kimi-k2 vs claude-sonnet-4-5
Side-by-side comparison of kimi-k2 and claude-sonnet-4-5— benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API.
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
claude-sonnet-4-5
Input / 1M
$3.00
Output / 1M
$15.00
Context
1M
Model ID
anthropic/claude-sonnet-4-5
Benchmark comparison
Intelligence Indexreasoning
kimi-k240.9%
claude-sonnet-4-5—
Coding Indexcoding
kimi-k234.8%
claude-sonnet-4-5—
Math Indexmath
kimi-k294.7%
claude-sonnet-4-5—
GPQA Diamondreasoning
kimi-k283.8%
claude-sonnet-4-5—
AIME 2025math
kimi-k294.7%
claude-sonnet-4-5—
LiveCodeBenchcoding
kimi-k285.3%
claude-sonnet-4-5—
Terminal-Bench Hardagentic
kimi-k231.1%
claude-sonnet-4-5—
τ²-Benchagentic
kimi-k293.0%
claude-sonnet-4-5—
SciCodecoding
kimi-k242.4%
claude-sonnet-4-5—
MMLU Proknowledge
kimi-k284.8%
claude-sonnet-4-5—
Humanity's Last Examreasoning
kimi-k222.3%
claude-sonnet-4-5—
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 | claude-sonnet-4-5 | |
|---|---|---|
| Input price / 1M | $0.60 | $3.00 |
| Output price / 1M | $2.50 | $15.00 |
| Context window | 262K tokens | 1M tokens |
| Max output | 262K tokens | 64K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | — | Yes |
| Provider | Google LLC (Vertex AI) | Anthropic PBC |
Questions people ask
Is kimi-k2 better than claude-sonnet-4-5?
Benchmark data is limited for one or both models. Compare pricing and capabilities in the tables above, and test both on your own workload.
Which is cheaper — kimi-k2 or claude-sonnet-4-5?
kimi-k2 is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while claude-sonnet-4-5 costs $3.00/$15.00.
Can I use kimi-k2 and claude-sonnet-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 — "vertex/kimi-k2" or "anthropic/claude-sonnet-4-5" — no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. claude-sonnet-4-5 supports up to 1M 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 claude-sonnet-4-5 with one line of code
Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.
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