What is GPT-4.1?
GPT-4.1 is OpenAI’s latest next-generation language model, available in three versions:
- GPT-4.1 (Standard)
- GPT-4.1 mini (Lightweight)
- GPT-4.1 nano (Ultra-Lightweight)
This series significantly improves code generation, instruction following, and long-context processing, supporting a context window of up to 1 million tokens. In benchmark tests, GPT-4.1 demonstrates exceptional performance, such as:
- SWE-bench Verified (coding test): 54.6% accuracy, 21.4% higher than GPT-4o
- Lower cost: Currently OpenAI’s fastest and most economical model
The GPT-4.1 series is available exclusively via API and is now open to all developers.
Key Features of GPT-4.1
1. Ultra-Long Context Processing
- Supports 1 million tokens (8x GPT-4o’s capacity)
- Can process entire books, large codebases, or hundreds of pages of documents
2. Multimodal Capabilities
- Image Understanding: Separate visual and text encoders with cross-attention
- Video Understanding: Achieves 72% accuracy on Video-MME for 30-60min unsubtitled videos (state-of-the-art)
3. Code Generation & Optimization
- 54.6% accuracy on SWE-bench Verified (21.4% higher than GPT-4o)
- 2x improvement in multilingual coding
4. Efficient Tool Use
- 60% higher score than GPT-4o in Windsurf’s internal benchmarks, with 30% faster tool invocation
5. Complex Instruction Handling
- 10.5% higher score than GPT-4o on Scale MultiChallenge
- Significant improvement in following complex instructions (per OpenAI’s internal evaluations)
6. Low Latency & Cost Efficiency
- GPT-4.1 mini: 50% lower latency, 83% cost reduction
- GPT-4.1 nano: OpenAI’s fastest and cheapest model
Technical Architecture of GPT-4.1
1. Optimized Transformer Architecture
- Enhanced attention mechanisms for better long-context comprehension
2. Mixture of Experts (MoE)
- 16 independent expert models, each with 111B parameters
- Only 2 experts activated per inference for efficiency
3. Training Data
- Trained on 13 trillion tokens
4. Inference Optimization
- Techniques like dynamic batching reduce latency and cost
Performance Comparison
| Model | Coding (SWE-bench) | Multimodal (Video-MME) | Latency | Cost (Input/1M Tokens) |
|---|---|---|---|---|
| GPT-4.1 | 54.6% (+21.4%) | 72.0% (+6.7%) | Standard | 2/2/8 (Output) |
| GPT-4.1 mini | ≈GPT-4o level | Better than GPT-4o | ↓50% | 0.4/0.4/1.6 (Output) |
| GPT-4.1 nano | 80.1% (MMLU) | - | Fastest | 0.1/0.1/0.4 (Output) |
Pricing
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| GPT-4.1 | $2 | $8 |
| GPT-4.1 mini | $0.4 | $1.6 |
| GPT-4.1 nano | $0.1 | $0.4 |
Use Cases
- Legal: 17% higher accuracy in document review vs. GPT-4o
- Finance: Efficient analysis of large reports and market data
- Programming: Generates higher-quality front-end code (80%+ human preference)
