Qwen/Qwen3-Max vs gemini-2.5-flash
Side-by-side comparison of Qwen/Qwen3-Max and gemini-2.5-flash: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API.
Qwen/Qwen3-Max
Input / 1M
$1.20
Output / 1M
$6.00
Context
256K
Model ID
deepinfra/Qwen/Qwen3-Max
gemini-2.5-flash
Input / 1M
$0.30
Output / 1M
$2.50
Context
1.0M
Model ID
google/gemini-2.5-flash
Pricing & specifications
| Qwen/Qwen3-Max | gemini-2.5-flash | |
|---|---|---|
| Input price / 1M | $1.20 | $0.30 |
| Output price / 1M | $6.00 | $2.50 |
| Context window | 256K tokens | 1.0M tokens |
| Max output | N/A | 66K tokens |
| Vision input | N/A | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | N/A |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | DeepInfra | Google Gemini API |
Questions people ask
Is Qwen/Qwen3-Max better than gemini-2.5-flash?
Benchmark data is limited for one or both models. Compare pricing and capabilities in the tables above, and test both on your own workload.
Which is cheaper, Qwen/Qwen3-Max or gemini-2.5-flash?
gemini-2.5-flash is cheaper. Qwen/Qwen3-Max costs $1.20/$6.00 per 1M input/output tokens, while gemini-2.5-flash costs $0.30/$2.50.
Can I use Qwen/Qwen3-Max and gemini-2.5-flash through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "deepinfra/Qwen/Qwen3-Max" and "google/gemini-2.5-flash", no other code changes needed.
What are the context windows?
Qwen/Qwen3-Max supports up to 256K tokens of context. gemini-2.5-flash supports up to 1.0M tokens. Longer context means you can feed larger documents or codebases in a single prompt, though quality often degrades past 128K for most models.
Switch between Qwen/Qwen3-Max and gemini-2.5-flash with one line of code
Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.