Claude Fable 5.2 vs GPT-6.1 Terra
Claude Fable 5.2 and GPT-6.1 Terra score 86.5% vs 85.8% averaged across five Vision Evals tasks (#2 and #3 of 67 models), so they are effectively tied.
- The task wins split 2-2, and the widest gap is counting, where Claude Fable 5.2 leads 86.5% vs 80.2%.
- GPT-6.1 Terra is about 1/9 of the cost: $0.0033 vs $0.031 overall average cost per sample.
- Claude Fable 5.2 is faster: 9.3s vs 10.1s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Claude Fable 5.2 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 Fable 5.2 vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Fable 5.2 vs GPT-6.1 Terra Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Claude Fable 5.2 | GPT-6.1 Terra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 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 | $8.00 | $1.00 |
| Output $/1M | $40.00 | $5.00 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 86.5% | 85.8% |
| Avg cost / sample | $0.031 | $0.0033 |
| Avg speed / sample | 9.33s | 10.05s |
| By task | ||
| Object Detection (low) | 81.6% ±0.3, Mean of 3 runs, range 81.2 to 81.9 | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | 83.3% ±0.5, Mean of 3 runs, range 82.8 to 83.7 | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 86.5% ±0.0, Mean of 3 runs, range 86.5 to 86.5 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 92.8% ±1.4, Mean of 3 runs, range 91.9 to 94.6 | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| Identification (high) | 94.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 79.5% | 81.9% |
| by category |
|
|
| OCR (high) | 84.8% | 84.4% |
| by category |
|
|
| Reasoning (low) | 88.1% ±1.3, Mean of 3 runs, range 86.8 to 89.4 | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 92.0% ±1.0, Mean of 3 runs, range 91.4 to 93.4 | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
Claude Fable 5.2 vs GPT-6.1 Terra: Overview
Claude Fable 5.2 is a proprietary multimodal model from Anthropic, released on November 9, 2026. It accepts text and images and produces text, with a one million token context window and up to 128,000 output tokens. Adaptive thinking is always enabled, with an effort setting that controls reasoning. Anthropic positions Fable for demanding reasoning and long-running technical work, including coding and analysis of visual inputs.
Fable 5.2 succeeds Fable 5.1. Anthropic reports improvements in coding, reasoning, and vision, with output generation up to 30% faster than its predecessor. Fable and Mythos 5.2 share an underlying model, with additional cybersecurity and biology safeguards applied to Fable. Model weights are not publicly released.
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, Claude Fable 5.2 performed slightly better overall. The two split the five vision tasks 2 to 2, but Claude Fable 5.2 averages 86.5% (#2 of 67) against 85.8% (#3 of 67) for GPT-6.1 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Counting benchmark at low effort, Claude Fable 5.2 leads with 86.5% against 80.2%. 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 $0.031. Claude Fable 5.2 is priced at $8.00 per 1M input tokens and $40.00 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 Fable 5.2 is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 10.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.