GPT-5 Mini vs Mistral Medium 3.1

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

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OpenAIGPT-5 Mini
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MistralMistral Medium 3.1
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GPT-5 Mini vs Mistral Medium 3.1: Overview

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

Mistral Medium 3.1

Mistral Medium 3.1, released in August 2025 as the mistral-medium-2508 update, is a proprietary frontier model from Mistral AI positioned between smaller open models and high-end closed LLMs. It is multimodal, handling both text and image inputs, with a context window of ~128K tokens.

Compared to Mistral Medium 3.0, the 3.1 release introduces improvements in reasoning, coding, STEM, and enterprise workflows, along with better tone control for conversational and business applications. It is designed for scalable enterprise deployments, including hybrid cloud and on-premises VPC setups. As part of Mistral’s Premier line, Medium 3.1 is a commercial-only offering: while it delivers strong accuracy and performance, trade-offs include higher costs than open-weight models, restricted fine-tuning access, and increased latency/cost for very large contexts.

GPT-5 Mini vs Mistral Medium 3.1 Comparison Table

PropertyGPT-5 MiniMistral Medium 3.1
OrganizationOpenAIMistral
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2025Aug 2025
Context Window400K128K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.250$0.400
Output $/1M$2.00$2.00
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Object DetectionDemo
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
73.13%
Avg Response Time11.72s
Median input tokensincl. image tokens1.4K
Median output tokens143
Est. cost / taskon this benchmark$0.0006
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
10%(1/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
89.5%(17/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology