GPT-5.4 Mini vs NVIDIA Nemotron 3 Ultra
OpenAI · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick GPT-5.4 Mini for free for every chatgpt user or fast, low-cost general tasks. 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; GPT-5.4 Mini if you want a managed API.
GPT-5.4 Mini (OpenAI) and NVIDIA Nemotron 3 Ultra (NVIDIA) are two of the models people most often weigh against each other in 2026. GPT-5.4 Mini is openAI's free, fast workhorse — capable general AI with no subscription needed. 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 GPT-5.4 Mini is API-metered at $0.75/$4.5 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 2.5× more — 1M (~1,500 pages) vs 400K (~600 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: NVIDIA Nemotron 3 Ultra is the newer model by about 3 months (released June 4, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GPT-5.4 Mini | NVIDIA Nemotron 3 Ultra |
|---|---|---|
| Provider | OpenAI (US) | NVIDIA (US) |
| Released | March 17, 2026 | June 4, 2026 |
| Context window | 400K (~600 pages) | 1M (~1,500 pages) |
| Price (in/out) | $0.75/$4.5 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Free for every ChatGPT user
GPT-5.4 Mini
GPT-5.4 Mini lists free for every ChatGPT user among its strengths; NVIDIA Nemotron 3 Ultra does not.
Fast, low-cost general tasks
GPT-5.4 Mini
GPT-5.4 Mini lists fast, low-cost general tasks among its strengths; NVIDIA Nemotron 3 Ultra does not.
Subagent capabilities
GPT-5.4 Mini
GPT-5.4 Mini lists subagent capabilities 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 — GPT-5.4 Mini 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 2.5× more than GPT-5.4 Mini's 400K in a single prompt.
A fully open release — weights, training data, and recipes under a permissive license
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 it carries the larger 1M context.
Lowest cost at scale
NVIDIA Nemotron 3 Ultra
Its weights are open, so at volume you pay for your own hardware instead of GPT-5.4 Mini's $0.75/$4.5 per 1M tokens.
Largest single-prompt input
NVIDIA Nemotron 3 Ultra
Its 1M window is about 2.5× larger than GPT-5.4 Mini's 400K, 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 GPT-5.4 Mini, 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; GPT-5.4 Mini is API-only.
Anyone whose priority is free for every chatgpt user
→ GPT-5.4 Mini
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.
GPT-5.4 Mini: where it fits
OpenAI's free, fast workhorse — capable general AI with no subscription needed. Released March 17, 2026 by OpenAI, it is built for free for every ChatGPT user, fast, low-cost general tasks, subagent capabilities, and native multimodal reasoning.
Its trade-offs are real: smaller context than flagship models, and not for the hardest reasoning. At $0.75 in / $4.5 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. GPT-5.4 Mini 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 GPT-5.4 Mini 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 GPT-5.4 Mini 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, GPT-5.4 Mini leans toward free for every chatgpt user 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, GPT-5.4 Mini 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 GPT-5.4 Mini is API-metered at $0.75/$4.5 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 400K, about 2.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.4 Mini and NVIDIA Nemotron 3 Ultra together?
Yes — a multi-model platform like LumiChats gives you GPT-5.4 Mini, 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, GPT-5.4 Mini or NVIDIA Nemotron 3 Ultra?
NVIDIA Nemotron 3 Ultra — released June 4, 2026, about 3 months after GPT-5.4 Mini.
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.