Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

OpenAI

GPT-4.1 nano

OpenAI

GPT-4.1 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 25 benchmarks. It excels particularly in MMLU (80.1%), IFEval (74.5%), CharXiv-D (73.9%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents OpenAI's latest advancement in AI technology.

Microsoft

Phi-3.5-mini-instruct

Microsoft

Phi-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.

Microsoft

Phi-3.5-mini-instruct

Microsoft

2024-08-23

OpenAI

GPT-4.1 nano

OpenAI

2025-04-14

7 months newer

Pricing Comparison

Cost per million tokens (USD)

OpenAI

GPT-4.1 nano

Input:$0.10
Output:$0.40
Microsoft

Phi-3.5-mini-instruct

$0.30 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

OpenAI

GPT-4.1 nano

Larger context
Max Context:1.1M
Microsoft

Phi-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 3 common benchmarks

OpenAI

GPT-4.1 nano

+14.2%
Average Score:65.8%
Microsoft

Phi-3.5-mini-instruct

Average Score:51.6%

Performance comparison across key benchmark categories

OpenAI

GPT-4.1 nano

code
+8.3%
74.5%
math
56.2%
general
32.4%
Microsoft

Phi-3.5-mini-instruct

code
66.2%
math
+4.7%
60.9%
general
+23.0%
55.4%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

GPT-4.1 nano

2024-05-31

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

OpenAI

GPT-4.1 nano

1 providers

OpenAI

Throughput: 200 tok/s
Latency: 2ms
Microsoft

Phi-3.5-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms
OpenAI

GPT-4.1 nano

+14.2%
Avg Score:65.8%
Providers:1
Microsoft

Phi-3.5-mini-instruct

Avg Score:51.6%
Providers:1