claude-opus-4-6 vs kimi-k2
Side-by-side comparison of claude-opus-4-6 and kimi-k2 — benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. claude-opus-4-6 outperforms kimi-k2 on 7 of 7 shared benchmarks.
claude-opus-4-6
Input / 1M
$5.00
Output / 1M
$25.00
Context
1M
Model ID
anthropic/claude-opus-4-6
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
Benchmark comparison
MMLU Proknowledge
claude-opus-4-687.8%
kimi-k282.3%
GPQA Diamondreasoning
claude-opus-4-681.2%
kimi-k270.0%
HumanEvalcoding
claude-opus-4-694.1%
kimi-k289.9%
SWE-Bench Verifiedcoding
claude-opus-4-674.5%
kimi-k265.8%
MATHmath
claude-opus-4-693.2%
kimi-k289.2%
AIME 2024math
claude-opus-4-687.3%
kimi-k280.1%
MMMUmultimodal
claude-opus-4-678.2%
kimi-k2—
LiveBenchreasoning
claude-opus-4-673.4%
kimi-k268.3%
τ-bench Retailagentic
claude-opus-4-671.2%
kimi-k2—
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
| claude-opus-4-6 | kimi-k2 | |
|---|---|---|
| Input price / 1M | $5.00 | $0.60 |
| Output price / 1M | $25.00 | $2.50 |
| Context window | 1M tokens | 262K tokens |
| Max output | 128K tokens | 262K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | Yes | — |
| Provider | Anthropic PBC | Google LLC (Vertex AI) |
Questions people ask
Is claude-opus-4-6 better than kimi-k2?
claude-opus-4-6 outperforms kimi-k2 on 7 of 7 shared benchmarks. See the benchmark comparison above for specifics — claude-opus-4-6 and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — claude-opus-4-6 or kimi-k2?
kimi-k2 is cheaper. claude-opus-4-6 costs $5.00/$25.00 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50.
Can I use claude-opus-4-6 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 — "anthropic/claude-opus-4-6" or "vertex/kimi-k2" — no other code changes needed.
What are the context windows?
claude-opus-4-6 supports up to 1M 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 claude-opus-4-6 and kimi-k2 with one line of code
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