Kimi K2 vs NVIDIA Nemotron 3 Super
Head-to-head specifications
| Metric | Kimi K2 | NVIDIA Nemotron 3 Super | Difference |
|---|---|---|---|
| Intelligence Index | 24.0 | 29.0 | -17.2% |
| Context window | 200K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.51 | $0.22 | +131.8% |
| Output speed (tokens/s) | 35 | 158 | -77.8% |
| Access | Open weights | Proprietary API | — |
- NVIDIA Nemotron 3 Super leads overall capability (Intelligence Index 29.0 vs 24.0).
- NVIDIA Nemotron 3 Super is the cheaper model to run at $0.22/1M blended tokens — about 2.3× cheaper.
- NVIDIA Nemotron 3 Super offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Kimi K2 or NVIDIA Nemotron 3 Super?
Kimi K2 advantages
- No decisive advantage on the tracked metrics.
NVIDIA Nemotron 3 Super advantages
- General intelligence (+17%)
- Context window (+80%)
- Affordability (+57%)
- Output speed (+78%)
Which should you choose?
- Choose the NVIDIA Nemotron 3 Super if you need the strongest overall reasoning and accuracy.
Value for money
NVIDIA Nemotron 3 Super offers more intelligence per dollar (2.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Kimi K2 vs NVIDIA Nemotron 3 Super: which should you choose?
Kimi K2 — Moonshot AI text model with an Intelligence Index of 24, a 200K-token context window and a blended price of $0.51/1M tokens (open weights).
NVIDIA Nemotron 3 Super — NVIDIA multimodal model with an Intelligence Index of 29, a 1M-token context window and a blended price of $0.22/1M tokens.
Kimi K2 vs NVIDIA Nemotron 3 Super: NVIDIA Nemotron 3 Super scores higher on the Intelligence Index. NVIDIA Nemotron 3 Super leads overall capability (Intelligence Index 29.0 vs 24.0). NVIDIA Nemotron 3 Super is the cheaper model to run at $0.22/1M blended tokens — about 2.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the NVIDIA Nemotron 3 Super scores 29.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The NVIDIA Nemotron 3 Super 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, NVIDIA Nemotron 3 Super generates faster (158 vs 35 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, NVIDIA Nemotron 3 Super is the cheaper model to run ($0.22 vs $0.51 per 1M tokens). Kimi K2 is open weights and NVIDIA Nemotron 3 Super 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 better than the NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super is the clearly stronger overall choice, winning most of the dimensions that matter. NVIDIA Nemotron 3 Super leads overall capability (Intelligence Index 29.0 vs 24.0).
What is the main difference between the Kimi K2 and the NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super leads overall capability (Intelligence Index 29.0 vs 24.0). NVIDIA Nemotron 3 Super is the cheaper model to run at $0.22/1M blended tokens — about 2.3× cheaper.
Which is better value?
NVIDIA Nemotron 3 Super offers more intelligence per dollar (2.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the NVIDIA Nemotron 3 Super if you need the strongest overall reasoning and accuracy.
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.