NVIDIA Nemotron 3 Super vs Kimi K2
Head-to-head specifications
| Metric | NVIDIA Nemotron 3 Super | Kimi K2 | Difference |
|---|---|---|---|
| Intelligence Index | 29.0 | 24.0 | +20.8% |
| Context window | 1M tokens | 200K tokens | — |
| Blended price ($/1M tokens) | $0.22 | $0.51 | -56.9% |
| Output speed (tokens/s) | 158 | 35 | +351.4% |
| Access | Proprietary API | Open weights | — |
- 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: NVIDIA Nemotron 3 Super or Kimi K2?
NVIDIA Nemotron 3 Super advantages
- General intelligence (+17%)
- Context window (+80%)
- Affordability (+57%)
- Output speed (+78%)
Kimi K2 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the NVIDIA Nemotron 3 Super if you need the strongest overall reasoning and accuracy.
- Choose the NVIDIA Nemotron 3 Super if you work with long documents or large codebases.
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.
NVIDIA Nemotron 3 Super vs Kimi K2: which should you choose?
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 — 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 vs Kimi K2: 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). NVIDIA Nemotron 3 Super is proprietary api and Kimi K2 is open weights. 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 NVIDIA Nemotron 3 Super better than the Kimi K2?
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 NVIDIA Nemotron 3 Super and the Kimi K2?
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. Choose the NVIDIA Nemotron 3 Super 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.