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GPT-5.4 Mini vs LLaVA-1.5

Compare GPT-5.4 Mini and LLaVA-1.5 side-by-side.

Compare GPT-5.4 Mini vs LLaVA-1.5 live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

GPT-5.4 Mini vs LLaVA-1.5: Overview

GPT-5.4 Mini

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.

LLaVA-1.5

LLaVA-1.5 is an open-source large multimodal model released in October 2023 by researchers at the University of Wisconsin-Madison and Microsoft Research. It builds on the original LLaVA architecture by introducing targeted refinements: switching the vision encoder to CLIP-ViT-L at 336-pixel resolution, replacing the projection layer with a two-layer MLP, and adding academic-task-oriented visual question answering data with response formatting prompts during training. These modifications achieve state-of-the-art performance across 11 benchmarks at release, with training completing in approximately one day on a single 8-A100 node.

The model accepts an image paired with a text prompt and generates natural language responses, supporting visual question answering, image captioning, and open-ended visual conversation. LLaVA-1.5 is available in 7B and 13B parameter variants built on the Vicuna language model, and is distributed under the Llama 2 Community License due to its Llama-2-based foundation. The original LLaVA paper was presented as an oral at NeurIPS 2023. Subsequent releases in the series (LLaVA-NeXT (LLaVA-1.6), LLaVA-NeXT-Video, and LLaVA-OneVision) are separate models with their own release pages and build on this foundation with expanded OCR, video, and multi-image capabilities.

GPT-5.4 Mini vs LLaVA-1.5 Comparison Table

PropertyGPT-5.4 MiniLLaVA-1.5
OrganizationOpenAIMicrosoft
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMar 2026Oct 2023
Context Window400K
Parameters7B, 13B
LicenseProprietaryCustom
Pricing per 1M tokens
Input $/1M$0.750
Output $/1M$4.50
Vision Tasks
Vision Language
Visual Question AnsweringDemo
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
77.61%
Avg Response Time5.80s
Median input tokensincl. image tokens1.4K
Median output tokens104
Est. cost / taskon this benchmark$0.0015
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
40%(4/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
84.2%(16/19)
OCR
Overall Score
77.29%
Avg Response Time3.24s
Median input tokensincl. image tokens105
Median output tokens126
Est. cost / taskon this benchmark$0.0006
Focused Scene OCR
75.8%(75/99)
Handwritten Math
40%(4/10)
License Plate Recognition
86.7%(26/30)
Text Recognition
73.3%(22/30)
VQA & Extraction
83.3%(50/60)

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