Phi-3.5-mini-instruct vs Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads the LLM Stats Score 12.5 to -3.8. Phi-3.5-mini-instruct is 3.6x cheaper per token.
Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen2.5 72B Instruct leads the overall LLM Stats Score 12.5 to -3.8, ranking #239 overall.
In the 7 individual benchmarks reported for both models, Qwen2.5 72B Instruct wins 7; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 3.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen2.5 72B Instruct 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 Phi-3.5-mini-instruct
- cost matters — it's about 3.6x cheaper per token
Choose Qwen2.5 72B Instruct
- overall performance matters — it scores 12.5 and ranks #239 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 7 of 7 exact shared results
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Sep 2024
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
31 reported for Phi-3.5-mini-instruct · 14 for Qwen2.5 72B Instruct
Phi-3.5-mini-instruct outperforms in 0 benchmarks, while Qwen2.5 72B Instruct is better at 7 benchmarks (Arena Hard, GPQA, GSM8k, HumanEval, MATH, MBPP, MMLU-Pro).
Qwen2.5 72B Instruct 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, Phi-3.5-mini-instruct ($0.10/1M tokens) is 3.5x cheaper than Qwen2.5 72B Instruct ($0.35/1M tokens).
For output processing, Phi-3.5-mini-instruct ($0.10/1M tokens) is 4.0x cheaper than Qwen2.5 72B Instruct ($0.40/1M tokens).
In conclusion, Qwen2.5 72B Instruct is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen2.5 72B Instruct has 68.9B more parameters than Phi-3.5-mini-instruct, making it 1813.2% larger.
Context Window
Maximum input and output token capacity
Qwen2.5 72B Instruct accepts 131,072 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 Qwen2.5 72B Instruct is limited to 8,192 tokens.
License
Usage and distribution terms
Phi-3.5-mini-instruct is licensed under MIT, while Qwen2.5 72B Instruct uses Qwen.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen
Open weights
Release Timeline
When each model was launched
Phi-3.5-mini-instruct was released on 2024-08-23, while Qwen2.5 72B Instruct was released on 2024-09-19.
Qwen2.5 72B Instruct is 1 month newer than Phi-3.5-mini-instruct.
Aug 23, 2024
2.0 years ago
Sep 19, 2024
2.0 years ago
3w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Phi-3.5-mini-instruct is available from Azure. Qwen2.5 72B Instruct is available from DeepInfra, Hyperbolic, Fireworks, Together.
Phi-3.5-mini-instruct
Qwen2.5 72B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Phi-3.5-mini-instruct and Qwen2.5 72B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Phi-3.5-mini-instruct vs Qwen2.5 72B Instruct.