kimi-k2.6 vs kimi-k2
Side-by-side comparison of kimi-k2.6 and kimi-k2: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. kimi-k2.6 outperforms kimi-k2 on 6 of 6 shared benchmarks.
kimi-k2.6
Input / 1M
$0.95
Output / 1M
$4.00
Context
262K
Model ID
moonshot/kimi-k2.6
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
Benchmark comparison
Intelligence Indexreasoning
kimi-k2.644.2%
kimi-k232.7%
Coding Indexcoding
kimi-k2.661.8%
kimi-k2N/A
Math Indexmath
kimi-k2.6N/A
kimi-k294.7%
GPQA Diamondreasoning
kimi-k2.691.1%
kimi-k283.8%
AIME 2025math
kimi-k2.6N/A
kimi-k294.7%
LiveCodeBenchcoding
kimi-k2.6N/A
kimi-k285.3%
Terminal-Bench Hardagentic
kimi-k2.643.9%
kimi-k231.1%
τ²-Benchagentic
kimi-k2.695.9%
kimi-k293.0%
SciCodecoding
kimi-k2.653.5%
kimi-k242.4%
MMLU Proknowledge
kimi-k2.6N/A
kimi-k284.8%
Humanity's Last Examreasoning
kimi-k2.635.9%
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
| kimi-k2.6 | kimi-k2 | |
|---|---|---|
| Input price / 1M | $0.95 | $0.60 |
| Output price / 1M | $4.00 | $2.50 |
| Context window | 262K tokens | 262K tokens |
| Max output | 262K tokens | 262K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | Moonshot AI | Google LLC (Vertex AI) |
Questions people ask
Is kimi-k2.6 better than kimi-k2?
kimi-k2.6 outperforms kimi-k2 on 6 of 6 shared benchmarks. See the benchmark comparison above for specifics: kimi-k2.6 and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, kimi-k2.6 or kimi-k2?
kimi-k2 is cheaper. kimi-k2.6 costs $0.95/$4.00 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50.
Can I use kimi-k2.6 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 "moonshot/kimi-k2.6" and "vertex/kimi-k2", no other code changes needed.
What are the context windows?
kimi-k2.6 supports up to 262K 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 kimi-k2.6 and kimi-k2 with one line of code
Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.