DeepSeek R1 Distill Qwen 14B vs Qwen2.5-Coder 32B Instruct
DeepSeek R1 Distill Qwen 14B leads the LLM Stats Score 10.9 to 2.0.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek R1 Distill Qwen 14B leads the overall LLM Stats Score 10.9 to 2.0, ranking #276 overall.
In the 1 individual benchmarks reported for both models, DeepSeek R1 Distill Qwen 14B wins 1; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek R1 Distill Qwen 14B
- overall performance matters — it scores 10.9 and ranks #276 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 Qwen2.5-Coder 32B Instruct
- you want predictable pricing at $0.09/M input and $0.09/M output
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 14B · 15 for Qwen2.5-Coder 32B Instruct
DeepSeek R1 Distill Qwen 14B outperforms in 1 benchmarks (LiveCodeBench), while Qwen2.5-Coder 32B Instruct is better at 0 benchmarks.
DeepSeek R1 Distill Qwen 14B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
Qwen2.5-Coder 32B Instruct has 17.2B more parameters than DeepSeek R1 Distill Qwen 14B, making it 116.2% larger.
Context Window
Maximum input and output token capacity
Only Qwen2.5-Coder 32B Instruct specifies input context (128,000 tokens). Only Qwen2.5-Coder 32B Instruct specifies output context (128,000 tokens).
License
Usage and distribution terms
DeepSeek R1 Distill Qwen 14B 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
DeepSeek R1 Distill Qwen 14B was released on 2025-01-20, while Qwen2.5-Coder 32B Instruct was released on 2024-09-19.
DeepSeek R1 Distill Qwen 14B is 4 months newer than Qwen2.5-Coder 32B Instruct.
Jan 20, 2025
1.7 years ago
4mo newerSep 19, 2024
2.1 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Distill Qwen 14B and Qwen2.5-Coder 32B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Distill Qwen 14B vs Qwen2.5-Coder 32B Instruct.