GPT-5.4 vs Muse Ember
Muse Ember: 76.6% average across five Vision Evals tasks (#16 of 67 models). We have not run GPT-5.4 through Roboflow's Vision Evals, so there is no head-to-head score.
Compare GPT-5.4 vs Muse Ember 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.
GPT-5.4 vs Muse Ember Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | GPT-5.4 | Muse Ember |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Nov 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.50 | $0.100 |
| Output $/1M | $15.00 | $0.500 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 76.6% |
| Avg cost / sample | – | $0.0009 |
| Avg speed / sample | – | 6.08s |
| By task | ||
| Object Detection (low) | – | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 |
| Object Detection (high) | – | 64.0% ±0.5, Mean of 3 runs, range 63.5 to 64.5 |
| Counting (low) | – | 79.7% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | – | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Identification (low) | – | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 90.6% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| OCR (low) | – | 69.5% |
| by category |
| |
| OCR (high) | – | 70.5% |
| by category |
| |
| Reasoning (low) | – | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 |
| Reasoning (high) | – | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 |
GPT-5.4 vs Muse Ember: Overview
GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.
Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.
Muse Ember is a proprietary multimodal reasoning model from Meta, released on November 9, 2026. It accepts text, images, video, audio, and PDF documents and returns text. The Meta Model API serves it as muse-ember-1.0, with a 1,048,576-token context window and up to 524,288 output tokens. Reasoning effort is adjustable from minimal to max.
Meta positions Ember for repeated tasks within larger agent workflows: tool calls, routing, coding subtasks, and processing collections of images and documents. It can caption or sort photos and extract fields from records while a larger Muse model handles planning. Meta recommends Spark or Wonder for harder single-shot coding and knowledge-heavy problems. Ember is available as a hosted model; its weights are not publicly released.