Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. 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-5.3-Codex if you want a managed API.
GPT-5.3-Codex (OpenAI) and NVIDIA Nemotron 3 Ultra (NVIDIA) are two of the models people most often weigh against each other in 2026. GPT-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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
Cost model: NVIDIA Nemotron 3 Ultra ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.3-Codex is API-metered at $1.75/$14 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.
Specifications
Spec
GPT-5.3-Codex
NVIDIA Nemotron 3 Ultra
Provider
OpenAI (US)
NVIDIA (US)
Released
February 24, 2026
June 4, 2026
Context window
400K (~600 pages)
1M (~1,500 pages)
Price (in/out)
$1.75/$14 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
Dedicated coding agent: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
CLI and IDE integration: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
Autonomous software tasks: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
The most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48): NVIDIA Nemotron 3 Ultra — A core design strength of NVIDIA Nemotron 3 Ultra.
Fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design: NVIDIA Nemotron 3 Ultra — A core design strength of NVIDIA Nemotron 3 Ultra.
A fully open release — weights, training data, and recipes under a permissive license: NVIDIA Nemotron 3 Ultra — A core design strength of NVIDIA Nemotron 3 Ultra.
Lowest cost at scale: NVIDIA Nemotron 3 Ultra — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: NVIDIA Nemotron 3 Ultra — Its 1M window is about 2.5× larger, 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-5.3-Codex, 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-5.3-Codex is API-only.
Anyone whose priority is dedicated coding agent: GPT-5.3-Codex — 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-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released February 24, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.75 in / $14 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-5.3-Codex 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.
Frequently asked questions
Is GPT-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex 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-5.3-Codex is API-metered at $1.75/$14 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-5.3-Codex and NVIDIA Nemotron 3 Ultra together?
Yes — a multi-model platform like LumiChats gives you GPT-5.3-Codex, 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-5.3-Codex or NVIDIA Nemotron 3 Ultra?
NVIDIA Nemotron 3 Ultra — released June 4, 2026, about 3 months after GPT-5.3-Codex.
GPT-5.3-Codex vs NVIDIA Nemotron 3 Ultra
OpenAI · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. 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-5.3-Codex if you want a managed API.
GPT-5.3-Codex (OpenAI) and NVIDIA Nemotron 3 Ultra (NVIDIA) are two of the models people most often weigh against each other in 2026. GPT-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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-5.3-Codex is API-metered at $1.75/$14 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-5.3-Codex
NVIDIA Nemotron 3 Ultra
Provider
OpenAI (US)
NVIDIA (US)
Released
February 24, 2026
June 4, 2026
Context window
400K (~600 pages)
1M (~1,500 pages)
Price (in/out)
$1.75/$14 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
Dedicated coding agent
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
CLI and IDE integration
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
Autonomous software tasks
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
The most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48)
NVIDIA Nemotron 3 Ultra
A core design strength of NVIDIA Nemotron 3 Ultra.
Fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design
NVIDIA Nemotron 3 Ultra
A core design strength of NVIDIA Nemotron 3 Ultra.
A fully open release — weights, training data, and recipes under a permissive license
NVIDIA Nemotron 3 Ultra
A core design strength of NVIDIA Nemotron 3 Ultra.
Lowest cost at scale
NVIDIA Nemotron 3 Ultra
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
NVIDIA Nemotron 3 Ultra
Its 1M window is about 2.5× larger, 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-5.3-Codex, 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-5.3-Codex is API-only.
Anyone whose priority is dedicated coding agent
→ GPT-5.3-Codex
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-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released February 24, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.75 in / $14 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-5.3-Codex 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-5.3-Codex 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.
Is GPT-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex 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-5.3-Codex is API-metered at $1.75/$14 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-5.3-Codex and NVIDIA Nemotron 3 Ultra together?
Yes — a multi-model platform like LumiChats gives you GPT-5.3-Codex, 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-5.3-Codex or NVIDIA Nemotron 3 Ultra?
NVIDIA Nemotron 3 Ultra — released June 4, 2026, about 3 months after GPT-5.3-Codex.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.