kimi-k2 vs claude-opus-4-7
Side-by-side comparison of kimi-k2 and claude-opus-4-7 — benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. claude-opus-4-7 outperforms kimi-k2 on 7 of 7 shared benchmarks.
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
claude-opus-4-7
Input / 1M
$5.00
Output / 1M
$25.00
Context
1M
Model ID
anthropic/claude-opus-4-7
Benchmark comparison
MMLU Proknowledge
kimi-k282.3%
claude-opus-4-789.1%
GPQA Diamondreasoning
kimi-k270.0%
claude-opus-4-783.4%
HumanEvalcoding
kimi-k289.9%
claude-opus-4-795.2%
SWE-Bench Verifiedcoding
kimi-k265.8%
claude-opus-4-778.6%
MATHmath
kimi-k289.2%
claude-opus-4-794.8%
AIME 2024math
kimi-k280.1%
claude-opus-4-791.5%
MMMUmultimodal
kimi-k2—
claude-opus-4-779.8%
LiveBenchreasoning
kimi-k268.3%
claude-opus-4-776.2%
τ-bench Retailagentic
kimi-k2—
claude-opus-4-774.5%
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-opus-4-7 | |
|---|---|---|
| Input price / 1M | $0.60 | $5.00 |
| Output price / 1M | $2.50 | $25.00 |
| Context window | 262K tokens | 1M tokens |
| Max output | 262K tokens | 128K 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-opus-4-7?
claude-opus-4-7 outperforms kimi-k2 on 7 of 7 shared benchmarks. See the benchmark comparison above for specifics — kimi-k2 and claude-opus-4-7 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — kimi-k2 or claude-opus-4-7?
kimi-k2 is cheaper. kimi-k2 costs $0.60/$2.50 per 1M input/output tokens, while claude-opus-4-7 costs $5.00/$25.00.
Can I use kimi-k2 and claude-opus-4-7 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-opus-4-7" — no other code changes needed.
What are the context windows?
kimi-k2 supports up to 262K tokens of context. claude-opus-4-7 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-opus-4-7 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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