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GPT-5 Nano vs Mistral Small 3.1 24B

Compare GPT-5 Nano and Mistral Small 3.1 24B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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OpenAIGPT-5 Nano
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GPT-5 Nano vs Mistral Small 3.1 24B Comparison Table

Evals updated August 20, 2026Pricing updated August 23, 2026

PropertyGPT-5 NanoMistral Small 3.1 24B
OrganizationOpenAIMistral
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025Mar 2025
Context Window400K128K
Parameters24B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.050$0.351
Output $/1M$0.400$0.555
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Object DetectionDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

GPT-5 Nano vs Mistral Small 3.1 24B: Overview

GPT-5 Nano

GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.

GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.

Mistral Small 3.1 24B

Mistral Small 3.1 24B, released on March 17, 2025, is an open-weight multimodal model from Mistral AI, distributed under the Apache-2.0 license. With around 24B parameters and a 128K token context window, it is available in both base and instruction-tuned (“Instruct”) variants. The model introduces vision support alongside text, enabling tasks like multimodal reasoning, captioning, and image-based Q&A.

It is multilingual, supporting many languages, and is optimized for fast responses, function calling, structured dialogue, and long-context reasoning. Despite its size, the model can be run locally in quantized formats, fitting on machines with ~32GB RAM, making it accessible to developers outside large cloud setups. However, the output length is smaller than the 128K input window, meaning long generations may require chaining. In addition, using full vision features or the maximum context window significantly increases compute costs, and performance on highly complex reasoning or enterprise-scale tasks still trails larger proprietary frontier models.