Model Comparison

GPT-4.1 nano vs Phi-3.5-mini-instruct

GPT-4.1 nano significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is 1.8x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

GPT-4.1 nano outperforms in 3 benchmarks (GPQA, MMLU, MMMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.

GPT-4.1 nano significantly outperforms across most benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Phi-3.5-mini-instruct costs less

For input processing, GPT-4.1 nano ($0.10/1M tokens) costs the same as Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, GPT-4.1 nano ($0.40/1M tokens) is 4.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, GPT-4.1 nano is more expensive than Phi-3.5-mini-instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sat May 02 2026 • llm-stats.com
OpenAI
GPT-4.1 nano
Input tokens$0.10
Output tokens$0.40
Best providerOpenAI
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

GPT-4.1 nano accepts 1,047,576 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Phi-3.5-mini-instruct can generate longer responses up to 128,000 tokens, while GPT-4.1 nano is limited to 32,768 tokens.

OpenAI
GPT-4.1 nano
Input1,047,576 tokens
Output32,768 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GPT-4.1 nano supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

GPT-4.1 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.

GPT-4.1 nano

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GPT-4.1 nano is licensed under a proprietary license, while Phi-3.5-mini-instruct uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

GPT-4.1 nano

Proprietary

Closed source

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

GPT-4.1 nano was released on 2025-04-14, while Phi-3.5-mini-instruct was released on 2024-08-23.

GPT-4.1 nano is 8 months newer than Phi-3.5-mini-instruct.

GPT-4.1 nano

Apr 14, 2025

1.0 years ago

7mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.7 years ago

Knowledge Cutoff

When training data ends

GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Phi-3.5-mini-instruct's cutoff date is not specified.

We can confirm GPT-4.1 nano's training data extends to 2024-05-31, but cannot make a direct comparison without Phi-3.5-mini-instruct's cutoff date.

GPT-4.1 nano

May 2024

Phi-3.5-mini-instruct

—

Provider Availability

GPT-4.1 nano is available from OpenAI. Phi-3.5-mini-instruct is available from Azure.

GPT-4.1 nano

openai logo
OpenAI
Input Price:Input: $0.10/1MOutput Price:Output: $0.40/1M

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

OpenAI
Larger context window (1,047,576 tokens)
Supports multimodal inputs
Higher GPQA score (50.3% vs 30.4%)
Higher MMLU score (80.1% vs 69.0%)
Higher MMMLU score (66.9% vs 55.4%)
Microsoft
Less expensive output tokens
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
OpenAI
GPT-4.1 nano
Microsoft
Phi-3.5-mini-instruct

FAQ

Common questions about GPT-4.1 nano vs Phi-3.5-mini-instruct

GPT-4.1 nano significantly outperforms across most benchmarks. GPT-4.1 nano is made by OpenAI and Phi-3.5-mini-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GPT-4.1 nano scores MMLU: 80.1%, IFEval: 74.5%, CharXiv-D: 73.9%, MMMLU: 66.9%, Multi-IF: 57.2%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.
Both models cost $0.10 per million input tokens.
GPT-4.1 nano supports 1.0M tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (1.0M vs 128K), multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.
GPT-4.1 nano is developed by OpenAI and Phi-3.5-mini-instruct is developed by Microsoft.