Claude Opus 5 vs GPT-5.6 Terra
Compare Claude Opus 5 and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Claude Opus 5 vs GPT-5.6 Terra on Vision Evals
Claude Opus 5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Claude Opus 5 leads 71.5% to 60.9%.
Overall, Claude Opus 5 averages 78.3% (#14 of 53) against 73.8% (#19 of 53) for GPT-5.6 Terra.
GPT-5.6 Terra is cheaper ($0.0088 vs $0.017 per sample), while Claude Opus 5 is faster (7.4s vs 7.7s per sample).
Claude Opus 5 vs GPT-5.6 Terra Comparison Table
Evals updated September 5, 2026Pricing updated September 8, 2026
| Property | Claude Opus 5 | GPT-5.6 Terra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.00 |
| Output $/1M | $25.00 | $12.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Image Tagging | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 78.3% | 73.8% |
| Avg cost / sample | $0.017 | $0.0088 |
| Avg speed / sample | 7.38s | 7.74s |
| By task | ||
| Object Detection (low) | 54.4% | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 |
| Object Detection (high) | – | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 |
| Counting (low) | 70.3% | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Counting (high) | – | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 |
| Identification (low) | 90.6% | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| Identification (high) | – | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 93.2% | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 |
| OCR (high) | – | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 |
| Data Extraction (low) | 89.7% | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 |
| Data Extraction (high) | – | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 71.5% | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 |
| Reasoning (high) | 74.2% | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 |
Claude Opus 5 vs GPT-5.6 Terra: Overview
Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at roughly half the cost, priced at $5 per million input tokens and $25 per million output tokens. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.
Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Opus 5 performed better. It scores higher on 5 of the six vision tasks and averages 78.3% (#14 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.
Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Opus 5 leads with 71.5% against 60.9%. 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 $0.017. Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5 is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 7.7s. 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 captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.