GPT-4o mini vs NVIDIA Nemotron 3 Super
OpenAI · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). Pick NVIDIA Nemotron 3 Super for high-throughput agentic reasoning (up to 2.2x gpt-oss-120b) or 1m-token context with strong long-context retrieval (91.6% ruler @ 1m). Choose NVIDIA Nemotron 3 Super if you need self-hosting or data privacy; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. NVIDIA Nemotron 3 Super is nVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context. 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 Super ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-4o mini is API-metered at $0.15/$0.6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: NVIDIA Nemotron 3 Super holds 7.8× more — 1M (~1,500 pages) vs 128K (~192 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 Super is the newer model by about 20 months (released March 11, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GPT-4o mini | NVIDIA Nemotron 3 Super |
|---|---|---|
| Provider | OpenAI (US) | NVIDIA (US) |
| Released | July 18, 2024 | March 11, 2026 |
| Context window | 128K (~192 pages) | 1M (~1,500 pages) |
| Price (in/out) | $0.15/$0.6 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 | 60.47% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Very low cost per token for its capability tier
GPT-4o mini
GPT-4o mini lists very low cost per token for its capability tier among its strengths; NVIDIA Nemotron 3 Super does not.
Strong coding for a small model (87.2% HumanEval)
GPT-4o mini
GPT-4o mini lists strong coding for a small model (87.2% HumanEval) among its strengths; NVIDIA Nemotron 3 Super does not.
Leading MMLU among peer small models (82%)
GPT-4o mini
GPT-4o mini lists leading MMLU among peer small models (82%) among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
Its 1M window holds about 7.8× more than GPT-4o mini's 128K in a single prompt.
Strong math reasoning (90.21% AIME 2025)
NVIDIA Nemotron 3 Super
NVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context — and it carries the larger 1M context.
Lowest cost at scale
NVIDIA Nemotron 3 Super
Its weights are open, so at volume you pay for your own hardware instead of GPT-4o mini's $0.15/$0.6 per 1M tokens.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 7.8× larger than GPT-4o mini's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ NVIDIA Nemotron 3 Super
At Open weight (self-host / free) it undercuts GPT-4o mini, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ NVIDIA Nemotron 3 Super
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ NVIDIA Nemotron 3 Super
Open weights let you run it on your own hardware; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier
→ GPT-4o mini
It is specifically built for that.
Anyone whose priority is high-throughput agentic reasoning (up to 2.2x gpt-oss-120b)
→ NVIDIA Nemotron 3 Super
That is its strongest area.
GPT-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
NVIDIA Nemotron 3 Super: where it fits
NVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context. Released March 11, 2026 by NVIDIA, it is built for high-throughput agentic reasoning (up to 2.2x GPT-OSS-120B), 1M-token context with strong long-context retrieval (91.6% RULER @ 1M), strong math reasoning (90.21% AIME 2025), and fully open weights, datasets, and recipes for self-hosting.
Its trade-offs: text-only; no image, audio, or video input, and requires roughly 8x H100-80GB GPUs to self-host at BF16. 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 Super gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-4o 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-4o mini and NVIDIA Nemotron 3 Super 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-4o mini or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for GPT-4o mini, so the honest test is your own repository — run an identical real bug through both. By design, GPT-4o mini leans toward very low cost per token for its capability tier while NVIDIA Nemotron 3 Super leans toward high-throughput agentic reasoning (up to 2.2x gpt-oss-120b), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-4o mini is API-metered at $0.15/$0.6 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 Super — 1M vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-4o mini and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, NVIDIA Nemotron 3 Super 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-4o mini or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 20 months after GPT-4o 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.