Roboflow
OpenAI

OpenAI: GPT-5.4 Mini

GPT-5.4 Mini Overview

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

GPT-5.4 Mini Interactive Demo

GPT-5.4 Mini Details & Performance

Details

Resources

Vision Tasks

Vision LanguageObject DetectionClassificationOCRVisual Question AnsweringCaptioning

Features

Foundation VisionLLMs with Vision CapabilitiesMultimodal Vision

Usage

Past 30 Days

Performance

Avg. Latency

Arena Rankings

GPT-5.4 Mini Vision Evals

Vision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across six vision tasks, and answers are scored against ground truth.

Evals updated July 10, 2026Pricing updated July 21, 2026

Overall score#14 of 16
65.3%
Avg cost / sample#5 of 16
$0.0026
Avg speed / sample#4 of 16
4.5s
Avg tokens / sample
1.8K

Strengths and weaknesses

GPT-5.4 Mini averages 65.3% across the six Vision Evals tasks, ranking #14 of 16 models overall.

Its weakest relative showing is Object Detection, ranking #16 of 16 at 3.9%.

At $0.0026 per sample it is the 5th cheapest of the 16 benchmarked models, and its average inference time of 4.5s per sample makes it the 4th fastest.

Performance profile

Field medianGPT-5.4 Mini

Field medians: Object Detection 41.5%, Counting 62.2%, Identification 84.4%, OCR 89.1%, Data Extraction 85.6%, Reasoning 76.1%.

Results by task

TaskScoreField (0 to 100)RankCost / sampleSpeed
Object Detection
3.9%
#16 of 16$0.00325.3s
Counting
60.8%
#9 of 16$0.00194.0s
Identification
78.1%
#12 of 16$0.00132.7s
OCR
88.1%
#14 of 16$0.00426.2s
Data Extraction
82.5%
#11 of 16$0.00142.8s
Reasoning
78.3%
#7 of 16$0.00164.5s

Price vs. performance

Score vs. cost

Overall benchmark score against estimated cost per sample. Upper-left is the sweet spot: high quality at low cost.

16 models on the current benchmark · scores and efficiency pooled across all six tasks · GPT-5.4 mini highlighted

GPT-5.4 Mini scores from a single evaluation run · Methodology

View all Vision Evals →

GPT-5.4 Mini Pricing

GPT-5.4 Mini costs $0.750 per 1M input tokens and $4.50 per 1M output tokens.

Input$0.750 / 1M tokens
Output$4.50 / 1M tokens
Cached input$0.075 / 1M tokens

Pricing updated Jul 21, 2026

Alternatives to GPT-5.4 Mini

Other models worth comparing for similar use cases.

Anthropic
Claude Sonnet 5
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Google
Gemini 3.5 Flash
Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.
Qwen
Qwen3.6 35B A3B
Qwen3.6-35B-A3B is a sparse Mixture-of-Experts (MoE) multimodal language model developed by the Qwen team at Alibaba Group. It carries 35 billion total parameters but activates only approximately 3 billion per forward pass via a learned routing mechanism, giving it the representational capacity of a large dense model at a fraction of the inference compute. The model is natively multimodal, processing images, documents, and video alongside text as a core architectural capability rather than an add-on. It supports a native context window of 262,144 tokens, extensible up to 1,010,000 tokens via YaRN. A key design feature is the unified thinking/non-thinking mode framework: users can switch between deliberate chain-of-thought reasoning and fast direct responses within a single model, and a "thinking preservation" option retains reasoning context across multi-turn agentic workflows to reduce redundant computation.The model is specifically optimized for agentic coding tasks, including repository-level reasoning, frontend workflow generation, multi-step tool use, and MCP (Model Context Protocol) integration. On SWE-bench Verified it scores 73.4%, on Terminal-Bench 2.0 it scores 51.5%, and on MCPMark it scores 37.0%. For vision-language tasks it achieves 92.0 on RefCOCO, 89.9 on OmniDocBench 1.5, and 83.7 on VideoMMMU. The model also supports Multi-Token Prediction (MTP) for speculative decoding. All Qwen3.6 open-weight models are released under the Apache 2.0 license.
Anthropic
Claude Sonnet 4.5
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.

Other OpenAI GPT Mini models

Other versions in the same family as GPT-5.4 Mini.

GPT-5.4 Mini License

Proprietary

License terms and commercial-use guidance for GPT-5.4 Mini.

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-5.4 Mini Vision

Yes. GPT-5.4 Mini accepts image input and handles OCR, data extraction, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Reasoning at 78.3% (#7 of 16). You can test it on your own image in the demo above.

Yes. its transcriptions match the ground truth 88.1% on average (#14 of 16) on Vision Evals OCR. Pulling specific fields out of documents (data extraction) scores 82.5%.

Not its strength. On Vision Evals, GPT-5.4 Mini scores 3.9% mAP@50 on object detection (#16 of 16) and 60.8% exact-match accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.

On our benchmark's task mix, GPT-5.4 Mini averages $0.0026 per sample at $0.75 per 1M input and $4.50 per 1M output tokens (#5 of 16 on cost), with an average speed of 4.5s per sample across the benchmark. Actual cost depends on your images and prompts.

On the overall Vision Evals ranking, GPT-5.4 Mini sits #14 of 16 at 65.3%, just behind Claude Sonnet 5 (65.6%) and just ahead of Claude Opus 4.8 (64.8%). See the full side-by-side: GPT-5.4 Mini vs Claude Sonnet 5.