Gemini 4 Argon vs NVIDIA Nemotron 3 Ultra
Google · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick Gemini 4 Argon for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses or google-reported deepswe v1.1 of 77.9% and lvbench long-video understanding of 91.7%. 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; Gemini 4 Argon if you want a managed API.
Gemini 4 Argon (Google) and NVIDIA Nemotron 3 Ultra (NVIDIA) are two of the models people most often weigh against each other in 2026. Gemini 4 Argon is google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). 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 Gemini 4 Argon is API-metered at $2/$10 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: 1M tokens vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Gemini 4 Argon is the newer model by about 4 months (released October 13, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Gemini 4 Argon | NVIDIA Nemotron 3 Ultra |
|---|---|---|
| Provider | Google (US) | NVIDIA (US) |
| Released | October 13, 2026 | June 4, 2026 |
| Context window | 1M tokens (~1,573 pages) | 1M (~1,500 pages) |
| Price (in/out) | $2/$10 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, audio, video, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses
Gemini 4 Argon
NVIDIA Nemotron 3 Ultra is comparatively weak here — a 550B mixture-of-experts is heavy to self-host, and the 1M context is rarely served in full
Google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%
Gemini 4 Argon
Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1) — and it is the newer of the two.
Artificial Analysis Intelligence Index of 53 at high effort (AI Weekly)
Gemini 4 Argon
NVIDIA Nemotron 3 Ultra is comparatively weak here — trails the best Chinese open models on overall intelligence
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 — Gemini 4 Argon 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
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 — and its weights are open while Gemini 4 Argon is API-only.
A fully open release — weights, training data, and recipes under a permissive license
NVIDIA Nemotron 3 Ultra
NVIDIA Nemotron 3 Ultra lists a fully open release — weights, training data, and recipes under a permissive license among its strengths; Gemini 4 Argon does not.
Lowest cost at scale
NVIDIA Nemotron 3 Ultra
Its weights are open, so at volume you pay for your own hardware instead of Gemini 4 Argon's $2/$10 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ NVIDIA Nemotron 3 Ultra
At Open weight (self-host / free) it undercuts Gemini 4 Argon, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 4 Argon
Larger 1M tokens 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; Gemini 4 Argon is API-only.
Anyone whose priority is 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses
→ Gemini 4 Argon
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.
Gemini 4 Argon: where it fits
Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Released October 13, 2026 by Google, it is built for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%, and artificial Analysis Intelligence Index of 53 at high effort (AI Weekly).
Its trade-offs are real: introductory $2/$10 pricing ends November 30; from December 1 it doubles to $4/$20, verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high, and announced September 30 for trusted cyber defenders only; broad paid-tier availability followed October 13 (not on the free tier). At $2 in / $10 out per million tokens, it sits in the mid 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. Gemini 4 Argon 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 Gemini 4 Argon 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 pricingFrequently asked questions
Is Gemini 4 Argon 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, Gemini 4 Argon leans toward 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses 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, Gemini 4 Argon 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 Gemini 4 Argon is API-metered at $2/$10 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?
Effectively neither — 1M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 4 Argon and NVIDIA Nemotron 3 Ultra together?
Yes — a multi-model platform like LumiChats gives you Gemini 4 Argon, 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, Gemini 4 Argon or NVIDIA Nemotron 3 Ultra?
Gemini 4 Argon — released October 13, 2026, about 4 months after NVIDIA Nemotron 3 Ultra.
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.