GPT-6 Sol vs Muse Spark 1.3
Compare GPT-6 Sol and Muse Spark 1.3 side-by-side.
Compare GPT-6 Sol vs Muse Spark 1.3 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-6 Sol vs Muse Spark 1.3 on Vision Evals
GPT-6 Sol scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Sol leads 73.6% to 58.6%.
Overall, GPT-6 Sol averages 80.7% (#10 of 57) against 79.8% (#13 of 57) for Muse Spark 1.3.
GPT-6 Sol is both cheaper ($0.0065 vs $0.0075 per sample) and faster (8.1s vs 23.1s per sample).
GPT-6 Sol vs Muse Spark 1.3 Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-6 Sol | Muse Spark 1.3 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | |
| Output $/1M | $4.25 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 80.7% | 79.8% |
| Avg cost / sample | $0.0065 | $0.0075 |
| Avg speed / sample | 8.15s | 23.14s |
| By task | ||
| Object Detection (low) | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 |
| OCR (high) | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 |
| Data Extraction (low) | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Data Extraction (high) | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Reasoning (low) | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
GPT-6 Sol vs Muse Spark 1.3: Overview
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed slightly better overall. The two split the six vision tasks 3 to 3, but GPT-6 Sol averages 80.7% (#10 of 57) against 79.8% (#13 of 57) for Muse Spark 1.3. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, GPT-6 Sol leads with 73.6% against 58.6%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0065 per sample against $0.0075. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 8.1s per inference against 23.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.