Gemma 4 31B vs Qwen3.5-Flash
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemma 4 31B and Qwen3.5-Flash.
Gemma 4 31B
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...
Qwen3.5-Flash
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...
Technical Specifications
| Specification | Gemma 4 31B | Qwen3.5-Flash |
|---|---|---|
| Provider | Alibaba | |
| Context Window | 262,144 tokens | 1,000,000 tokens |
| Agent Suitability | N/A | N/A |
| Time to First Token (TTFT) | N/A | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-04-02 | 2026-02-25 |
API Pricing Comparison
Input Price per Million Tokens
Gemma 4 31B
$0.12
Qwen3.5-Flash
$0.07
Output Price per Million Tokens
Gemma 4 31B
$0.35
Qwen3.5-Flash
$0.26
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Gemma 4 31B Quirks & Gotchas
No developer gotchas reported.
Qwen3.5-Flash Quirks & Gotchas
No developer gotchas reported.