Grok 4.7 vs Qwen3.5-27B
Compare Grok 4.7 and Qwen3.5-27B side-by-side.
Compare Grok 4.7 vs Qwen3.5-27B 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.
Models in this comparison
Grok 4.7 vs Qwen3.5-27B on Vision Evals
Grok 4.7 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.5-27B leads 50.5% to 40.4%.
Overall, Grok 4.7 averages 71.9% (#22 of 54) against 70.8% (#23 of 54) for Qwen3.5-27B.
Qwen3.5-27B is cheaper ($0.0043 vs $0.012 per sample), while Grok 4.7 is faster (23.6s vs 80.4s per sample).
Grok 4.7 vs Qwen3.5-27B Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Grok 4.7 | Qwen3.5-27B |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | open |
| Modality | — | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 500K | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.60 | $0.195 |
| Output $/1M | $4.80 | $1.56 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 71.9% | 70.8% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.012 | $0.0043 |
| Avg speed / sample | 23.55s | 80.37s |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Object Detection (high) | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 | – |
| Counting (low) | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 | – |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR (low) | 92.6% ±0.7, Mean of 3 runs, range 92.1 to 93.4 | 84.7% ±3.3, Mean of 3 runs, range 80.8 to 87.3 |
| OCR (high) | 93.5% ±0.3, Mean of 3 runs, range 93.1 to 93.8 | – |
| Data Extraction (low) | 84.9% ±2.6, Mean of 3 runs, range 82.5 to 87.6 | 83.8% ±1.5, Mean of 3 runs, range 82.5 to 85.6 |
| Data Extraction (high) | 87.6% ±1.5, Mean of 3 runs, range 86.6 to 89.7 | – |
| Reasoning (low) | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | – |
Grok 4.7 vs Qwen3.5-27B: Overview
Grok 4.7 is a proprietary model from SpaceXAI, released on September 21, 2026. It accepts text and images as input and returns text. It extends Grok 4.6 and is listed at the same API price.
Its Vision Evals scores are on the leaderboard. Running it in the Playground is not available yet, because the inference workflow is not ready.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on 4 of the six vision tasks and averages 71.9% (#22 of 54) against 70.8% (#23 of 54) for Qwen3.5-27B. 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, Qwen3.5-27B leads with 50.5% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5-27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0043 per sample against $0.012. Actual costs depend on your image sizes, prompts, and output length.
Grok 4.7 is faster. Across Roboflow's Vision Evals it averaged 23.6s per inference against 80.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.