qwen3.7-plus vs kimi-k2
Side-by-side comparison of qwen3.7-plus and kimi-k2: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. qwen3.7-plus outperforms kimi-k2 on 6 of 6 shared benchmarks.
qwen3.7-plus
Input / 1M
$0.32
Output / 1M
$1.28
Context
1.0M
Model ID
alibaba/qwen3.7-plus
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
Benchmark comparison
Intelligence Indexreasoning
qwen3.7-plus53.3%
kimi-k240.9%
Coding Indexcoding
qwen3.7-plus46.5%
kimi-k234.8%
Math Indexmath
qwen3.7-plusN/A
kimi-k294.7%
GPQA Diamondreasoning
qwen3.7-plus90.0%
kimi-k283.8%
AIME 2025math
qwen3.7-plusN/A
kimi-k294.7%
LiveCodeBenchcoding
qwen3.7-plusN/A
kimi-k285.3%
Terminal-Bench Hardagentic
qwen3.7-plus47.0%
kimi-k231.1%
τ²-Benchagentic
qwen3.7-plus93.0%
kimi-k293.0%
SciCodecoding
qwen3.7-plus45.5%
kimi-k242.4%
MMLU Proknowledge
qwen3.7-plusN/A
kimi-k284.8%
Humanity's Last Examreasoning
qwen3.7-plus33.4%
kimi-k222.3%
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 | kimi-k2 | |
|---|---|---|
| Input price / 1M | $0.32 | $0.60 |
| Output price / 1M | $1.28 | $2.50 |
| Context window | 1.0M tokens | 262K tokens |
| Max output | 66K tokens | 262K 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 kimi-k2?
qwen3.7-plus outperforms kimi-k2 on 6 of 6 shared benchmarks. See the benchmark comparison above for specifics: qwen3.7-plus and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, qwen3.7-plus or kimi-k2?
qwen3.7-plus is cheaper. qwen3.7-plus costs $0.32/$1.28 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50.
Can I use qwen3.7-plus and kimi-k2 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/kimi-k2", no other code changes needed.
What are the context windows?
qwen3.7-plus supports up to 1.0M tokens of context. kimi-k2 supports up to 262K 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 kimi-k2 with one line of code
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