Claude Opus 5 vs GPT-6.1 Terra
GPT-6.1 Terra and Claude Opus 5 score 85.8% vs 70.9% averaged across five Vision Evals tasks (#2 and #21 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 54.4%).
- GPT-6.1 Terra is about 1/6 of the cost: $0.0033 vs $0.020 overall average cost per sample.
- Claude Opus 5 is faster: 8.2s vs 10.1s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Opus 5 vs GPT-6.1 Terra 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.
Claude Opus 5 vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5 vs GPT-6.1 Terra Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Claude Opus 5 | GPT-6.1 Terra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Oct 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $1.00 |
| Output $/1M | $25.00 | $5.00 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Image Tagging | Not listed | Supported |
Both support 4 more tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 70.9% | 85.8% |
| Avg cost / sample | $0.020 | $0.0033 |
| Avg speed / sample | 8.21s | 10.05s |
| By task | ||
| Object Detection (low) | 54.4% | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | – | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 70.3% | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 90.6% | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 68.0% | 81.9% |
| by category |
|
|
| OCR (high) | 65.8% | 84.4% |
| by category |
|
|
| Reasoning (low) | 71.5% | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 74.2% | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
Claude Opus 5 vs GPT-6.1 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 a lower cost. 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-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.
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 70.9% (#21 of 65) for Claude Opus 5. 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-6.1 Terra leads with 84.7% against 54.4%. 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 $0.020. Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 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 8.2s per inference against 10.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.