Kimi K2 0905 vs Nova 2.0 Lite
Head-to-head specifications
| Metric | Kimi K2 0905 | Nova 2.0 Lite | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 24.0 | +16.7% |
| Context window | 300K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.62 | $0.43 | +44.2% |
| Output speed (tokens/s) | 36 | 146 | -75.3% |
| Access | Open weights | Proprietary API | — |
- Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0).
- Nova 2.0 Lite is the cheaper model to run at $0.43/1M blended tokens — about 1.4× cheaper.
- Nova 2.0 Lite offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Kimi K2 0905 or Nova 2.0 Lite?
Kimi K2 0905 advantages
- General intelligence (+14%)
Nova 2.0 Lite advantages
- Context window (+70%)
- Affordability (+31%)
- Output speed (+75%)
Which should you choose?
- Choose the Kimi K2 0905 if you need the strongest overall reasoning and accuracy.
- Choose the Nova 2.0 Lite if you work with long documents or large codebases.
Value for money
Nova 2.0 Lite offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Kimi K2 0905 vs Nova 2.0 Lite: which should you choose?
Kimi K2 0905 — Moonshot AI text model with an Intelligence Index of 28, a 300K-token context window and a blended price of $0.62/1M tokens (open weights).
Nova 2.0 Lite — Amazon multimodal model with an Intelligence Index of 24, a 1M-token context window and a blended price of $0.43/1M tokens.
Kimi K2 0905 vs Nova 2.0 Lite: Kimi K2 0905 scores higher on the Intelligence Index. Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0). Nova 2.0 Lite is the cheaper model to run at $0.43/1M blended tokens — about 1.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Kimi K2 0905 scores 28.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Nova 2.0 Lite accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Nova 2.0 Lite generates faster (146 vs 36 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Nova 2.0 Lite is the cheaper model to run ($0.43 vs $0.62 per 1M tokens). Kimi K2 0905 is open weights and Nova 2.0 Lite is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Kimi K2 0905 better than the Nova 2.0 Lite?
Nova 2.0 Lite takes the overall edge, though Kimi K2 0905 wins in specific areas worth weighing. Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0).
What is the main difference between the Kimi K2 0905 and the Nova 2.0 Lite?
Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0). Nova 2.0 Lite is the cheaper model to run at $0.43/1M blended tokens — about 1.4× cheaper.
Which is better value?
Nova 2.0 Lite offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Kimi K2 0905 if you need the strongest overall reasoning and accuracy. Choose the Nova 2.0 Lite if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.