Both Gemini and DeepSeek Claim Improvements in Coding and Reasoning for Their AI Models

Google released some Gemini model updates:

New Gemini 2.5 Pro Model:

Google is focusing on continuous improvement, and Gemini 2.5 Pro is apparently the result of that.

The focus is on more robust reasoning and enhanced coding capabilities, which in theory should make it more well-rounded for complex tasks.

Top Performance:

The LMArena leaderboard is a significant benchmark for large language models. Gemini 2.5 Pro achieved the top spot which could mean it performs better than its competition in real life.

Improved Accuracy:

The concept of “thinking models” is what’s led to better performance. Instead of simply generating responses, these models are designed to reason through the problem-solving process. This leads to more accurate and reliable outputs.

This process is similar to how humans process thoughts.

Strong Reasoning and Coding:

The model’s claimed strong performance in complex tasks, as well as its performance on coding, math, and science benchmarks, could mean it’s pretty well-rounded.

This is important because it means that Gemini 2.5 Pro can be used in a wide range of applications, from software development to scientific research.

“Humanity’s Last Exam”:

This benchmark is designed to test a model’s ability to reason and solve complex problems. Gemini 2.5 Pro’s score of 18.8% apparently shows its advanced reasoning capabilities.

Coding Improvements:

There is also supposed to be significant improvement in coding performance, particularly on the SWE-Bench Verified benchmark. This could mean that Gemini 2.5 Pro is becoming increasingly effective at generating and understanding code.

This should mean something to you coders out there. Someone told me that the use of a custom agent setup implies that the model can use tools and interact with its enviroment to solve coding tasks. You would know better than me what that means.

Long Context Window:

A large context window allows the model to process and understand more information at once. This is crucial for tasks that require understanding long documents or complex conversations.

The expansion to a 2 million token window will improve its ability to handle complex and extensive information.

All in all, these updates show that the focus is now on improved reasoning, coding, and contextual understanding.

DeepSeek’s model upgrade

DeepSeek released a major upgrade to its V3 large language model. The new model is called DeepSeek-V3-0324.

Compared to its predecessor, this model shows improvements in reasoning and coding. Just like Gemini, benchmarks, Hugging Face in this instance, indicate higher performance in multiple technical areas.

So, we shan’t be getting into the thick of things but rather we shall just both Gemini and DeepSeek are claiming improvements to reasoning and coding.

We shall see what performs better in real life.