Roboflow
OpenAI

OpenAI: GPT-4.1 mini

This model is deprecated

GPT-4.1 mini and can no longer be run here. Its evaluation results and details remain available for reference. Try GPT-5.6 Terra instead.

GPT-4.1 mini Overview

GPT-4.1 mini, released by OpenAI in April 2025, is a smaller, faster, and cheaper variant of GPT-4.1 designed for high-throughput and cost-sensitive applications. It is multimodal, handling both text and images, and inherits the full model’s strengths in coding, structured outputs, and long-context reasoning. With support for up to 1 million tokens, it enables reliable processing of extended documents, multi-file codebases, and lengthy conversations while keeping latency low.

GPT-4.1 mini offers an efficient alternative to GPT-4.1 and replaced GPT-4o mini as the default ChatGPT model in May 2025. Despite being smaller, it matches or outperforms GPT-4o on several benchmarks, particularly for instruction following and real-world coding tasks. Ideal use cases include large-scale conversational systems, affordable developer tools, document analysis, and interactive assistants where speed and cost are critical.

GPT-4.1 mini Details & Performance

Details

Resources

Vision Tasks

CaptioningChart Question AnsweringClassificationDocument Question AnsweringImage TaggingMulti-Label ClassificationOCRObject DetectionVision LanguageVisual Question Answering

Features

Foundation VisionLLMs with Vision CapabilitiesMultimodal Vision

Usage

Past 30 Days

Not available

Not in Playground

Performance

Avg. Latency

GPT-4.1 mini Vision Evals

GPT-4.1 mini has been deprecated by its provider and can no longer be evaluated on the current benchmark. The legacy Vision Evals results below are preserved for reference. See the current Vision Evals

Visual Understanding

77 models · 67 tasks
HighestLowest
This model#25 of 7768.66% pass rate · better than 62%
Score68.66%pass rate across 67 tasks
Speed2.54savg response per task
Cost$0.0008 / task$0.400 in · $1.60 out / 1M
Tokens1.9K / task1.9K in · 6 out
Score key:≥75%40–74%<40%
CategoryPassedScore
Defect Detection12 / 15
80%
Spatial Understanding15 / 19
78.9%
Object Understanding11 / 14
78.6%
Document Understanding7 / 9
77.8%
Object Counting1 / 10
10%
HighestLowest
This model#21 of 5879.91% pass rate · better than 64%
Score79.91%pass rate across 229 tasks
Speed2.05savg response per task
Cost$0.0001 / task$0.400 in · $1.60 out / 1M
Tokens143 / task130 in · 10 out
Score key:≥75%40–74%<40%
CategoryPassedScore
License Plate Recognition28 / 30
93.3%
Focused Scene OCR83 / 99
83.8%
Text Recognition24 / 30
80%
VQA & Extraction44 / 60
73.3%
Handwritten Math4 / 10
40%

Scores based on a single evaluation run · Methodology

View all legacy Vision Evals results →

GPT-4.1 mini Pricing

GPT-4.1 mini costs $0.400 per 1M input tokens and $1.60 per 1M output tokens.

Input$0.400 / 1M tokens
Output$1.60 / 1M tokens
Cached input$0.100 / 1M tokens

Pricing updated Aug 27, 2026

Price vs. performance

Estimated cost per task vs. Visual Understanding score, for this model and others ranked near it. Upper-left is the sweet spot (high quality, low cost). Based on Vision Evals (legacy) results.

10 of 11 models plotted · 1 not yet evaluated

ModelScoreMedian tokensEst. cost / taskCompare
AnthropicClaude Sonnet 570.2%2.2K$0.0048Compare
AnthropicClaude Sonnet 4.670.2%2.3K$0.0080Compare
OpenAIGPT-5.6 Luna70.2%1.5K$0.0003Compare
GoogleGemini 2.5 Pro70.2%856$0.0060Compare
GoogleGemini 3.1 Flash-Lite68.7%1.1K$0.0003Compare
OpenAIGPT-4.1 Mini(this model)68.7%1.9K
GoogleGemma 4 26B A4B68.7%531$0.0001Compare
QwenQwen3.6 Plus68.7%1.6K$0.0005Compare
AnthropicClaude Opus 4.867.2%2.2K$0.012Compare
AnthropicClaude Opus 4.767.2%2.6K$0.015Compare
GoogleGemma 4 31B67.2%467$0.0001Compare

Alternatives to GPT-4.1 mini

Other models worth comparing for similar use cases.

Google
Gemini 3 Flash
Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.
Anthropic
Claude Sonnet 4.6
Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
Qwen
Qwen3 VL 30B A3B Instruct
Qwen3 VL 30B A3B Instruct is an open-weight multimodal large language model developed by Alibaba as part of the Qwen family, built for instruction-following tasks that unify text generation with visual and video understanding. Released around October 2025 under the Apache-2.0 license, it targets efficient, high-fidelity vision-language reasoning across very long contexts.The model accepts text and image inputs and produces text outputs, with strong performance in OCR, spatial reasoning, long-video understanding, and agentic or GUI-centric visual tasks. It uses a Mixture-of-Experts (A3B) design with ~31.1B total parameters and ~3B active per token, paired with Qwen3-VL’s unified multimodal stack (including Interleaved-MRoPE and DeepStack fusion) to process text, images, and video in a single architecture. OCR support expands to 32 languages, enhancing document workflows. With a native ~262K token context window (extendable further), it stands out today for its balance of scale, efficiency, long-context support, and open accessibility in multimodal systems.
Google
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.

Other OpenAI GPT Mini models

Other versions in the same family as GPT-4.1 mini.

GPT-4.1 mini License

Proprietary

GPT-4.1 mini is proprietary: the weights are not distributed, and the GPT-4.1 mini license is the vendor's commercial terms of service that you accept when you call the API.

Commercial use
Permitted under the vendor terms, typically metered per token or per request, with the vendor usage policy applying to your inputs and outputs.
Modification
Not available. GPT-4.1 mini weights are closed, so you can configure prompts and use vendor-hosted fine-tuning where it is offered, but you cannot modify the model itself.
Redistribution
Not permitted. You cannot self-host or resell the model; you build on the hosted API instead.

Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.

Do I need a commercial license for GPT-4.1 mini?

Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy a model whose commercial license is included in your plan — on Roboflow Managed Cloud or a Self-Hosted Inference Server — Roboflow's licensing page lists the supported alternatives to GPT-4.1 mini.

Do not hesitate to reach out with questions for your commercial project — our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

Talk to sales

This model is proprietary. The author retains all rights, and use of the model is governed by their specific terms of service or license agreement.

Commercial use depends on the terms set by the model author. Most proprietary commercial models require a paid subscription, API key, or per-call billing. Check the provider’s pricing and terms-of-service for details.

License information is provided as a guide and is not legal advice.

Frequently Asked Questions About GPT-4.1 mini Vision

Yes. GPT-4.1 mini accepts image input, and on Roboflow's previous vision benchmark it passed 68.7% of visual understanding tasks (#25 of 77) and scored 79.9% on OCR.

GPT-4.1 mini has been deprecated by its provider and can no longer be run, so it is not part of Roboflow's current Vision Evals. Its results from the previous benchmark are preserved on this page for reference.