GPT-5.6 Terra vs GPT-6 Sol
Compare GPT-5.6 Terra and GPT-6 Sol side-by-side.
Compare GPT-5.6 Terra vs GPT-6 Sol 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 GPT-6 Sol on Vision Evals
GPT-6 Sol scores higher on all six Vision Evals tasks.
The widest gap is Object Detection, where GPT-6 Sol leads 73.6% to 60.6%.
Overall, GPT-5.6 Terra averages 73.8% (#21 of 57) against 80.7% (#10 of 57) for GPT-6 Sol.
GPT-6 Sol is cheaper ($0.0065 vs $0.0088 per sample), while GPT-5.6 Terra is faster (7.7s vs 8.1s per sample).
GPT-5.6 Terra vs GPT-6 Sol Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-5.6 Terra | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.1M | 1.1M |
| Parameters | undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $12.00 | |
| 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% | 80.7% |
| Avg cost / sample | $0.0088 | $0.0065 |
| Avg speed / sample | 7.74s | 8.15s |
| By task | ||
| Object Detection (low) | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 |
| Object Detection (high) | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 |
| Counting (low) | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 |
| Counting (high) | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 |
| Identification (low) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| Identification (high) | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 |
| OCR (high) | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 |
| Data Extraction (low) | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 |
| Data Extraction (high) | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 |
| Reasoning (low) | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 |
| Reasoning (high) | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 |
GPT-5.6 Terra vs GPT-6 Sol: 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.
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all six vision tasks and averages 80.7% (#10 of 57) against 73.8% (#21 of 57) 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 Object Detection benchmark at low effort, GPT-6 Sol leads with 73.6% against 60.6%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0065 per sample against $0.0088. 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 8.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.