GPT-6.1 Terra vs MiMo V2.6 Flash
GPT-6.1 Terra and MiMo V2.6 Flash score 85.8% vs 49.2% averaged across five Vision Evals tasks (#2 and #57 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins all 5 tasks, with its widest lead on reasoning (85.4% vs 33.1%).
- MiMo V2.6 Flash is under a tenth of the cost: $0.0003 vs $0.0033 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 16.3s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 Terra vs MiMo V2.6 Flash 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.
GPT-6.1 Terra vs MiMo V2.6 Flash on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs MiMo V2.6 Flash Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Terra | MiMo V2.6 Flash |
|---|---|---|
| Organization | OpenAI | Xiaomi |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Sep 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | 309B total, 15B active |
| License | Proprietary | MIT |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $0.140 |
| Output $/1M | $5.00 | $0.280 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 85.8% | 49.2% |
| Avg cost / sample | $0.0033 | $0.0003 |
| Avg speed / sample | 10.05s | 16.34s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 37.8% ±1.1, Mean of 3 runs, range 36.4 to 38.7 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | 45.0% ±2.5, Mean of 3 runs, range 42.2 to 47.1 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 49.5% ±8.1, Mean of 3 runs, range 41.9 to 58.1 |
| Counting (high) | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 | 64.9% ±1.4, Mean of 3 runs, range 63.5 to 66.2 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 76.0% ±3.1, Mean of 3 runs, range 71.9 to 78.1 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 82.3% ±4.7, Mean of 3 runs, range 78.1 to 87.5 |
| OCR (low) | 81.9% | 49.7% |
| by category |
|
|
| OCR (high) | 84.4% | 50.4% |
| by category |
|
|
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 33.1% ±2.0, Mean of 3 runs, range 31.1 to 35.1 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | 58.5% ±1.3, Mean of 3 runs, range 57.0 to 59.6 |
GPT-6.1 Terra vs MiMo V2.6 Flash: Overview
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.
MiMo-V2.6-Flash is the efficiency-oriented checkpoint of Xiaomi's MiMo-V2.6 series, a natively omnimodal foundation model that accepts text, image, video, and audio in a single model and supports a one million token context window. The language backbone is a sparse mixture-of-experts transformer with roughly 309 billion total parameters and 15 billion activated per token, organized as 48 layers with 256 routed experts and top-8 routing. It uses a hybrid attention scheme that interleaves sliding window attention with global attention layers to cut key-value cache cost on long sequences, and pairs the backbone with a vision encoder, an audio encoder, and an audio tokenizer, plus a multi-token prediction module and a draft model for faster decoding.
Training emphasizes large scale reinforcement learning on verifiable, long-horizon tasks, with RL compute, environment diversity, and grader compute scaled together in a single mixed run.
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 49.2% (#57 of 65) for MiMo V2.6 Flash. 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, GPT-6.1 Terra leads with 85.4% against 33.1%. This is the widest gap between the two models across the benchmark's tasks.
MiMo V2.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0003 per sample against $0.0033. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output; MiMo V2.6 Flash is priced at $0.14 per 1M input tokens and $0.28 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 16.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.