kimi-k2 vs grok-4.5
Side-by-side comparison of kimi-k2 and grok-4.5: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. grok-4.5 outperforms kimi-k2 on 4 of 4 shared benchmarks.
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
xAI Corp.
grok-4.5
Input / 1M
$2.00
Output / 1M
$6.00
Context
500K
Model ID
xai/grok-4.5
Benchmark comparison
Intelligence Indexreasoning
kimi-k232.7%
grok-4.553.8%
Coding Indexcoding
kimi-k2N/A
grok-4.572.4%
Math Indexmath
kimi-k294.7%
grok-4.5N/A
GPQA Diamondreasoning
kimi-k283.8%
grok-4.593.1%
AIME 2025math
kimi-k294.7%
grok-4.5N/A
LiveCodeBenchcoding
kimi-k285.3%
grok-4.5N/A
Terminal-Bench Hardagentic
kimi-k231.1%
grok-4.5N/A
τ²-Benchagentic
kimi-k293.0%
grok-4.5N/A
SciCodecoding
kimi-k242.4%
grok-4.554.1%
MMLU Proknowledge
kimi-k284.8%
grok-4.5N/A
Humanity's Last Examreasoning
kimi-k222.3%
grok-4.540.3%
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 | grok-4.5 | |
|---|---|---|
| Input price / 1M | $0.60 | $2.00 |
| Output price / 1M | $2.50 | $6.00 |
| Context window | 262K tokens | 500K tokens |
| Max output | 262K tokens | N/A |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | Google LLC (Vertex AI) | xAI Corp. |
Questions people ask
Is kimi-k2 better than grok-4.5?
grok-4.5 outperforms kimi-k2 on 4 of 4 shared benchmarks. See the benchmark comparison above for specifics: kimi-k2 and grok-4.5 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, kimi-k2 or grok-4.5?
kimi-k2 is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while grok-4.5 costs $2.00/$6.00.
Can I use kimi-k2 and grok-4.5 through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "vertex/kimi-k2" and "xai/grok-4.5", no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. grok-4.5 supports up to 500K 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 and grok-4.5 with one line of code
Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.