Ling 3.1 Flash vs NVIDIA Nemotron 3 Ultra

InclusionAI (Ant Group) · China | NVIDIA · US · Updated June 2026

Quick verdict

Pick Ling 3.1 Flash for hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work or artificial analysis intelligence index score of 41 — above average for its price tier. Pick NVIDIA Nemotron 3 Ultra for the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48) or fast, efficient long-horizon agentic reasoning via a hybrid mamba-transformer design. Choose NVIDIA Nemotron 3 Ultra if you need self-hosting or data privacy; Ling 3.1 Flash if you want a managed API.

Ling 3.1 Flash (InclusionAI (Ant Group), China) and NVIDIA Nemotron 3 Ultra (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. Ling 3.1 Flash is inclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. NVIDIA Nemotron 3 Ultra is nVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

▸Cost model: NVIDIA Nemotron 3 Ultra ships open weights you can self-host (hardware cost only, no per-token fee), while Ling 3.1 Flash is API-metered at $0.3/$0.9 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: NVIDIA Nemotron 3 Ultra holds 3.8× more — 1M (~1,500 pages) vs 262K tokens (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Ling 3.1 Flash is the newer model by about 4 months (released September 30, 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	Ling 3.1 Flash	NVIDIA Nemotron 3 Ultra
Provider	InclusionAI (Ant Group) (China)	NVIDIA (US)
Released	September 30, 2026	June 4, 2026
Context window	262K tokens (~393 pages)	1M (~1,500 pages)
Price (in/out)	$0.3/$0.9 per 1M tokens	Open weight (self-host / free)
Open weight?	No — API only	Yes — self-hostable
Modalities	text, code	text, code
SWE-Bench Verified	Not published	Not published
MRCR v2 @ 1M	Not published	Not published

Who wins what

Hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work

Ling 3.1 Flash

Artificial Analysis Intelligence Index score of 41 — above average for its price tier

Ling 3.1 Flash

NVIDIA Nemotron 3 Ultra is comparatively weak here — trails the best Chinese open models on overall intelligence

First-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok)

Ling 3.1 Flash

Ling 3.1 Flash lists first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok) among its strengths; NVIDIA Nemotron 3 Ultra does not.

The most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48)

NVIDIA Nemotron 3 Ultra

Open weights make this possible at all — Ling 3.1 Flash is API-only, so it cannot leave the vendor's servers.

Fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design

NVIDIA Nemotron 3 Ultra

Its 1M window holds about 3.8× more than Ling 3.1 Flash's 262K tokens in a single prompt.

A fully open release — weights, training data, and recipes under a permissive license

NVIDIA Nemotron 3 Ultra

Ling 3.1 Flash is comparatively weak here — shipped as an announcement plus a two-week free trial rather than a full release — no weights, no model card, and limited independent verification at launch

Lowest cost at scale

NVIDIA Nemotron 3 Ultra

Its weights are open, so at volume you pay for your own hardware instead of Ling 3.1 Flash's $0.3/$0.9 per 1M tokens.

Largest single-prompt input

NVIDIA Nemotron 3 Ultra

Its 1M window is about 3.8× larger than Ling 3.1 Flash's 262K tokens, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ NVIDIA Nemotron 3 Ultra

At Open weight (self-host / free) it undercuts Ling 3.1 Flash, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ NVIDIA Nemotron 3 Ultra

Larger 1M window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ NVIDIA Nemotron 3 Ultra

Open weights let you run it on your own hardware; Ling 3.1 Flash is API-only.

Anyone whose priority is hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work

→ Ling 3.1 Flash

It is specifically built for that.

Anyone whose priority is the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48)

→ NVIDIA Nemotron 3 Ultra

That is its strongest area.

An enterprise with regional data-residency rules

→ NVIDIA Nemotron 3 Ultra or Ling 3.1 Flash

Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

Ling 3.1 Flash: where it fits

InclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. Released September 30, 2026 by InclusionAI (Ant Group), it is built for hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work, artificial Analysis Intelligence Index score of 41 — above average for its price tier, first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok), and predecessor Ling 3.0 Flash shipped MIT-licensed open weights, suggesting a similar path once 3.1 clears review.

Its trade-offs are real: shipped as an announcement plus a two-week free trial rather than a full release — no weights, no model card, and limited independent verification at launch, context window currently served at 262K tokens even though InclusionAI's stated target is 1M — the larger window isn't live yet, not yet open-weight despite InclusionAI saying it plans to open-source the model 'soon', and announced via WeChat rather than a formal English-language technical report, so some specs are vendor-reported only. At $0.3 in / $0.9 out per million tokens, it sits in the budget price band.

NVIDIA Nemotron 3 Ultra: where it fits

NVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents. Released June 4, 2026 by NVIDIA, it is built for the most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48), fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design, a fully open release — weights, training data, and recipes under a permissive license, and strong coding for an open model (SWE-Bench Verified in the high 60s).

Its trade-offs: trails the best Chinese open models on overall intelligence, and a 550B mixture-of-experts is heavy to self-host, and the 1M context is rarely served in full. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

The defining split here is open vs. closed. NVIDIA Nemotron 3 Ultra gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Ling 3.1 Flash gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Ling 3.1 Flash and NVIDIA Nemotron 3 Ultra 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 pricing

Frequently asked questions

Is Ling 3.1 Flash or NVIDIA Nemotron 3 Ultra better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Ling 3.1 Flash leans toward hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work while NVIDIA Nemotron 3 Ultra leans toward the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Ling 3.1 Flash or NVIDIA Nemotron 3 Ultra?

NVIDIA Nemotron 3 Ultra is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Ling 3.1 Flash is API-metered at $0.3/$0.9 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

NVIDIA Nemotron 3 Ultra — 1M vs 262K tokens, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Ling 3.1 Flash and NVIDIA Nemotron 3 Ultra together?

Yes — a multi-model platform like LumiChats gives you Ling 3.1 Flash, NVIDIA Nemotron 3 Ultra 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, Ling 3.1 Flash or NVIDIA Nemotron 3 Ultra?

Ling 3.1 Flash — released September 30, 2026, about 4 months after NVIDIA Nemotron 3 Ultra.

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Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.