Grok 4.20 vs Qwen3.5-397B-A17B

On shared benchmark coverage, SWE-bench Verified has Grok 4.20 at 76.7 and Qwen3.5-397B-A17B at 76.2, with Grok 4.20 ahead by 0.5 points; Google-Proof Q&A has Grok 4.20 at 88 and Qwen3.5-397B-A17B at 89.3, with Qwen3.5-397B-A17B ahead by 1.3 points; Humanity's Last Exam has Grok 4.20 at 32.2 and Qwen3.5-397B-A17B at 28.7, with Grok 4.20 ahead by 3.5 points. The largest visible gap is 3.5 points on Humanity's Last Exam, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint is close: both models cover vision, multimodal input, reasoning mode, function calling, and tool use. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

For cost, Grok 4.20 lists $1.25/1M input and $2.50/1M output tokens on the cheapest tracked provider, while Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-397B-A17B lower by about $0.65 per million blended tokens. Availability is 2 providers versus 4, so concentration risk also matters.

Choose Grok 4.20 when long-context analysis and larger context windows are central to the workload. Choose Qwen3.5-397B-A17B when vision-heavy evaluation, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.