GPT-4o mini vs Phi-3.5-mini-instruct
GPT-4o mini leads the LLM Stats Score 4.2 to -3.3. Phi-3.5-mini-instruct is 2.6x cheaper per token.
OpenAI · Microsoft · Updated for 2026
Which is better?
GPT-4o mini leads the overall LLM Stats Score 4.2 to -3.3, ranking #285 overall.
In the 5 individual benchmarks reported for both models, GPT-4o mini wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 2.6x 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 mini
- overall performance matters — it scores 4.2 and ranks #285 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
Choose Phi-3.5-mini-instruct
- cost matters — it's about 2.6x 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
9 reported for GPT-4o mini · 31 for Phi-3.5-mini-instruct
GPT-4o mini outperforms in 5 benchmarks (GPQA, HumanEval, MATH, MGSM, MMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.
GPT-4o mini 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 mini ($0.15/1M tokens) is 1.5x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, GPT-4o mini ($0.60/1M tokens) is 6.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, GPT-4o mini 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 mini is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4o mini supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
GPT-4o mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4o mini
Phi-3.5-mini-instruct
License
Usage and distribution terms
GPT-4o mini 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 mini was released on 2024-07-18, while Phi-3.5-mini-instruct was released on 2024-08-23.
Phi-3.5-mini-instruct is 1 month newer than GPT-4o mini.
Jul 18, 2024
2.1 years ago
Aug 23, 2024
2.0 years ago
1mo newerKnowledge Cutoff
When training data ends
GPT-4o mini has a documented knowledge cutoff of 2023-10-01, while Phi-3.5-mini-instruct's cutoff date is not specified.
We can confirm GPT-4o mini's training data extends to 2023-10-01, but cannot make a direct comparison without Phi-3.5-mini-instruct's cutoff date.
Oct 2023
—
Provider Availability
GPT-4o mini is available from Azure. Phi-3.5-mini-instruct is available from Azure.
GPT-4o mini
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4o mini and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o mini vs Phi-3.5-mini-instruct.