GPT-5.6 Terra vs Qwen3.8 27B
Compare GPT-5.6 Terra and Qwen3.8 27B side-by-side. See how these vision models stack up in Classification, Open Prompt, Object Detection, OCR, and Image Captioning.
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Models in this comparison
GPT-5.6 Terra vs Qwen3.8 27B on Vision Evals
GPT-5.6 Terra scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.8 27B leads 65.7% to 60.6%.
Overall, GPT-5.6 Terra averages 73.8% (#24 of 61) against 74.7% (#22 of 61) for Qwen3.8 27B.
Qwen3.8 27B is cheaper ($0.0009 vs $0.0088 per sample), while GPT-5.6 Terra is faster (7.7s vs 18.0s per sample).
GPT-5.6 Terra vs Qwen3.8 27B Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | GPT-5.6 Terra | Qwen3.8 27B |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 1.1M | 262K |
| Parameters | 27.78B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.025 |
| Output $/1M | $12.00 | $4.35 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 73.8% | 74.7% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0088 | $0.0009 |
| Avg speed / sample | 7.74s | 17.99s |
| By task | ||
| Object Detection (low) | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 | 65.7% ±1.0, Mean of 3 runs, range 64.6 to 66.5 |
| Object Detection (high) | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 | 66.1% ±1.4, Mean of 3 runs, range 64.9 to 67.8 |
| Counting (low) | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 | 64.9% ±4.1, Mean of 3 runs, range 60.8 to 68.9 |
| Counting (high) | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 | 68.0% ±2.0, Mean of 3 runs, range 66.2 to 70.3 |
| Identification (low) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 85.4% ±4.7, Mean of 3 runs, range 81.3 to 90.6 |
| Identification (high) | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 | 92.2% ±1.2, Mean of 3 runs, range 91.1 to 93.4 |
| OCR (high) | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 | 91.5% ±1.4, Mean of 3 runs, range 90.1 to 92.9 |
| Data Extraction (low) | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 | 78.0% ±1.0, Mean of 3 runs, range 77.3 to 79.4 |
| Data Extraction (high) | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | 80.8% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 | 62.0% ±2.0, Mean of 3 runs, range 60.3 to 64.2 |
| Reasoning (high) | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 | 66.0% ±0.7, Mean of 3 runs, range 65.6 to 66.9 |
GPT-5.6 Terra vs Qwen3.8 27B: Overview
GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.
GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 27B performed slightly better overall. The two split the six vision tasks 3 to 3, but Qwen3.8 27B averages 74.7% (#22 of 61) against 73.8% (#24 of 61) for GPT-5.6 Terra. 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, Qwen3.8 27B leads with 65.7% against 60.6%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.0088. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Terra is faster. Across Roboflow's Vision Evals it averaged 7.7s per inference against 18.0s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image classification and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.