DeepSeek R1 Distill Qwen 32B vs Mistral NeMo Instruct
DeepSeek R1 Distill Qwen 32B leads the LLM Stats Score 13.1 to -4.8. Mistral NeMo Instruct is 6.2x cheaper per token.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek R1 Distill Qwen 32B leads the overall LLM Stats Score 13.1 to -4.8, ranking #254 overall.
On price, Mistral NeMo Instruct is roughly 6.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral NeMo 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 DeepSeek R1 Distill Qwen 32B
- overall performance matters — it scores 13.1 and ranks #254 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you want the most recent training data — it shipped Jan 2025
Choose Mistral NeMo Instruct
- cost matters — it's about 6.2x cheaper per token
- you process long inputs — it offers a 131,072 token context window
At a glance
The differences that matter most.
Individual benchmarks
4 reported for DeepSeek R1 Distill Qwen 32B · 8 for Mistral NeMo Instruct
DeepSeek R1 Distill Qwen 32B and Mistral NeMo Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 6.3x more expensive than Mistral NeMo Instruct ($0.02/1M tokens).
For output processing, DeepSeek R1 Distill Qwen 32B ($0.18/1M tokens) is 6.0x more expensive than Mistral NeMo Instruct ($0.03/1M tokens).
In conclusion, DeepSeek R1 Distill Qwen 32B is more expensive than Mistral NeMo Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek R1 Distill Qwen 32B has 20.8B more parameters than Mistral NeMo Instruct, making it 173.3% larger.
Context Window
Maximum input and output token capacity
Mistral NeMo Instruct accepts 131,072 input tokens compared to DeepSeek R1 Distill Qwen 32B's 128,000 tokens. Mistral NeMo Instruct can generate longer responses up to 131,072 tokens, while DeepSeek R1 Distill Qwen 32B is limited to 128,000 tokens.
License
Usage and distribution terms
DeepSeek R1 Distill Qwen 32B is licensed under MIT, while Mistral NeMo 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 32B was released on 2025-01-20, while Mistral NeMo Instruct was released on 2024-07-18.
DeepSeek R1 Distill Qwen 32B is 6 months newer than Mistral NeMo Instruct.
Jan 20, 2025
1.7 years ago
6mo newerJul 18, 2024
2.2 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. Mistral NeMo Instruct is available from DeepInfra, Google, Mistral AI.
DeepSeek R1 Distill Qwen 32B
Mistral NeMo Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek R1 Distill Qwen 32B and Mistral NeMo Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek R1 Distill Qwen 32B vs Mistral NeMo Instruct.