OpenAI Gpt OSS 120b vs OpenAI o4 mini high | Who will Win?
TLDRThe video compares OpenAI's GPT-OSS 120B model with the O4 Mini High model, focusing on their performance in reasoning tasks and coding challenges. GPT-OSS 120B is an open-source model with 117 billion parameters, capable of running on an 80GB GPU. While it performs close to O4 Mini High in reasoning benchmarks, it falls short in complex coding tasks. The open-source model is significantly cheaper and faster in inference but struggles with accuracy and complexity. The video concludes that while GPT-OSS 120B is a good open-source option, it doesn't match the capabilities of O4 Mini High.
Takeaways
- 🤖 OpenAI has released an open-source model named GPT-OSS, with versions like GPT-OSS 120B and GPT-OSS 20V, which are claimed to perform well in reasoning tasks.
- 📈 GPT-OSS 120B can run on a single 80GB GPU like Nvidia H100 and has 36 layers with 117 billion parameters, using 5.1 billion parameters for next-token prediction.
- 🌐 The models are primarily trained on English text data focused on STEM coding and general knowledge, and are not multimodal.
- 📊 In benchmark tests, GPT-OSS 120B performs close to OpenAI O4 Mini in reasoning tasks but falls slightly behind in some coding competitions.
- 💰 GPT-OSS 120B is significantly cheaper than O4 Mini High, with prices around $0.10 per million input tokens and $0.50 per million output tokens from the cheapest providers.
- 🏃♂️ In a coding competition, both GPT-OSS 120B and O4 Mini High struggled with complex tasks, often producing non-functional or basic implementations.
- 💥 GPT-OSS 120B showed faster inference speeds compared to O4 Mini High, but its performance in coding tasks was inconsistent.
- 🏆 O4 Mini High outperformed GPT-OSS 120B in specific reasoning questions and coding tasks, though GPT-OSS 120B had some strong performances in certain areas.
- 🔍 The open-source model GPT-OSS 120B is a good option for its price and speed, but it is not on par with O4 Mini High in terms of overall performance.
- 📈 Despite its smaller size, GPT-OSS-120B is one of the better open-source models available, though it still lags behind some proprietary models.
- 👀 The presenter suggests that while GPT-OSS 120B is useful for some tasks, it is not suitable for complex code generation and reasoning tasks where accuracy is critical.
Q & A
What are the two models being compared in the video?
-The two models being compared are the GPT-OSS 120B and the OpenAI O4 Mini High.
What is the primary difference between ChatGPT OSS 120B and O4 Mini High in terms of performance?
-While ChatGPT OSS 120B is a smaller and cheaper model, it performs on par with O4 Mini High in many reasoning tasks, though O4 Mini High generally has a slight edge in overall performance.
How does the inference speed of GPT-OSS 120B compare to O4 Mini High?
-GPT-OSS 120B has a much faster inference speed compared to O4 Mini High, often generating responses within seconds, while O4 Mini High takes longer.
What are the main benchmarks used to compare the models?
-The benchmarks used include Codeforces competition, Humanity's Last Exam, AIME 2024 and 2025, reasoning tasks, MMLU benchmark, and function calling.
How do the models perform in the Codeforces competition?
-O4 Mini scores 2719, while GPT-OSS 120B scores slightly lower at 2622.
What is the context length of GPT-OSS 120B?
-The context length of GPT-OSS 120B is 128K, which is lower compared to some modern models that offer up to 256K.
What is the pricing difference between O4 Mini High and GPT-OSS 120B?
-O4 Mini High is priced at $1.10 per million input tokens and $4.40 per million output tokens, while GPT-OSS 120B is around eight times cheaper, with the cheapest provider offering it at $0.10 per million input tokens and $0.50 per million output tokens.
How do the models perform in coding tasks?
-Both models struggle with complex coding tasks, often introducing bugs or failing to produce functional code. However, GPT-OSS 120B is faster in generating responses, though not necessarily more accurate.
What is the advantage of using GPT-OSS 120B over O4 Mini High?
-The main advantage of GPT-OSS 120B is its significantly lower cost and faster inference speed, making it suitable for tasks where speed and cost are more important than absolute performance.
What is the conclusion regarding the comparison between GPT-OSS 120B and O4 Mini High?
-While GPT-OSS 120B is a strong open-source model, it does not surpass O4 Mini High in overall performance. However, it is a good option for users who prioritize cost and speed over performance.
Outlines
💻 Introduction to GPT OSS Models and Their Performance
The paragraph introduces OpenAI's newly released open-source models, GPT OSS 120B and GPT OSS 20V, highlighting their performance in reasoning tasks and tool use capabilities. These models are trained using techniques similar to those used for O3 and other frontier systems. The GPT OSS 120B model is compared to OpenAI's O4 Mini, showing that it performs very close to O4 Mini in core reasoning tasks. The model can run on a single 80GB GPU, such as Nvidia H100. Detailed specifications of the GPT OSS 12B model are provided, including its 36 layers, 117 billion parameters, and context length of 128K. The models are primarily trained on English text data, focusing on STEM coding and general knowledge. Benchmarks are discussed, showing that GPT OSS 120B performs well in various tests, such as Codeforces competition, humanity's last exam, and AIME 2024, though slightly lower than O4 Mini. The paragraph concludes with a discussion of the models' ability to use tools like web search and Python code execution.
📊 Comparative Analysis of GPT OSS and O4 Mini High
This paragraph presents a detailed comparison between GPT OSS 120B and O4 Mini High through a series of challenging reasoning questions. The questions include topics like tennis player arrangement, seating arrangement, jersey number identification, and Navy designation. The results show that O4 Mini High generally outperforms GPT OSS 120B, scoring higher in most questions. The total scores are 11 out of 40 for O4 Mini High and 8 out of 40 for GPT OSS 120B. The paragraph also discusses the pricing of these models, with O4 Mini High costing $1.10 per million input tokens and $4.40 per million output tokens, while GPT OSS 12B is significantly cheaper at $0.10 per million input tokens and $0.50 per million output tokens. A coding competition is then conducted between the two models, where they are tasked with creating a dragon using HTML, CSS, and JS, and implementing a Super Mario game. The results indicate that while GPT OSS 120B has faster inference speed, both models struggle with front-end design tasks.
🎮 Further Coding Challenges and Performance Evaluation
The paragraph continues with more coding challenges to evaluate the performance of GPT OSS 120B and O4 Mini High. The tasks include creating a hollow circle with an opening and balls inside, simulating Earth's rotation, and developing a Pac-Man game. The results show that both models struggle with these complex coding tasks, often producing non-functional or incomplete code. GPT OSS 120B is noted for its fast inference speed, but its code often fails to work correctly even after feedback. O4 Mini High, while slower, provides more reliable but still imperfect code. The paragraph concludes that neither model is suitable for complex code generation, though they may be useful for simpler tasks or making small edits.
🎵 Final Coding Test and Conclusion
In the final paragraph, the models are tasked with creating a music equalizer in Python. Both O4 Mini High and GPT OSS 120B fail to produce working code, further emphasizing their limitations in complex coding tasks. The paragraph summarizes the overall findings, stating that while GPT OSS 120B is a good open-source model, it does not surpass O4 Mini High in reasoning or coding performance. The author concludes by encouraging viewers to stay tuned for updates on new models and developments in the AI space, emphasizing the importance of following the latest information for staying ahead in the field.
Mindmap
Keywords
💡OpenAI
💡GPT OSS 120B
💡O4 Mini High
💡Reasoning Tasks
💡Coding Competition
💡Benchmark
💡Inference Speed
💡Context Length
💡Parameter
💡Tool Use Capabilities
Highlights
OpenAI has released an open-source model named GPT OSS, with versions GPT OSS 120B and GPT OSS 20V.
GPT OSS 120B performs very close to OpenAI O4 Mini in core reasoning tasks.
GPT OSS 120B can run on a single 80GB GPU, such as Nvidia H100.
GPT OSS 120B has 36 layers, 117 billion parameters, and uses 5.1 billion parameters for next-token prediction.
The model uses 128 experts, with 4 experts used for each next-token prediction.
GPT OSS models are trained primarily on English text data, focusing on STEM coding and general knowledge.
In benchmark tests, GPT OSS 120B scores slightly lower than O4 Mini in Codeforces competition.
GPT OSS 120B outperforms O4 Mini in function calling tasks.
GPT OSS 120B scores 97.9 in EIME 2025, while O4 Mini scores 99.5.
In practical testing, GPT OSS 120B scored 8 out of 40 in reasoning questions, compared to O4 Mini High's 11 out of 40.
GPT OSS 120B is significantly cheaper than O4 Mini High, with a cost of $0.10 per million input tokens and $0.50 per million output tokens.
In coding tasks, both O4 Mini High and GPT OSS 120B struggled to provide accurate and functional code.
GPT OSS 120B demonstrated faster inference speed compared to O4 Mini High in coding tasks.
Despite its smaller size, GPT OSS 120B is considered a strong open-source model but not as advanced as O4 Mini High.
The open-source model is better suited for small edits and simple tasks rather than complex code generation.