Gemini 2.5 Flash vs Mistral Medium 3.1

Compare Gemini 2.5 Flash and Mistral Medium 3.1 side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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GoogleGemini 2.5 Flash
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MistralMistral Medium 3.1
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Gemini 2.5 Flash vs Mistral Medium 3.1: Overview

Gemini 2.5 Flash

Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.

Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.

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.

Gemini 2.5 Flash vs Mistral Medium 3.1 Comparison Table

PropertyGemini 2.5 FlashMistral Medium 3.1
OrganizationGoogleMistral
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Aug 2025
Context Window1.0M128K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.300$0.400
Output $/1M$2.50$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
55.22%
Avg Response Time24.91s
Median input tokensincl. image tokens294
Median output tokens171
Est. cost / taskon this benchmark$0.0005
Defect Detection
60%(9/15)
Document Understanding
88.9%(8/9)
Object Counting
0%(0/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
52.6%(10/19)

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