GPT-6.1 Terra vs Qwen3.5 35B A3B
GPT-6.1 Terra averages 85.8% across five Vision Evals tasks and Qwen3.5 35B A3B 62.5% across four (#2 and #34 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins all 4 tasks both ran, with its widest lead on object detection (84.7% vs 52.9%).
- Qwen3.5 35B A3B is about half the cost: $0.0016 vs $0.0033 overall average cost per sample.
- GPT-6.1 Terra is faster: 10.1s vs 31.9s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 Terra vs Qwen3.5 35B A3B 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 Qwen3.5 35B A3B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs Qwen3.5 35B A3B Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Terra | Qwen3.5 35B A3B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Feb 2026 |
| Context Window | 1.1M | 262K |
| Parameters | undisclosed | 35B |
| License | Proprietary | Apache 2.0 |
| 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 | $1.00 |
| Vision Tasks | ||
| Captioning | 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% | 62.5% 4/5 tasks |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0033 | $0.0016 |
| Avg speed / sample | 10.05s | 31.88s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | – |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 |
| Counting (high) | 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 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 81.9% | – |
| by category |
| |
| OCR (high) | 84.4% | – |
| by category |
| |
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | – |
GPT-6.1 Terra vs Qwen3.5 35B A3B: 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.
The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.
Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Terra performed better. It scores higher on all 4 vision tasks and averages 85.8% (#2 of 65) against 62.5% (#34 of 65) for Qwen3.5 35B A3B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, GPT-6.1 Terra leads with 84.7% against 52.9%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 35B A3B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 per sample against $0.0033. 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 31.9s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.