LongCat-2.0 vs NVIDIA Nemotron 3 Super
Meituan · China | NVIDIA · US · Updated June 2026
Quick verdict
Pick LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months or massive native 1m context at near-linear cost via sparse attention. 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).
LongCat-2.0 (Meituan, 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. LongCat-2.0 is a trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. 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. The breakdown below works through their capabilities and ideal use cases so you can match one to your task.
Key differences at a glance
- ▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: LongCat-2.0 is the newer model by about 4 months (released July 5, 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 | LongCat-2.0 | NVIDIA Nemotron 3 Super |
|---|---|---|
| Provider | Meituan (China) | NVIDIA (US) |
| Released | July 5, 2026 | March 11, 2026 |
| Context window | 1M (~1,500 pages) | 1M (~1,500 pages) |
| Price (in/out) | Open weight (self-host / free) | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | Not published | 60.47% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months
LongCat-2.0
A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips — and it is the newer of the two.
Massive native 1M context at near-linear cost via sparse attention
LongCat-2.0
LongCat-2.0 lists massive native 1M context at near-linear cost via sparse attention among its strengths; NVIDIA Nemotron 3 Super does not.
Fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active)
LongCat-2.0
LongCat-2.0 lists fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active) among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists high-throughput agentic reasoning (up to 2.2x GPT-OSS-120B) among its strengths; LongCat-2.0 does not.
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists 1M-token context with strong long-context retrieval (91.6% RULER @ 1M) among its strengths; LongCat-2.0 does not.
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; LongCat-2.0 does not.
Which should you pick?
Anyone whose priority is near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months
→ LongCat-2.0
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 LongCat-2.0
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
LongCat-2.0: where it fits
A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. Released July 5, 2026 by Meituan, it is built for near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months, massive native 1M context at near-linear cost via sparse attention, fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active), and trained end to end on domestic Chinese chips, independent of Nvidia hardware.
Its trade-offs are real: a 1.6T model is extremely expensive to self-host, so most use leans on the China-hosted API, and headline scores are vendor-reported on SWE-Bench Pro, not the Verified set. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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." LongCat-2.0 (China) and NVIDIA Nemotron 3 Super (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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 LongCat-2.0 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 LongCat-2.0 or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for LongCat-2.0, so the honest test is your own repository — run an identical real bug through both. By design, LongCat-2.0 leans toward near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months while NVIDIA Nemotron 3 Super leans toward high-throughput agentic reasoning (up to 2.2x gpt-oss-120b), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LongCat-2.0 or NVIDIA Nemotron 3 Super?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both LongCat-2.0 and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you LongCat-2.0, 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, LongCat-2.0 or NVIDIA Nemotron 3 Super?
LongCat-2.0 — released July 5, 2026, about 4 months 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.