Background
Recently, I have been using GPT-5.5 to review computer science knowledge, and its capability has genuinely stunned me. The earlier GPT-5 series models felt somewhat lacking in a human touch, but 5.5 has clearly changed that impression. I believe many people feel the same way: lately, everyone has started paying attention to GPT again. Image 2 is far ahead of other text-to-image models, and GPT-5.5 also feels like a model worthy of the LLM crown.
I still remember the timing: GPT-5.5 arrived in the early hours of April 24, 2026, Beijing time, while DeepSeek V4 was released around noon that same day. It was another major release from the DeepSeek team after half a year of quiet work.
In DeepSeek’s launch article, most of the models used for comparison were previous-generation models from overseas AI companies. Without question, DeepSeek V4 cannot beat GPT-5.5, but its value and contribution should not be overshadowed by GPT-5.5’s brilliance.
DeepSeek Capabilities I Am Optimistic About
1. 1M context, with strong retrieval ability
On Context Arena, DeepSeek V4 Pro ranks first among Chinese open source models in retrieval ability under the 128K context stress test.
Why does this matter? When you assign a task to a model and let it execute through tools such as OpenCode, the longer the task runs and the longer the context becomes, the easier it is for the model to forget earlier information. In the end, the result is more likely to drift away from what the user expected.
2. The largest parameter scale among Chinese, and even global, open source models
In recent years, constrained by factors such as compute, many Chinese teams, including Alibaba’s Qwen team, have been researching smaller models and pushing their performance to the limit. But the effective path toward AGI and continued capability improvement is still to make models larger while also making them more efficient. This time, DeepSeek has raised the total parameter count of V4 Pro directly to 1.6T, more than twice that of the R1 model. This helps ensure the model has more abundant world knowledge.
3. …
There are many other highlights I have not yet discovered. If readers have new observations, feel free to add them in the comments.
My Personal Experience Using DeepSeek V4 Pro
Yesterday, I subscribed to Kimi’s lowest-tier membership and used it together with the official Kimi CLI for data preprocessing.
The preprocessing results still lagged behind Claude Code with the Opus model and Codex with GPT-5.5. Also, Kimi K2.6 only has a 256K context window. Even with fairly good prompts, it still failed to remove some obvious noise.
So today, I topped up 50 yuan for the DeepSeek API and paired it with OpenCode to clean up the remaining work from Kimi. The initial result was not satisfying, so I paused the execution in OpenCode and instructed it to read one article completely, then preprocess that article before moving on. In the end, with OpenCode’s help, DeepSeek V4 Pro completed the cleanup task quite well.
After that, I gave it more data preprocessing tasks, and the results were also fairly satisfying.
Conclusion
DeepSeek V4 Pro’s experience on the web or desktop client is not as smooth as Doubao’s, and its feature set is not as complete. But in API-based workflows, it performs tasks quite well.
With the May Day holiday approaching, DeepSeek API pricing has been heavily discounted, making it very cost-effective.
DeepSeek is currently on the Pareto frontier: strong model capability at a low price. If your budget is limited but you still want to preserve model quality, it is a good option.
Although its performance is not as strong as the latest models such as GPT-5.5, its strengths are openness, low cost, and the acceleration of AI democratization. Models such as Gemini have far more parameters than DeepSeek, so it is not surprising that DeepSeek cannot currently beat the very top models. Even so, its contribution deserves recognition.
The DeepSeek team is quiet and restrained: unmoved by praise, unafraid of criticism, following its own path with composure and discipline, and holding to long-termism. This attitude is much better than OpenAI’s Sam Altman-style hype or Anthropic keeping Mythos under wraps while stirring up attention.
When I was in the second year of graduate school, from the second half of 2024 to 2025, before R1 came out, I already used DeepSeek for data processing. It was cheap, had no concurrency limits, and offered the best value for money.
I am optimistic about DeepSeek. Every stir from the little blue whale pushes open source AI further forward. DeepSeek stands on the right side of history, and I look forward to more surprises from it in the future.
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