GPT-6 Sol vs NVIDIA Nemotron 3 Ultra
OpenAI · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick GPT-6 Sol for openai's everyday-work tier below gpt-6 astra, built with the same methods that improved astra's professional work, factuality, coding, computer use, and alignment or openai says it makes about half as many mistakes as gpt-5.6 sol. 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-6 Sol if you want a managed API.
GPT-6 Sol (OpenAI) and NVIDIA Nemotron 3 Ultra (NVIDIA) are two of the models people most often weigh against each other in 2026. GPT-6 Sol is openAI's September 22, 2026 mid-tier update — Astra-derived improvements at half the GPT-5.6 Sol price, $2/$10 per million tokens. 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-6 Sol 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: 1.05M 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: GPT-6 Sol is the newer model by about 4 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GPT-6 Sol | NVIDIA Nemotron 3 Ultra |
|---|---|---|
| Provider | OpenAI (US) | NVIDIA (US) |
| Released | September 22, 2026 | June 4, 2026 |
| Context window | 1.05M tokens (~1,575 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 | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
OpenAI's everyday-work tier below GPT-6 Astra, built with the same methods that improved Astra's professional work, factuality, coding, computer use, and alignment
GPT-6 Sol
OpenAI's September 22, 2026 mid-tier update — Astra-derived improvements at half the GPT-5.6 Sol price, $2/$10 per million tokens — and it is the newer of the two.
OpenAI says it makes about half as many mistakes as GPT-5.6 Sol
GPT-6 Sol
GPT-6 Sol lists openAI says it makes about half as many mistakes as GPT-5.6 Sol among its strengths; NVIDIA Nemotron 3 Ultra does not.
Priced at half of the GPT-5.6 Sol/Luna generation's rates ($2/$10 vs GPT-5.6 Sol's $5/$30)
GPT-6 Sol
GPT-6 Sol lists priced at half of the GPT-5.6 Sol/Luna generation's rates ($2/$10 vs GPT-5.6 Sol's $5/$30) 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-6 Sol 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
GPT-6 Sol is comparatively weak here — more reasoning capability than sibling GPT-6 Luna, but still positioned below flagship GPT-6 Astra
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 its weights are open while GPT-6 Sol is API-only.
Lowest cost at scale
NVIDIA Nemotron 3 Ultra
Its weights are open, so at volume you pay for your own hardware instead of GPT-6 Sol'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 GPT-6 Sol, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6 Sol
Larger 1.05M 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; GPT-6 Sol is API-only.
Anyone whose priority is openai's everyday-work tier below gpt-6 astra, built with the same methods that improved astra's professional work, factuality, coding, computer use, and alignment
→ GPT-6 Sol
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-6 Sol: where it fits
OpenAI's September 22, 2026 mid-tier update — Astra-derived improvements at half the GPT-5.6 Sol price, $2/$10 per million tokens. Released September 22, 2026 by OpenAI, it is built for openAI's everyday-work tier below GPT-6 Astra, built with the same methods that improved Astra's professional work, factuality, coding, computer use, and alignment, openAI says it makes about half as many mistakes as GPT-5.6 Sol, priced at half of the GPT-5.6 Sol/Luna generation's rates ($2/$10 vs GPT-5.6 Sol's $5/$30), and 1.05M-token context window, input capped at 922K.
Its trade-offs are real: more reasoning capability than sibling GPT-6 Luna, but still positioned below flagship GPT-6 Astra, and cache-read pricing ($0.20/MTok) still costlier than Luna's ($0.01/MTok) for high-cache workloads. 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. GPT-6 Sol 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-6 Sol 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-6 Sol 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-6 Sol leans toward openai's everyday-work tier below gpt-6 astra, built with the same methods that improved astra's professional work, factuality, coding, computer use, and alignment 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-6 Sol 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-6 Sol 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 — 1.05M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-6 Sol and NVIDIA Nemotron 3 Ultra together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Sol, 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-6 Sol or NVIDIA Nemotron 3 Ultra?
GPT-6 Sol — released September 22, 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.