GPT-6.1 Terra vs Muse Spark Open
GPT-6.1 Terra and Muse Spark Open score 85.8% vs 69.4% averaged across five Vision Evals tasks (#2 and #25 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins all 5 tasks, with its widest lead on object detection (84.7% vs 57.8%).
- GPT-6.1 Terra is about half the cost: $0.0033 vs $0.0059 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 10.1s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 Terra vs Muse Spark Open 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-6.1 Terra vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Terra | Muse Spark Open |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Oct 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $1.25 |
| Output $/1M | $5.00 | $4.25 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 85.8% | 69.4% |
| Avg cost / sample | $0.0033 | $0.0059 |
| Avg speed / sample | 10.05s | 7.37s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 81.9% | 67.6% |
| by category |
|
|
| OCR (high) | 84.4% | 68.6% |
| by category |
|
|
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
GPT-6.1 Terra vs Muse Spark Open: Overview
GPT-6.1 Terra is an OpenAI model released on October 26, 2026 for visual reasoning, computer use and writing. It accepts text and image inputs and produces text, with a 1,050,000-token context window and up to 128,000 output tokens. OpenAI lists applications including matching product photographs to catalog entries, interpreting technical drawings and operating application screens.
The API exposes six reasoning effort settings: none, low, medium, high, xhigh and max. Medium is the default. The model supports structured outputs and tool calling through the Responses API. Vision Evals reports its Low and High results for text understanding, counting, identification, reasoning and object detection, using native OpenAI API inference. Terra is a proprietary model; its weights are not released.
Muse Spark Open is an open-weight multimodal model released by Meta on October 15, 2026. Its sparse mixture-of-experts architecture has 816 billion total parameters and activates 42 billion per token. It accepts text, images, audio and video, produces text, and supports a context window of 1,048,576 tokens. Meta provides BF16 and NVFP4 checkpoints for self-hosting, alongside access through hosted inference partners.
The model is designed for coding, tool use and multi-step reasoning over long tasks. Its visual inputs support workflows involving screenshots, documents and other image content. Vision Evals measures its text understanding, counting, identification, reasoning and object detection at Low and High effort. The published weights use the custom Muse Model License, with commercial use subject to its terms and the accompanying usage policy.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Terra performed better. It scores higher on all five vision tasks and averages 85.8% (#2 of 65) against 69.4% (#25 of 65) for Muse Spark Open. 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-6.1 Terra leads with 84.7% against 57.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6.1 Terra is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.0059. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output; Muse Spark Open 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.
Muse Spark Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 10.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.