GPT-5
Unified model with adaptive reasoning depth and multimodal capabilities
Overall Highlight
Adaptive reasoning with 256K context — auto-scales compute based on task difficulty
Overview
GPT-5 introduces OpenAI's unified model architecture that dynamically adjusts reasoning depth based on task complexity. Features native multimodal understanding (text, image, audio), improved instruction following, and enhanced tool use. The model automatically allocates more compute to harder problems, reducing the need for manual reasoning mode selection.
Capabilities
- ▸Adaptive reasoning depth (auto-scaling compute)
- ▸Native multimodal understanding
- ▸Improved function calling and tool use
- ▸Enhanced instruction following
- ▸Code generation with execution feedback
- ▸Structured output with strict mode
Use Cases
- →General-purpose AI assistance
- →Multimodal content analysis
- →Automated coding with iterative refinement
- →Data analysis and visualization
- →Content generation at scale
Version Breakdown
GPT-5
2025 Q3Context Window
256K tokens
Parameters
Undisclosed
Release
2025 Q3
Highlights
- ▸Adaptive reasoning — auto-allocates compute to hard problems
- ▸Unified model replacing separate reasoning variants
- ▸Native multimodal (text + image + audio)
- ▸Best-in-class instruction following
Benchmarks
MMLU
90.1
HumanEval
94.2
GSM8K
97.1
MATH
80.3
GPT-5 mini
2025 Q3Context Window
128K tokens
Parameters
Undisclosed
Release
2025 Q3
Highlights
- ▸Compact variant for cost-sensitive workloads
- ▸3x cheaper than GPT-5
- ▸Retains multimodal capabilities
- ▸Excellent for high-volume applications
Benchmarks
MMLU
84.0
HumanEval
88.1
GSM8K
93.2
MATH
70.5
GPT-4o
2024 Q2Context Window
128K tokens
Parameters
~200B (multimodal)
Release
2024 Q2
Highlights
- ▸Previous flagship — mature and stable
- ▸Excellent ecosystem support
- ▸Strong multimodal capabilities
- ▸Good cost-to-performance ratio
Benchmarks
MMLU
83.1
HumanEval
85.4
GSM8K
90.5
MATH
64.2