GPT-5.6 Terra vs Muse Spark 1.2
Compare GPT-5.6 Terra and Muse Spark 1.2 side-by-side. See how these vision models stack up in Classification, Open Prompt, Object Detection, OCR, and Image Captioning.
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Models in this comparison
GPT-5.6 Terra vs Muse Spark 1.2 on Vision Evals
Muse Spark 1.2 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.2 leads 75.1% to 60.9%.
Overall, GPT-5.6 Terra averages 73.8% (#19 of 53) against 80.5% (#10 of 53) for Muse Spark 1.2.
Muse Spark 1.2 is cheaper ($0.0072 vs $0.0088 per sample), while GPT-5.6 Terra is faster (7.7s vs 7.8s per sample).
GPT-5.6 Terra vs Muse Spark 1.2 Comparison Table
Evals updated September 5, 2026Pricing updated September 20, 2026
| Property | GPT-5.6 Terra | Muse Spark 1.2 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $12.00 | $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 | 73.8% | 80.5% |
| Avg cost / sample | $0.0088 | $0.0072 |
| Avg speed / sample | 7.74s | 7.81s |
| By task | ||
| Object Detection (low) | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 | 59.0% ±1.0, Mean of 3 runs, range 58.1 to 60.2 |
| Object Detection (high) | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 | 60.5% ±0.3, Mean of 3 runs, range 60.2 to 60.7 |
| Counting (low) | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 76.6% ±2.7, Mean of 3 runs, range 74.3 to 79.7 |
| Counting (high) | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 | 75.2% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Identification (low) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 87.5% ±0.0, Mean of 3 runs, range 87.5 to 87.5 |
| OCR (low) | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 | 93.6% ±0.6, Mean of 3 runs, range 92.9 to 94.1 |
| OCR (high) | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 | 92.9% ±0.8, Mean of 3 runs, range 91.9 to 93.6 |
| Data Extraction (low) | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 | 89.0% ±1.0, Mean of 3 runs, range 87.6 to 89.7 |
| Data Extraction (high) | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 88.3% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Reasoning (low) | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 | 75.1% ±0.3, Mean of 3 runs, range 74.8 to 75.5 |
| Reasoning (high) | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 | 75.7% ±0.3, Mean of 3 runs, range 75.5 to 76.2 |
GPT-5.6 Terra vs Muse Spark 1.2: Overview
GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.
GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.
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, Muse Spark 1.2 performed better. It scores higher on 5 of the six vision tasks and averages 80.5% (#10 of 53) against 73.8% (#19 of 53) for GPT-5.6 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Muse Spark 1.2 leads with 75.1% against 60.9%. 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 $0.0088. GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 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-5.6 Terra is faster. Across Roboflow's Vision Evals it averaged 7.7s 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 image classification and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.