qwen3.7-plus vs gemini-3.5-flash
Side-by-side comparison of qwen3.7-plus and gemini-3.5-flash: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-3.5-flash outperforms qwen3.7-plus on 6 of 7 shared benchmarks.
qwen3.7-plus
Input / 1M
$0.32
Output / 1M
$1.28
Context
1.0M
Model ID
alibaba/qwen3.7-plus
gemini-3.5-flash
Input / 1M
$1.50
Output / 1M
$9.00
Context
1.0M
Model ID
vertex/gemini-3.5-flash
Benchmark comparison
Intelligence Indexreasoning
qwen3.7-plus39.0%
gemini-3.5-flash50.2%
Coding Indexcoding
qwen3.7-plus55.9%
gemini-3.5-flash70.1%
GPQA Diamondreasoning
qwen3.7-plus90.0%
gemini-3.5-flash92.2%
Terminal-Bench Hardagentic
qwen3.7-plus47.0%
gemini-3.5-flash40.9%
τ²-Benchagentic
qwen3.7-plus93.0%
gemini-3.5-flash95.3%
SciCodecoding
qwen3.7-plus45.5%
gemini-3.5-flash53.1%
Humanity's Last Examreasoning
qwen3.7-plus33.4%
gemini-3.5-flash41.0%
Scores sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and don't capture every aspect of model quality.
Pricing & specifications
| qwen3.7-plus | gemini-3.5-flash | |
|---|---|---|
| Input price / 1M | $0.32 | $1.50 |
| Output price / 1M | $1.28 | $9.00 |
| Context window | 1.0M tokens | 1.0M tokens |
| Max output | 66K tokens | 66K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | N/A | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | Alibaba Cloud | Google LLC (Vertex AI) |
Questions people ask
Is qwen3.7-plus better than gemini-3.5-flash?
gemini-3.5-flash outperforms qwen3.7-plus on 6 of 7 shared benchmarks. See the benchmark comparison above for specifics: qwen3.7-plus and gemini-3.5-flash have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, qwen3.7-plus or gemini-3.5-flash?
qwen3.7-plus is cheaper. qwen3.7-plus costs $0.32/$1.28 per 1M input/output tokens, while gemini-3.5-flash costs $1.50/$9.00.
Can I use qwen3.7-plus and gemini-3.5-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 "alibaba/qwen3.7-plus" and "vertex/gemini-3.5-flash", no other code changes needed.
What are the context windows?
qwen3.7-plus supports up to 1.0M tokens of context. gemini-3.5-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 qwen3.7-plus and gemini-3.5-flash with one line of code
Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.