Compare/Kimi K2 Thinking vs Nova 2.0 Omni (low)

Kimi K2 ThinkingvsNova 2.0 Omni (low)

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Kimi

Kimi K2 Thinking

Input
$0.6/M
Output
$2.5/M
Speed
103 tok/s
TTFT
0.66s
Amazon

Nova 2.0 Omni (low)

Input
$0.3/M
Output
$2.5/M
Speed
TTFT

Winner by Category

Cheaper
Nova 2.0 Omni (low)
Faster (tok/s)
Kimi K2 Thinking
Lower Latency
Nova 2.0 Omni (low)
Benchmarks (11-0)
Kimi K2 Thinking

Pricing Comparison

MetricKimi K2 ThinkingNova 2.0 Omni (low)
Input ($/M tokens)$0.6$0.3
Output ($/M tokens)$2.5$2.5
Cost for 1M input + 100K output tokens:
Kimi K2 Thinking$0.85
Nova 2.0 Omni (low)$0.55

Speed Comparison

Output Speed (tokens/s) — higher is better
Kimi K2 Thinking
103 tok/s
Nova 2.0 Omni (low)
Time to First Token (seconds) — lower is better
Kimi K2 Thinking
0.66s
Nova 2.0 Omni (low)

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
40.923.2
Coding Index
34.813.9
Math Index
94.756.0
GPQA Diamond
83.8%69.9%
MMLU-Pro
84.8%79.8%
LiveCodeBench
85.3%59.2%
AIME 2025
94.7%56.0%
MATH-500
Humanity's Last Exam
22.3%4.0%
SciCode
42.4%34.3%
IFBench
68.1%61.8%
TerminalBench
31.1%3.8%
Kimi K2 Thinking11 wins
0 winsNova 2.0 Omni (low)

Frequently Asked Questions

Which is cheaper, Kimi K2 Thinking or Nova 2.0 Omni (low)?

Nova 2.0 Omni (low) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.85/M tokens vs $1.07/M for Kimi K2 Thinking.

Which model performs better on benchmarks?

Kimi K2 Thinking wins 11 out of 12 benchmarks compared to 0 for Nova 2.0 Omni (low). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Kimi K2 Thinking generates tokens faster at 103 tok/s vs 0 tok/s. However, Nova 2.0 Omni (low) has lower time-to-first-token (0.00s vs 0.66s).

When should I use Kimi K2 Thinking vs Nova 2.0 Omni (low)?

Choose based on your priorities: Nova 2.0 Omni (low) for lower cost, Kimi K2 Thinking for stronger benchmark performance, and Kimi K2 Thinking for faster generation. For latency-sensitive apps, check the TTFT comparison above.