GPT OSS 120B vs Phi-3.5-mini-instruct
GPT OSS 120B leads the LLM Stats Score 29.2 to -3.8. Phi-3.5-mini-instruct is 1.8x cheaper per token.
OpenAI · Microsoft · Updated for 2026
Which is better?
GPT OSS 120B leads the overall LLM Stats Score 29.2 to -3.8, ranking #123 overall.
In the 2 individual benchmarks reported for both models, GPT OSS 120B wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT OSS 120B also accepts a larger context window (131,072 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 OSS 120B
- overall performance matters — it scores 29.2 and ranks #123 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you process long inputs — it offers a 131,072 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 1.8x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GPT OSS 120B · 31 for Phi-3.5-mini-instruct
GPT OSS 120B outperforms in 2 benchmarks (GPQA, MMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.
GPT OSS 120B 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 OSS 120B ($0.09/1M tokens) is 1.1x cheaper than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, GPT OSS 120B ($0.45/1M tokens) is 4.5x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, GPT OSS 120B is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GPT OSS 120B has 113.0B more parameters than Phi-3.5-mini-instruct, making it 2973.7% larger.
Context Window
Maximum input and output token capacity
GPT OSS 120B accepts 131,072 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. GPT OSS 120B can generate longer responses up to 131,072 tokens, while Phi-3.5-mini-instruct is limited to 128,000 tokens.
License
Usage and distribution terms
GPT OSS 120B is licensed under Apache 2.0, while Phi-3.5-mini-instruct uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GPT OSS 120B was released on 2025-08-05, while Phi-3.5-mini-instruct was released on 2024-08-23.
GPT OSS 120B is 12 months newer than Phi-3.5-mini-instruct.
Aug 5, 2025
1.1 years ago
11mo newerAug 23, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT OSS 120B is available from DeepInfra, Novita, OpenAI, Fireworks, Groq. Phi-3.5-mini-instruct is available from Azure.
GPT OSS 120B
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT OSS 120B and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT OSS 120B vs Phi-3.5-mini-instruct.