DeepSeek R1 Distill Qwen 32B vs Phi-3.5-mini-instruct
DeepSeek R1 Distill Qwen 32B leads the LLM Stats Score 13.3 to -3.7. Phi-3.5-mini-instruct is 1.4x cheaper per token.
DeepSeek · Microsoft · Updated for 2026
Which is better?
DeepSeek R1 Distill Qwen 32B leads the overall LLM Stats Score 13.3 to -3.7, ranking #231 overall.
In the 1 individual benchmarks reported for both models, DeepSeek R1 Distill Qwen 32B wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 1.4x 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 DeepSeek R1 Distill Qwen 32B
- overall performance matters — it scores 13.3 and ranks #231 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 want the most recent training data — it shipped Jan 2025
Choose Phi-3.5-mini-instruct
- cost matters — it's about 1.4x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
4 reported for DeepSeek R1 Distill Qwen 32B · 31 for Phi-3.5-mini-instruct
DeepSeek R1 Distill Qwen 32B outperforms in 1 benchmarks (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks.
DeepSeek R1 Distill Qwen 32B 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, DeepSeek R1 Distill Qwen 32B ($0.12/1M tokens) is 1.2x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, DeepSeek R1 Distill Qwen 32B ($0.18/1M tokens) is 1.8x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, DeepSeek R1 Distill Qwen 32B is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek R1 Distill Qwen 32B has 29.0B more parameters than Phi-3.5-mini-instruct, making it 763.2% 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
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while Phi-3.5-mini-instruct was released on 2024-08-23.
DeepSeek R1 Distill Qwen 32B is 5 months newer than Phi-3.5-mini-instruct.
Jan 20, 2025
1.6 years ago
5mo 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
DeepSeek R1 Distill Qwen 32B is available from DeepInfra. Phi-3.5-mini-instruct is available from Azure.
DeepSeek R1 Distill Qwen 32B
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Distill Qwen 32B and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Distill Qwen 32B vs Phi-3.5-mini-instruct.