Grok 4.7 vs Qwen3.6 35B A3B
Compare Grok 4.7 and Qwen3.6 35B A3B side-by-side.
Compare Grok 4.7 vs Qwen3.6 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.
Models in this comparison
Grok 4.7 vs Qwen3.6 35B A3B on Vision Evals
Grok 4.7 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.6 35B A3B leads 57.0% to 40.4%.
Overall, Grok 4.7 averages 71.9% (#22 of 54) against 71.9% (#21 of 54) for Qwen3.6 35B A3B.
Qwen3.6 35B A3B is cheaper ($0.0012 vs $0.012 per sample), while Grok 4.7 is faster (23.6s vs 27.1s per sample).
Grok 4.7 vs Qwen3.6 35B A3B Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Grok 4.7 | Qwen3.6 35B A3B |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | open |
| Modality | — | multimodal |
| Release Date | Sep 2026 | Apr 2026 |
| Context Window | 500K | 262K |
| Parameters | 35B total, 3B active | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.60 | $0.150 |
| Output $/1M | $4.80 | $1.00 |
| 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 | |
| Phrase Grounding | ||
| Video Classification | ||
| 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% | 71.9% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.012 | $0.0012 |
| Avg speed / sample | 23.55s | 27.10s |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | 57.0% ±1.3, Mean of 3 runs, range 56.1 to 58.7 |
| 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 | 65.3% ±2.7, Mean of 3 runs, range 62.2 to 67.6 |
| 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 | 82.3% ±6.3, Mean of 3 runs, range 75.0 to 87.5 |
| 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 | 87.7% ±0.0, Mean of 3 runs, range 87.6 to 87.7 |
| 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 | 84.5% ±1.0, Mean of 3 runs, range 83.5 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 | 54.8% ±1.3, Mean of 3 runs, range 53.0 to 55.6 |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | – |
Grok 4.7 vs Qwen3.6 35B A3B: 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.
Qwen3.6-35B-A3B is a sparse Mixture-of-Experts (MoE) multimodal language model developed by the Qwen team at Alibaba Group. It carries 35 billion total parameters but activates only approximately 3 billion per forward pass via a learned routing mechanism, giving it the representational capacity of a large dense model at a fraction of the inference compute. The model is natively multimodal, processing images, documents, and video alongside text as a core architectural capability rather than an add-on. It supports a native context window of 262,144 tokens, extensible up to 1,010,000 tokens via YaRN. A key design feature is the unified thinking/non-thinking mode framework: users can switch between deliberate chain-of-thought reasoning and fast direct responses within a single model, and a "thinking preservation" option retains reasoning context across multi-turn agentic workflows to reduce redundant computation.
The model is specifically optimized for agentic coding tasks, including repository-level reasoning, frontend workflow generation, multi-step tool use, and MCP (Model Context Protocol) integration. On SWE-bench Verified it scores 73.4%, on Terminal-Bench 2.0 it scores 51.5%, and on MCPMark it scores 37.0%. For vision-language tasks it achieves 92.0 on RefCOCO, 89.9 on OmniDocBench 1.5, and 83.7 on VideoMMMU. The model also supports Multi-Token Prediction (MTP) for speculative decoding. All Qwen3.6 open-weight models are released under the Apache 2.0 license.