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qwen3.7-plus vs kimi-k2.5

Side-by-side comparison of qwen3.7-plus and kimi-k2.5: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. qwen3.7-plus outperforms kimi-k2.5 on 5 of 6 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
qwen3.7-plus31.9%
kimi-k2.527.6%
Coding Indexcoding
qwen3.7-plus55.9%
kimi-k2.546.8%
GPQA Diamondreasoning
qwen3.7-plus90.0%
kimi-k2.587.9%
Terminal-Bench Hardagentic
qwen3.7-plus47.0%
kimi-k2.534.8%
τ²-Benchagentic
qwen3.7-plus93.0%
kimi-k2.595.9%
SciCodecoding
qwen3.7-plus46.1%
kimi-k2.5N/A
Humanity's Last Examreasoning
qwen3.7-plus35.6%
kimi-k2.530.7%

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-pluskimi-k2.5
Input price / 1M$0.40$0.32$0.60
Output price / 1M$1.60$1.28$3.00
Context window1.0M tokens262K tokens
Max output131K tokens262K tokens
Vision inputYesYes
Tool callingYesYes
ReasoningYesYes
Prompt cachingYesYes
Computer useN/AN/A
ProviderAlibaba CloudMoonshot AI

Questions people ask

Is qwen3.7-plus better than kimi-k2.5?
qwen3.7-plus outperforms kimi-k2.5 on 5 of 6 shared benchmarks. See the benchmark comparison above for specifics: qwen3.7-plus and kimi-k2.5 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, qwen3.7-plus or kimi-k2.5?
qwen3.7-plus is cheaper. qwen3.7-plus costs $0.32/$1.28 per 1M input/output tokens, while kimi-k2.5 costs $0.60/$3.00. The prices shown are what you pay: qwen3.7-plus runs at 20% off its list rate of $0.40 per 1M input and $1.60 per 1M output.
Can I use qwen3.7-plus and kimi-k2.5 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 "moonshot/kimi-k2.5", no other code changes needed.
What are the context windows?
qwen3.7-plus supports up to 1.0M tokens of context. kimi-k2.5 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-k2.5 with one line of code

Requesty provides a single OpenAI-compatible API for 600+ models. Change the model parameter, not your code.