GPT-5.6 Terra vs Grok 4.7
Compare GPT-5.6 Terra and Grok 4.7 side-by-side.
Compare GPT-5.6 Terra vs Grok 4.7 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-5.6 Terra vs Grok 4.7 on Vision Evals
Grok 4.7 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-5.6 Terra leads 60.6% to 40.4%.
Overall, GPT-5.6 Terra averages 73.8% (#19 of 54) against 71.9% (#22 of 54) for Grok 4.7.
GPT-5.6 Terra is both cheaper ($0.0088 vs $0.012 per sample) and faster (7.7s vs 23.6s per sample).
GPT-5.6 Terra vs Grok 4.7 Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-5.6 Terra | Grok 4.7 |
|---|---|---|
| Organization | OpenAI | SpaceXAI |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.1M | 500K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.60 |
| Output $/1M | $12.00 | $4.80 |
| 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 | 73.8% | 71.9% |
| Avg cost / sample | $0.0088 | $0.012 |
| Avg speed / sample | 7.74s | 23.55s |
| By task | ||
| Object Detection (low) | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 |
| Object Detection (high) | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 |
| Counting (low) | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 |
| Counting (high) | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 |
| Identification (low) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 | 92.6% ±0.7, Mean of 3 runs, range 92.1 to 93.4 |
| OCR (high) | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 | 93.5% ±0.3, Mean of 3 runs, range 93.1 to 93.8 |
| Data Extraction (low) | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 | 84.9% ±2.6, Mean of 3 runs, range 82.5 to 87.6 |
| Data Extraction (high) | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 87.6% ±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 | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 |
| Reasoning (high) | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 |
GPT-5.6 Terra vs Grok 4.7: 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.
Grok 4.7 is a proprietary model from SpaceXAI, released on September 21, 2026. It accepts text and images as input and returns text. It extends Grok 4.6 and is listed at the same API price.
Its Vision Evals scores are on the leaderboard. Running it in the Playground is not available yet, because the inference workflow is not ready.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on 4 of the six vision tasks and averages 71.9% (#22 of 54) against 73.8% (#19 of 54) for GPT-5.6 Terra. 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-5.6 Terra leads with 60.6% against 40.4%. 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 $0.0088 per sample against $0.012. GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 per 1M output; Grok 4.7 is priced at $1.60 per 1M input tokens and $4.80 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 23.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.