Mistral Small 3.1 24B Base vs Qwen2.5-Coder 32B Instruct
Mistral Small 3.1 24B Base significantly outperforms across most benchmarks. Qwen2.5-Coder 32B Instruct is 1.7x cheaper per token.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Mistral Small 3.1 24B Base outperforms in 2 benchmarks (MMLU, MMLU-Pro), while Qwen2.5-Coder 32B Instruct is better at 0 benchmarks. Mistral Small 3.1 24B Base significantly outperforms across most benchmarks.
On price, Qwen2.5-Coder 32B Instruct is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current benchmark, pricing, and model metadata for 2026.
Choose Mistral Small 3.1 24B Base
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- you want the most recent training data — it shipped Mar 2025
Choose Qwen2.5-Coder 32B Instruct
- cost matters — it's about 1.7x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
Mistral Small 3.1 24B Base outperforms in 2 benchmarks (MMLU, MMLU-Pro), while Qwen2.5-Coder 32B Instruct is better at 0 benchmarks.
Mistral Small 3.1 24B Base significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Mistral Small 3.1 24B Base ($0.10/1M tokens) is 1.1x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).
For output processing, Mistral Small 3.1 24B Base ($0.30/1M tokens) is 3.3x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).
In conclusion, Mistral Small 3.1 24B Base 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 8.0B more parameters than Mistral Small 3.1 24B Base, making it 33.3% 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.
Input Capabilities
Supported data types and modalities
Mistral Small 3.1 24B Base supports multimodal inputs, whereas Qwen2.5-Coder 32B Instruct does not.
Mistral Small 3.1 24B Base can handle both text and other forms of data like images, making it suitable for multimodal applications.
Mistral Small 3.1 24B Base
Qwen2.5-Coder 32B Instruct
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Mistral Small 3.1 24B Base was released on 2025-03-17, while Qwen2.5-Coder 32B Instruct was released on 2024-09-19.
Mistral Small 3.1 24B Base is 6 months newer than Qwen2.5-Coder 32B Instruct.
Mar 17, 2025
1.4 years ago
5mo newerSep 19, 2024
1.9 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
Mistral Small 3.1 24B Base is available from Mistral AI. Qwen2.5-Coder 32B Instruct is available from Lambda, DeepInfra, Hyperbolic, Fireworks.
Mistral Small 3.1 24B Base
Qwen2.5-Coder 32B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Small 3.1 24B Base and Qwen2.5-Coder 32B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Small 3.1 24B Base vs Qwen2.5-Coder 32B Instruct.