GPT-6.1 Terra vs Qwen3.8 Flash
GPT-6.1 Terra and Qwen3.8 Flash score 85.8% vs 59.7% averaged across five Vision Evals tasks (#2 and #37 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 35.1%).
- Qwen3.8 Flash is about 1/8 of the cost: $0.0004 vs $0.0033 overall average cost per sample.
- Qwen3.8 Flash is faster: 7.3s vs 10.1s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6.1 Terra vs Qwen3.8 Flash on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs Qwen3.8 Flash Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | GPT-6.1 Terra | Qwen3.8 Flash |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Aug 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | 125B total, 6B active (+51B N-gram embeddings) |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $0.150 |
| Output $/1M | $5.00 | $0.470 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 85.8% | 59.7% |
| Avg cost / sample | $0.0033 | $0.0004 |
| Avg speed / sample | 10.05s | 7.34s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 59.8% ±1.1, Mean of 3 runs, range 58.5 to 60.8 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | 67.0% ±1.6, Mean of 3 runs, range 65.3 to 68.5 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 56.3% ±2.7, Mean of 3 runs, range 54.0 to 59.5 |
| Counting (high) | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 | 68.0% ±0.7, Mean of 3 runs, range 67.6 to 68.9 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 81.9% | 58.9% |
| by category |
|
|
| OCR (high) | 84.4% | 62.9% |
| by category |
|
|
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 35.1% ±3.3, Mean of 3 runs, range 31.1 to 37.8 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | 69.5% ±0.7, Mean of 3 runs, range 68.9 to 70.2 |
GPT-6.1 Terra vs Qwen3.8 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.
Qwen3.8-Flash is a multimodal mixture-of-experts model from the Qwen team at Alibaba, and the production counterpart of the open-weight Qwen3.8-Flash-Next preview that introduces the architecture intended for the Qwen4 family. The main model carries 125 billion parameters alongside a separate 51 billion parameter N-gram embedding table, while activating roughly 6 billion parameters per token. It accepts interleaved image and text input and returns text, handling 262,144 tokens of context natively with extension to 1,000,000 tokens using YaRN. The production configuration runs with the 1M context window by default and adds built-in tool support.
Four architectural changes separate it from earlier Qwen releases: hybrid attention that pairs Gated DeltaNet for history compression with Qwen Sparse Attention, which uses a lightweight indexer to select micro-blocks of context; a Gated Residual scheme; N-gram embeddings; and training with the Muon optimizer, refined around orthogonalization accuracy and the division of parameters between Muon and AdamW. Qwen reports training cost around one ninth that of Qwen3.7-Plus, with QSA attention kernels measured up to 7.6 times faster in prefill and 4.9 times faster in decode at 1M-token context.