GPT-5 mini vs Phi-3.5-mini-instruct
GPT-5 mini leads the LLM Stats Score 27.6 to -3.8. Phi-3.5-mini-instruct is 6.9x cheaper per token.
OpenAI · Microsoft · Updated for 2026
Which is better?
GPT-5 mini leads the overall LLM Stats Score 27.6 to -3.8, ranking #134 overall.
In the 1 individual benchmarks reported for both models, GPT-5 mini wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 6.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5 mini also accepts a larger context window (400,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-5 mini
- overall performance matters — it scores 27.6 and ranks #134 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 400,000 token context window
- you want the most recent training data — it shipped Aug 2025
Choose Phi-3.5-mini-instruct
- cost matters — it's about 6.9x cheaper per token
- 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
5 reported for GPT-5 mini · 31 for Phi-3.5-mini-instruct
GPT-5 mini outperforms in 1 benchmarks (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks.
GPT-5 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-5 mini ($0.25/1M tokens) is 2.5x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, GPT-5 mini ($2.00/1M tokens) is 20.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, GPT-5 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
GPT-5 mini accepts 400,000 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Both models can generate responses up to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
GPT-5 mini supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
GPT-5 mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-5 mini
Phi-3.5-mini-instruct
License
Usage and distribution terms
GPT-5 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-5 mini was released on 2025-08-07, while Phi-3.5-mini-instruct was released on 2024-08-23.
GPT-5 mini is 12 months newer than Phi-3.5-mini-instruct.
Aug 7, 2025
1.1 years ago
11mo newerAug 23, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
GPT-5 mini has a documented knowledge cutoff of 2024-05-30, while Phi-3.5-mini-instruct's cutoff date is not specified.
We can confirm GPT-5 mini's training data extends to 2024-05-30, but cannot make a direct comparison without Phi-3.5-mini-instruct's cutoff date.
May 2024
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Provider Availability
GPT-5 mini is available from OpenAI. Phi-3.5-mini-instruct is available from Azure.
GPT-5 mini
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5 mini and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5 mini vs Phi-3.5-mini-instruct.