Claude Opus 5.5 vs GPT-5.6 Terra
Compare Claude Opus 5.5 and GPT-5.6 Terra side-by-side.
Compare Claude Opus 5.5 vs GPT-5.6 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.
Models in this comparison
Claude Opus 5.5 vs GPT-5.6 Terra on Vision Evals
Claude Opus 5.5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Claude Opus 5.5 leads 83.0% to 60.9%.
Overall, Claude Opus 5.5 averages 85.5% (#3 of 57) against 73.8% (#21 of 57) for GPT-5.6 Terra.
GPT-5.6 Terra is both cheaper ($0.0088 vs $0.014 per sample) and faster (7.7s vs 12.8s per sample).
Claude Opus 5.5 vs GPT-5.6 Terra Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Opus 5.5 | GPT-5.6 Terra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| 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 | 85.5% | 73.8% |
| Avg cost / sample | $0.014 | $0.0088 |
| Avg speed / sample | 12.76s | 7.74s |
| By task | ||
| Object Detection (low) | 74.4% ±0.5, Mean of 3 runs, range 73.9 to 74.8 | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 |
| Object Detection (high) | 76.8% ±1.2, Mean of 3 runs, range 75.4 to 77.8 | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 87.8% ±0.6, Mean of 3 runs, range 87.0 to 88.2 | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 |
| OCR (high) | 87.2% ±0.6, Mean of 3 runs, range 86.5 to 87.8 | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 |
| Data Extraction (low) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 |
Claude Opus 5.5 vs GPT-5.6 Terra: Overview
Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.
On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.
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 5.5 performed better. It scores higher on 5 of the six vision tasks and averages 85.5% (#3 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.
Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Opus 5.5 leads with 83.0% 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 $0.014. 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 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.