GPT-6 Astra vs Muse Spark 1.2
Compare GPT-6 Astra and Muse Spark 1.2 side-by-side. See how these vision models stack up in Object Detection, OCR, Open Prompt, Classification, and Image Captioning.
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Models in this comparison
GPT-6 Astra vs Muse Spark 1.2 on Vision Evals
GPT-6 Astra scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Astra leads 82.1% to 59.0%.
Overall, GPT-6 Astra averages 86.6% (#1 of 53) against 80.5% (#10 of 53) for Muse Spark 1.2.
Muse Spark 1.2 is cheaper ($0.0072 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 7.8s per sample).
GPT-6 Astra vs Muse Spark 1.2 Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | GPT-6 Astra | Muse Spark 1.2 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | Undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $1.25 |
| Output $/1M | $50.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 86.6% | 80.5% |
| Avg cost / sample | $0.030 | $0.0072 |
| Avg speed / sample | 6.67s | 7.81s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 59.0% ±1.0, Mean of 3 runs, range 58.1 to 60.2 |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | 60.5% ±0.3, Mean of 3 runs, range 60.2 to 60.7 |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 76.6% ±2.7, Mean of 3 runs, range 74.3 to 79.7 |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | 75.2% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 87.5% ±0.0, Mean of 3 runs, range 87.5 to 87.5 |
| OCR (low) | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 | 93.6% ±0.6, Mean of 3 runs, range 92.9 to 94.1 |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | 92.9% ±0.8, Mean of 3 runs, range 91.9 to 93.6 |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 89.0% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | 88.3% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 75.1% ±0.3, Mean of 3 runs, range 74.8 to 75.5 |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 75.7% ±0.3, Mean of 3 runs, range 75.5 to 76.2 |
GPT-6 Astra vs Muse Spark 1.2: Overview
GPT-6 Astra is a proprietary multimodal reasoning model from OpenAI that accepts text and image input and produces text output. It is positioned as the company's flagship system for long-horizon end-to-end work spanning complex reasoning, software engineering, computer use, browsing, research and document creation. The model exposes a graduated reasoning effort control with low, medium, high, xhigh and max settings, and it accepts a change to that setting partway through a conversation rather than only at request time. It launches as a single tier with no smaller mini or nano variants, carries a context window of roughly 1.05 million tokens with a maximum output of 128,000 tokens, and reports a knowledge cutoff of April 30, 2026.
OpenAI reports evaluation results across agentic, scientific and security benchmarks, including 96.0% on GPQA Diamond, 64.6% on Terminal-Bench Science, 72.6% on OSWorld 2.0, and a perfect score on ExploitBench, along with near saturation on FrontierMath Tier 4 and ARC-AGI-3. The model supports computer use, structured outputs, streaming, programmatic tool calling, multi-agent orchestration, prompt caching and persisted reasoning, and it keeps earlier context windows searchable so it can recover requirements or tool outputs from previous turns. OpenAI describes Astra as the first of its models to cross the Critical cybersecurity capability threshold under its Preparedness Framework.
Muse Spark 1.2 is a proprietary multimodal reasoning model from Meta Superintelligence Labs, released as a coding-focused update to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents and returns text, with a context window of roughly one million tokens that allows whole repositories, long documents, and extended agent trajectories to be held in a single request. The model thinks before answering, and the amount of reasoning effort it spends is configurable per request. Alongside its visual and document understanding, it supports structured output and parallel function calling, and it is designed to operate either as a planning agent that delegates work or as a subagent executing tasks in parallel.
Training for version 1.2 scaled up compute on coding tasks and widened the diversity of training environments, concentrating on long-horizon work such as whole-repository generation, large end-to-end projects, and automated research. Part of the training data was self-generated, with Muse Spark 1.1 producing coding environments and instruction-following templates and grading candidate solutions against them. The model was co-trained with the Muse Code terminal agent, incorporating rejection-sampled harness trajectories and that toolset. Meta reports 82.9 percent on Terminal-Bench 2.1, an improvement of 6.7 points over Muse Spark 1.1. Multimodal use cases documented for the family include visual-to-code generation and detailed image and video captioning.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Astra performed better. It scores higher on 3 of the six vision tasks and averages 86.6% (#1 of 53) against 80.5% (#10 of 53) for Muse Spark 1.2. 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 Astra leads with 82.1% against 59.0%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark 1.2 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0072 per sample against $0.030. GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Muse Spark 1.2 is priced at $1.25 per 1M input tokens and $4.25 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Astra is faster. Across Roboflow's Vision Evals it averaged 6.7s per inference against 7.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.