Google's fourth-generation open dense model with native multimodal understanding.
Compact vision-language model with always-on reasoning for visual tasks.
No shared demos for these models yet.
No captured outputs for these models yet.
Both Gemma 4 31B and Qwen3 VL 8B Thinking are capable AI models. The best choice depends on your specific use case: consider pricing, context window, speed, and which capabilities (vision, reasoning, audio) you need.
Gemma 4 31B has a lower blended cost. Gemma 4 31B: $0.12 input / $0.37 output. Qwen3 VL 8B Thinking: $0.12 input / $1.36 output.
Gemma 4 31B has a larger context window: Gemma 4 31B supports 262K tokens vs Qwen3 VL 8B Thinking at 131K tokens.
Yes, both Gemma 4 31B and Qwen3 VL 8B Thinking support vision/image input.
Gemma 4 31B and Qwen3 VL 8B Thinking have similar overall capabilities. The main differences lie in pricing, context window size, and provider-specific strengths. Use this comparison page to review all metrics side by side.
If cost is your priority, choose the cheaper option. If you need the highest intelligence for complex tasks, pick the higher-scoring model. For long documents or codebases, choose the larger context window. You can try both Gemma 4 31B and Qwen3 VL 8B Thinking for free on idapt.app to see which performs better for your specific needs.