Kimi K2.6 vs NVIDIA Nemotron 3 Super
Moonshot AI · China | NVIDIA · US · Updated June 2026
Quick verdict
Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents). Pick NVIDIA Nemotron 3 Super for high-throughput agentic reasoning (up to 2.2x gpt-oss-120b) or 1m-token context with strong long-context retrieval (91.6% ruler @ 1m). On a tight budget at scale, NVIDIA Nemotron 3 Super is the value pick.
Kimi K2.6 (Moonshot AI, China) and NVIDIA Nemotron 3 Super (NVIDIA, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Kimi K2.6 is moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. NVIDIA Nemotron 3 Super is nVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context. They diverge most on price, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: NVIDIA Nemotron 3 Super holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Coding: Kimi K2.6 leads SWE-Bench Verified by 19.7 points (80.2% vs 60.47%) — a real edge on hard, real-world software tasks.
- ▸Recency: Kimi K2.6 is the newer model by about 40 days (released April 20, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Kimi K2.6 | NVIDIA Nemotron 3 Super |
|---|---|---|
| Provider | Moonshot AI (China) | NVIDIA (US) |
| Released | April 20, 2026 | March 11, 2026 |
| Context window | 256K (~393 pages) | 1M (~1,500 pages) |
| Price (in/out) | $0.95/$4 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, video, code | text, code |
| SWE-Bench Verified | 80.2% | 60.47% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open-weight agentic coding and long-horizon tasks
Kimi K2.6
It scores 80.2% on SWE-Bench Verified against NVIDIA Nemotron 3 Super's 60.47% — a 19.7-point edge on real repository work.
Multi-agent swarms (scales to ~300 sub-agents)
Kimi K2.6
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host — and it leads SWE-Bench Verified 80.2% to 60.47%.
Self-hosting and data-residency control
Kimi K2.6
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host — and it is the newer of the two.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
NVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context — and it carries the larger 1M context.
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
Its 1M window holds about 3.8× more than Kimi K2.6's 256K in a single prompt.
Strong math reasoning (90.21% AIME 2025)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists strong math reasoning (90.21% AIME 2025) among its strengths; Kimi K2.6 does not.
Lowest cost at scale
NVIDIA Nemotron 3 Super
Its weights are open, so at volume you pay for your own hardware instead of Kimi K2.6's $0.95/$4 per 1M tokens.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 3.8× larger than Kimi K2.6's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ NVIDIA Nemotron 3 Super
At Open weight (self-host / free) it undercuts Kimi K2.6, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ NVIDIA Nemotron 3 Super
Larger 1M window fits more in one prompt.
Anyone whose priority is open-weight agentic coding and long-horizon tasks
→ Kimi K2.6
It is specifically built for that.
Anyone whose priority is high-throughput agentic reasoning (up to 2.2x gpt-oss-120b)
→ NVIDIA Nemotron 3 Super
That is its strongest area.
An enterprise with regional data-residency rules
→ NVIDIA Nemotron 3 Super or Kimi K2.6
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Kimi K2.6: where it fits
Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. Released April 20, 2026 by Moonshot AI, it is built for open-weight agentic coding and long-horizon tasks, multi-agent swarms (scales to ~300 sub-agents), self-hosting and data-residency control, and strong price-to-performance across many API providers.
Its trade-offs are real: 256K context trails the 1M Claude and Gemini flagships, weaker on single-turn vision and grounded multimodal tasks, and chinese-jurisdiction data and newer vendor track record. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
NVIDIA Nemotron 3 Super: where it fits
NVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context. Released March 11, 2026 by NVIDIA, it is built for high-throughput agentic reasoning (up to 2.2x GPT-OSS-120B), 1M-token context with strong long-context retrieval (91.6% RULER @ 1M), strong math reasoning (90.21% AIME 2025), and fully open weights, datasets, and recipes for self-hosting.
Its trade-offs: text-only; no image, audio, or video input, and requires roughly 8x H100-80GB GPUs to self-host at BF16. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Kimi K2.6 (China) and NVIDIA Nemotron 3 Super (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. NVIDIA Nemotron 3 Super is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Kimi K2.6 and NVIDIA Nemotron 3 Super without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Kimi K2.6 or NVIDIA Nemotron 3 Super better for coding?
On SWE-Bench Verified, Kimi K2.6 scores 80.2% and NVIDIA Nemotron 3 Super scores 60.47% — Kimi K2.6 has the measurable edge.
Which is cheaper, Kimi K2.6 or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super is cheaper — $0.95/$4 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K2.6 and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.6, NVIDIA Nemotron 3 Super and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Kimi K2.6 or NVIDIA Nemotron 3 Super?
Kimi K2.6 — released April 20, 2026, about 40 days after NVIDIA Nemotron 3 Super.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.