Grok 4.7 vs Qwen3.8 Flash
Compare Grok 4.7 and Qwen3.8 Flash side-by-side.
Compare Grok 4.7 vs Qwen3.8 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.
Models in this comparison
Grok 4.7 vs Qwen3.8 Flash on Vision Evals
Grok 4.7 scores higher on 3 of the six Vision Evals tasks.
The widest gap is Reasoning, where Grok 4.7 leads 64.2% to 35.1%.
Overall, Grok 4.7 averages 71.9% (#22 of 54) against 68.8% (#27 of 54) for Qwen3.8 Flash.
Qwen3.8 Flash is both cheaper ($0.0004 vs $0.012 per sample) and faster (6.5s vs 23.6s per sample).
Grok 4.7 vs Qwen3.8 Flash Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Grok 4.7 | Qwen3.8 Flash |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | closed |
| Modality | — | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 500K | 1.0M |
| Parameters | 125B total, 6B active (+51B N-gram embeddings) | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $1.60 | $0.150 |
| Output $/1M | $4.80 | $0.470 |
| 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% | 68.8% |
| Avg cost / sample | $0.012 | $0.0004 |
| Avg speed / sample | 23.55s | 6.48s |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | 59.8% ±1.1, Mean of 3 runs, range 58.5 to 60.8 |
| Object Detection (high) | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 | 67.0% ±1.6, Mean of 3 runs, range 65.3 to 68.5 |
| Counting (low) | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 | 56.3% ±2.7, Mean of 3 runs, range 54.0 to 59.5 |
| Counting (high) | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 | 68.0% ±0.7, Mean of 3 runs, range 67.6 to 68.9 |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 92.6% ±0.7, Mean of 3 runs, range 92.1 to 93.4 | 88.0% ±1.0, Mean of 3 runs, range 86.8 to 88.9 |
| OCR (high) | 93.5% ±0.3, Mean of 3 runs, range 93.1 to 93.8 | 91.3% ±0.5, Mean of 3 runs, range 90.8 to 91.9 |
| Data Extraction (low) | 84.9% ±2.6, Mean of 3 runs, range 82.5 to 87.6 | 84.9% ±1.5, Mean of 3 runs, range 83.5 to 86.6 |
| Data Extraction (high) | 87.6% ±1.5, Mean of 3 runs, range 86.6 to 89.7 | 84.5% ±1.0, Mean of 3 runs, range 83.5 to 85.6 |
| Reasoning (low) | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 | 35.1% ±3.3, Mean of 3 runs, range 31.1 to 37.8 |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | 69.5% ±0.7, Mean of 3 runs, range 68.9 to 70.2 |
Grok 4.7 vs Qwen3.8 Flash: 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.
Qwen3.8-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. Reported scores include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 84.5 on AndroidWorld and 95.7 on MathVision.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on 3 of the six vision tasks and averages 71.9% (#22 of 54) against 68.8% (#27 of 54) for Qwen3.8 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, Grok 4.7 leads with 64.2% against 35.1%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.012. Grok 4.7 is priced at $1.60 per 1M input tokens and $4.80 per 1M output; Qwen3.8 Flash is priced at $0.15 per 1M input tokens and $0.47 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.8 Flash is faster. Across Roboflow's Vision Evals it averaged 6.5s per inference against 23.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.