Gemini 3.8 Flash vs GPT-6.1 Terra
GPT-6.1 Terra and Gemini 3.8 Flash score 85.8% vs 79.8% averaged across five Vision Evals tasks (#2 and #8 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins 4 of the 5 tasks, with its widest lead on object detection (84.7% vs 68.1%).
- GPT-6.1 Terra is cheaper: $0.0033 vs $0.0045 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.8 Flash 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.
Gemini 3.8 Flash vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.8 Flash vs GPT-6.1 Terra Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Gemini 3.8 Flash | GPT-6.1 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 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 | $0.825 | $1.00 |
| Output $/1M | $4.13 | $5.00 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 79.8% | 85.8% |
| Avg cost / sample | $0.0045 | $0.0033 |
| Avg speed / sample | 12.76s | 10.05s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 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 | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 72.8% | 81.9% |
| by category |
|
|
| OCR (high) | 78.7% | 84.4% |
| by category |
|
|
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
Gemini 3.8 Flash vs GPT-6.1 Terra: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
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 4 of the five vision tasks and averages 85.8% (#2 of 65) against 79.8% (#8 of 65) for Gemini 3.8 Flash. 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 68.1%. 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 $0.0045. Gemini 3.8 Flash is priced at $0.83 per 1M input tokens and $4.13 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.
GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s per inference against 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.