GLM-5.3-Flash vs Mistral Small 3.1 24B Base
Comparing GLM-5.3-Flash and Mistral Small 3.1 24B Base across benchmarks, pricing, and capabilities.
Zhipu AI · Mistral AI · Updated for 2026
Which is better?
GLM-5.3-Flash and Mistral Small 3.1 24B Base trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Mistral Small 3.1 24B Base is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Mistral Small 3.1 24B Base
- cost matters — it's about 1.6x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash and Mistral Small 3.1 24B Basedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 1.5x more expensive than Mistral Small 3.1 24B Base ($0.10/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 1.7x more expensive than Mistral Small 3.1 24B Base ($0.30/1M tokens).
In conclusion, GLM-5.3-Flash is more expensive than Mistral Small 3.1 24B Base.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 296.0B more parameters than Mistral Small 3.1 24B Base, making it 1233.3% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Mistral Small 3.1 24B Base's 128,000 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while Mistral Small 3.1 24B Base is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-5.3-Flash and Mistral Small 3.1 24B Base support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Mistral Small 3.1 24B Base
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Mistral Small 3.1 24B Base 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
GLM-5.3-Flash was released on 2026-08-26, while Mistral Small 3.1 24B Base was released on 2025-03-17.
GLM-5.3-Flash is 18 months newer than Mistral Small 3.1 24B Base.
Aug 26, 2026
0 days ago
1.4yr newerMar 17, 2025
1.4 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
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Mistral Small 3.1 24B Base is available from Mistral AI.
GLM-5.3-Flash
Mistral Small 3.1 24B Base
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Mistral Small 3.1 24B Base side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Mistral Small 3.1 24B Base.