and neither mistranslated domains, commands, or plugin names.

However, some paragraph transition issues appeared in DeepSeek’s body text.

For example, an independent Gutenberg paragraph starts with a lowercase which:

<p>which ultimately returned a 404.</p>

<p>which ultimately returned a 404.</p>

This sentence depends on the previous paragraph to make sense. Since Gutenberg saves it as an independent paragraph, if a screenshot, code block, or quote is inserted in between, this sentence will appear incomplete.

GLM 5.2’s corresponding handling is:

<p>This ultimately returned a 404.</p>

<p>This ultimately returned a 404.</p>

Even if other content is inserted between article blocks, this sentence can still stand independently.

In addition, DeepSeek also had a few literal translations and unclear references. GLM 5.2’s body text tone is relatively more stable and better suited for technical troubleshooting articles.

9. Why I Did Not Continue with a Full Long-Text Test

Theoretically, a more complete testing method would be to have GLM 5.2 and DeepSeek perform a one-time translation of the same complete Gutenberg long article.

However, the current custom plugin has already completed a lot of processing centered around the WordPress content structure, including:

• Code block parsing;
• Code content placeholders;
• Gutenberg block structure protection;
• HTML tag protection;
• Separate processing for titles, summaries, and body text;
• Handling of overly long content and output length control;
• Placeholder restoration;
• Output integrity checks;
• API exception and timeout handling.

To achieve a completely fair long-text comparison locally, I would need to port these processing logics into the test script.

This would no longer be simply calling two APIs, but rather locally replicating the core workflow of a WordPress translation plugin.

If DeepSeek had already shown a clear quality advantage in the short-text tests, it would still be worth investing time to continue with a full long-text test.

But judging from the current results, DeepSeek’s main advantage is faster speed; its translation quality did not obviously and consistently surpass GLM 5.2.

To verify an uncertain outcome, investing a large amount of time to port the entire plugin functionality is not cost-effective.

Therefore, stopping this round of testing here is a choice that better aligns with actual investment and return.

10. Current Conclusions

Several fairly clear conclusions can be drawn from this test.

DeepSeek’s Advantages

• The API can be integrated normally;
• There are no obvious obstacles for domestic server access;
• Response speed is faster than GLM 5.2;
• Basic Gutenberg and HTML structures can be preserved normally;
• It can be kept as a backup model.

GLM 5.2’s Advantages

• Title information is more complete;
• The summary preserves the original facts more fully;
• Body text paragraph transitions are more stable;
• The tone of technical articles is more natural;
• Plugin customization for full-article translation has already been completed;
• Code block placeholder and structure restoration mechanisms have already been integrated.

Overall, DeepSeek V4 Pro did not demonstrate a translation quality advantage in this round of testing sufficient to replace GLM 5.2.

Therefore, continuing to use GLM 5.2 in the current production environment better aligns with actual investment and return.

The DeepSeek Provider and remaining API balance can be retained. Later, if DeepSeek releases a clearly stronger new model, or if SlyTranslate natively adds a more comprehensive full-article protection feature, it will not be too late to retest then.

11. Next Round May Test OpenAI

After testing GLM 5.2 and DeepSeek, my current feeling is:

Representative domestic models have already reached a fairly practical translation level, but if I want to achieve a more obvious quality improvement, the next step may require considering foreign models.

The most worthwhile one to test among them is still OpenAI.

However, there is a practical issue here: my WordPress server is located on Alibaba Cloud in Hangzhou, and the server currently cannot directly access some foreign AI APIs.

When testing Gemini previously, the Hangzhou server experienced connection timeouts when accessing the Google API. The OpenAI API may also face similar network issues.

Therefore, the more reasonable next step is not to immediately modify the WordPress plugin, but to first conduct a test with the same content using OpenAI and GLM 5.2 on a local computer.

The testing process could be:

1. Call the OpenAI API locally;
2. Use the same title, summary, and body text as with GLM 5.2;
3. Compare translation accuracy, naturalness, and technical expression;
4. Confirm whether OpenAI has a clear quality advantage;
5. Only if the quality is indeed higher, then research access solutions for the Hangzhou Alibaba Cloud server.

If the final difference is only slightly better, there is no need to change the solution for network access, proxy stability, and server maintenance complexity.

Only when OpenAI’s translation quality is clearly leading is it of practical value to solve the server connection issue.

12. Summary

This DeepSeek test did not bring about a conclusion to “immediately replace GLM 5.2,” but the test itself was still valuable.

It at least proved that:

• The DeepSeek API can be integrated normally;
• DeepSeek’s response speed is indeed faster;
• Both domestic models already possess good basic translation capabilities;
• What ultimately determines the translation effect right now is not just the model itself, but also the plugin’s structure protection, context processing, and content restoration mechanisms;
• Without a clear quality improvement, it is not worth repeating the development of an entire set of custom features for another model.

At this stage, I will continue to keep GLM 5.2 as SlyTranslate’s production translation model.

DeepSeek will remain as a backup Provider.

The next step, if I continue to pursue translation quality improvements, will be to prioritize testing OpenAI locally. Only after confirming that its translation quality is indeed significantly higher than GLM 5.2 will I further research how to resolve the issue of accessing the OpenAI API from the Alibaba Cloud Hangzhou server.