DeepSeek v4 Pro shows size is not the same as value
Pro is described as a very large open model with 1.6 trillion parameters, but it is not clearly the best open model in many comparisons. GLM 5.1 has 750 billion parameters, Kimi K2.6 has 1 trillion, and MiniMax M3 is around 450 billion, yet some users see them as stronger in benchmarks or daily use. MiMo v2.5 Pro is also said to rank higher in some tests while being offered at a similar cloud price.
The main counterpoint is that Pro is still a preview, so its current results may not reflect the final release. Several comments argue that parameter count is now a weak way to judge models because actual hardware size, precision, cache use, and matter more. For agent builders, the possible advantage is not top benchmark rank, but cheap and low cache cost.
The caveat is : some users report good results, while others see confident mistakes, so it needs hands-on testing before becoming a for serious agent work.
Key points
- Pro is extremely large at 1.6 trillion parameters, but size has not translated into clear top .
- Smaller or similarly priced models such as GLM, Kimi, MiniMax, and MiMo are being compared favorably against it.
- Many commenters say the model is still a preview and may improve later.
- For agent work, API price, cache cost, and may matter more than benchmark rank.
- It may be worth testing for low-cost workflows, but not trusting blindly for critical automation yet.