Gemini 3.5 Flash vs GPT-6.1 Terra
GPT-6.1 Terra and Gemini 3.5 Flash score 85.8% vs 81.8% averaged across five Vision Evals tasks (#2 and #5 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins 3 of the 5 tasks, with its widest lead on object detection (84.7% vs 70.6%).
- GPT-6.1 Terra is about a quarter of the cost: $0.0033 vs $0.012 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 11.5s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare Gemini 3.5 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 3.5 Flash vs GPT-6.1 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.5 Flash vs GPT-6.1 Terra Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Gemini 3.5 Flash | GPT-6.1 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 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 | $1.50 | $1.00 |
| Output $/1M | $9.00 | $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 | 81.8% | 85.8% |
| Avg cost / sample | $0.012 | $0.0033 |
| Avg speed / sample | 11.52s | 10.05s |
| By task | ||
| Object Detection (low) | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 |
| Object Detection (high) | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 |
| Counting (low) | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 |
| Identification (low) | 99.0% ±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) | 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 |
| OCR (low) | 76.7% | 81.9% |
| by category |
|
|
| OCR (high) | 75.5% | 84.4% |
| by category |
|
|
| Reasoning (low) | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 |
| Reasoning (high) | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 |
Gemini 3.5 Flash vs GPT-6.1 Terra: Overview
Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.
Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.
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 3 of the five vision tasks and averages 85.8% (#2 of 65) against 81.8% (#5 of 65) for Gemini 3.5 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 70.6%. 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 $0.012. Gemini 3.5 Flash is priced at $1.50 per 1M input tokens and $9.00 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.
GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s per inference against 11.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.