GPT-5.6 Terra vs Muse Spark 1.1
Compare GPT-5.6 Terra and Muse Spark 1.1 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 1.1 on Vision Evals
Muse Spark 1.1 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.1 leads 74.8% to 59.6%.
Overall, GPT-5.6 Terra averages 72.4% (#12 of 25) against 79.2% (#6 of 25) for Muse Spark 1.1.
GPT-5.6 Terra is both cheaper ($0.0044 vs $0.0069 per sample) and faster (7.2s vs 11.4s per sample).
GPT-5.6 Terra vs Muse Spark 1.1 Comparison Table
Evals updated August 6, 2026Pricing updated August 11, 2026
| Property | GPT-5.6 Terra | Muse Spark 1.1 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Jul 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $1.25 |
| Output $/1M | $6.00 | $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 | 72.4% | 79.2% |
| Avg cost / sample | $0.0044 | $0.0069 |
| Avg speed / sample | 7.15s | 11.40s |
| By task | ||
| Object Detection | 60.7% $0.0070 | 58.2% $0.010 |
| Counting | 67.6% $0.0030 | 75.7% $0.0043 |
| Identification | 78.1% $0.0020 | 87.5% $0.0032 |
| OCR | 88.8% $0.0065 | 92.5% $0.0063 |
| Data Extraction | 79.4% $0.0018 | 86.6% $0.0031 |
| Reasoning (low) | 59.6% $0.0025 | 74.8% $0.0065 |
| Reasoning (high) | 64.2% $0.0033 | 76.2% $0.013 |
GPT-5.6 Terra vs Muse Spark 1.1: 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 1.1 is a natively multimodal reasoning model from Meta Superintelligence Labs, released on July 9, 2026, as a significant upgrade to the original Muse Spark. The model accepts text, image, video, PDF, and audio as input and produces text output. It operates with a 1-million-token context window (1,048,576 tokens per the Meta Model API documentation) and is designed specifically for agentic tasks that require planning, tool use, computer use, and multi-agent orchestration. The model runs in a "Thinking" mode, where adjustable reasoning effort is applied before generating a response. It can function both as a main agent gathering context, forming plans, and delegating to parallel subagents and as a subagent that adheres to assigned tasks and escalates when needed. It is trained to decide autonomously when to write automation scripts versus interact directly with a user interface.
Muse Spark 1.1 supports a range of multimodal capabilities including visual perception, image and video captioning, visual-to-code generation, and document analysis. The model was evaluated under Meta's Advanced AI Scaling Framework across frontier risk categories including chemical and biological threats, cybersecurity, and loss-of-control scenarios. Parameter count, architecture details, and training data composition are not publicly disclosed. The model is proprietary and closed-weight, accessible to consumers through the Meta AI app and to developers via the Meta Model API, which launched in public preview alongside this release.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.1 performed better. It scores higher on 5 of the six vision tasks and averages 79.2% (#6 of 25) against 72.4% (#12 of 25) 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 1.1 leads with 74.8% against 59.6%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0044 per sample against $0.0069. GPT-5.6 Terra is priced at $1.00 per 1M input tokens and $6.00 per 1M output; Muse Spark 1.1 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 7.2s per inference against 11.4s. 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.