GPT-4o vs Phi-3.5-mini-instruct
GPT-4o leads the LLM Stats Score 14.3 to -3.8. Phi-3.5-mini-instruct is 43.8x cheaper per token.
OpenAI · Microsoft · Updated for 2026
Which is better?
GPT-4o leads the overall LLM Stats Score 14.3 to -3.8, ranking #226 overall.
In the 4 individual benchmarks reported for both models, GPT-4o wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 43.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-4o
- overall performance matters — it scores 14.3 and ranks #226 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
Choose Phi-3.5-mini-instruct
- cost matters — it's about 43.8x cheaper per token
- you want the most recent training data — it shipped Aug 2024
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
38 reported for GPT-4o · 31 for Phi-3.5-mini-instruct
GPT-4o outperforms in 4 benchmarks (GPQA, MMLU, MMLU-Pro, MMMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.
GPT-4o significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4o ($2.50/1M tokens) is 25.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, GPT-4o ($10.00/1M tokens) is 100.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, GPT-4o is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Both models have the same input context window of 128,000 tokens. Phi-3.5-mini-instruct can generate longer responses up to 128,000 tokens, while GPT-4o is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4o supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
GPT-4o can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4o
Phi-3.5-mini-instruct
License
Usage and distribution terms
GPT-4o 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.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
GPT-4o was released on 2024-08-06, while Phi-3.5-mini-instruct was released on 2024-08-23.
Phi-3.5-mini-instruct is 1 month newer than GPT-4o.
Aug 6, 2024
2.1 years ago
Aug 23, 2024
2.0 years ago
2w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT-4o is available from Azure, OpenAI. Phi-3.5-mini-instruct is available from Azure.
GPT-4o
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4o and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o vs Phi-3.5-mini-instruct.