Phi-3.5-mini-instruct vs Qwen2.5-Coder 32B Instruct
Qwen2.5-Coder 32B Instruct leads the LLM Stats Score 2.2 to -3.8. Qwen2.5-Coder 32B Instruct is 1.1x cheaper per token.
Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen2.5-Coder 32B Instruct leads the overall LLM Stats Score 2.2 to -3.8, ranking #305 overall.
In the 10 individual benchmarks reported for both models, Qwen2.5-Coder 32B Instruct wins 8; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen2.5-Coder 32B Instruct is roughly 1.1x 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 Phi-3.5-mini-instruct
- you want predictable pricing at $0.10/M input and $0.10/M output
Choose Qwen2.5-Coder 32B Instruct
- overall performance matters — it scores 2.2 and ranks #305 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 8 of 10 exact shared results
- cost matters — it's about 1.1x cheaper per token
- 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 · 15 for Qwen2.5-Coder 32B Instruct
Phi-3.5-mini-instruct outperforms in 2 benchmarks (ARC-C, TruthfulQA), while Qwen2.5-Coder 32B Instruct is better at 8 benchmarks (GSM8k, HellaSwag, HumanEval, MATH, MBPP, MMLU, MMLU-Pro, Winogrande).
Qwen2.5-Coder 32B 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 1.1x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).
For output processing, Phi-3.5-mini-instruct ($0.10/1M tokens) is 1.1x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).
In conclusion, Phi-3.5-mini-instruct is more expensive than Qwen2.5-Coder 32B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen2.5-Coder 32B Instruct has 28.2B more parameters than Phi-3.5-mini-instruct, making it 742.1% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 128,000 tokens. Both models can generate responses up to 128,000 tokens.
License
Usage and distribution terms
Phi-3.5-mini-instruct is licensed under MIT, while Qwen2.5-Coder 32B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Phi-3.5-mini-instruct was released on 2024-08-23, while Qwen2.5-Coder 32B Instruct was released on 2024-09-19.
Qwen2.5-Coder 32B 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-Coder 32B Instruct is available from Lambda, DeepInfra, Hyperbolic, Fireworks.
Phi-3.5-mini-instruct
Qwen2.5-Coder 32B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Phi-3.5-mini-instruct and Qwen2.5-Coder 32B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Phi-3.5-mini-instruct vs Qwen2.5-Coder 32B Instruct.