Compare/Kimi K2 Thinking vs Nova 2.0 Omni (Non-reasoning)

Kimi K2 ThinkingvsNova 2.0 Omni (Non-reasoning)

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

Kimi

Kimi K2 Thinking

Input
$0.6/M
Output
$2.5/M
Speed
121 tok/s
TTFT
1.24s
Amazon

Nova 2.0 Omni (Non-reasoning)

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

Winner by Category

Cheaper
Nova 2.0 Omni (Non-reasoning)
Faster (tok/s)
Kimi K2 Thinking
Lower Latency
Kimi K2 Thinking
Benchmarks (1-0)
Kimi K2 Thinking

Pricing Comparison

MetricKimi K2 ThinkingNova 2.0 Omni (Non-reasoning)
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 (Non-reasoning)$0.55

Speed Comparison

Output Speed (tokens/s) — higher is better
Kimi K2 Thinking
121 tok/s
Nova 2.0 Omni (Non-reasoning)
Time to First Token (seconds) — lower is better
Kimi K2 Thinking
1.24s
Nova 2.0 Omni (Non-reasoning)

Editorial Analysis

Verdict. Kimi K2 Thinking wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Nova 2.0 Omni (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Nova 2.0 Omni (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Kimi K2 Thinking is strongest on Intelligence Index (33.5). Nova 2.0 Omni (Non-reasoning) leads on Intelligence Index (10.4).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. Kimi and Amazon sell to overlapping but distinct developer audiences: Kimi tends to ship frontier reasoning models with premium positioning, while Amazon often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Kimi K2 Thinking costs $55.50 ($666/year); Nova 2.0 Omni (Non-reasoning) costs $46.50 ($558/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Kimi K2 Thinking ≈ $8.00/run, Nova 2.0 Omni (Non-reasoning) ≈ $6.50/run. At agent/realtime scale (200M input / 100M output per million requests): Kimi K2 Thinking ≈ $370/run, Nova 2.0 Omni (Non-reasoning) ≈ $310/run. Nova 2.0 Omni (Non-reasoning) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
33.510.4
Coding Index
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Kimi K2 Thinking1 wins
0 winsNova 2.0 Omni (Non-reasoning)

Frequently Asked Questions

Which is cheaper, Kimi K2 Thinking or Nova 2.0 Omni (Non-reasoning)?

Nova 2.0 Omni (Non-reasoning) 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 1 out of 12 benchmarks compared to 0 for Nova 2.0 Omni (Non-reasoning). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Kimi K2 Thinking generates tokens faster at 121 tok/s vs — tok/s. Kimi K2 Thinking also has lower time-to-first-token (1.24s vs —s).

When should I use Kimi K2 Thinking vs Nova 2.0 Omni (Non-reasoning)?

Choose based on your priorities: Nova 2.0 Omni (Non-reasoning) 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.