Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). 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).
gpt-oss-120b (OpenAI) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. 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 context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Context window: NVIDIA Nemotron 3 Super holds 7.6× more — 1M (~1,500 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Coding: a near dead heat on SWE-Bench Verified (62.4% vs 60.47%) — both are top-tier coders.
Recency: NVIDIA Nemotron 3 Super is the newer model by about 7 months (released March 11, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
gpt-oss-120b
NVIDIA Nemotron 3 Super
Provider
OpenAI (US)
NVIDIA (US)
Released
August 5, 2025
March 11, 2026
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
62.4%
60.47%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4: gpt-oss-120b — A core design strength of gpt-oss-120b.
Configurable reasoning depth (low/medium/high): gpt-oss-120b — A core design strength of gpt-oss-120b.
Agentic tool use, function calling, and code execution: gpt-oss-120b — A core design strength of gpt-oss-120b.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B): NVIDIA Nemotron 3 Super — A core design strength of NVIDIA Nemotron 3 Super.
1M-token context with strong long-context retrieval (91.6% RULER @ 1M): NVIDIA Nemotron 3 Super — A core design strength of NVIDIA Nemotron 3 Super.
Strong math reasoning (90.21% AIME 2025): NVIDIA Nemotron 3 Super — A core design strength of NVIDIA Nemotron 3 Super.
Largest single-prompt input: NVIDIA Nemotron 3 Super — Its 1M window is about 7.6× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: NVIDIA Nemotron 3 Super — Larger 1M window fits more in one prompt.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4: gpt-oss-120b — 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-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
gpt-oss-120b and NVIDIA Nemotron 3 Super overlap enough that the right pick depends on your specific job. NVIDIA Nemotron 3 Super holds the larger context; and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, NVIDIA Nemotron 3 Super for high-throughput agentic reasoning (up to 2.2x gpt-oss-120b). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is gpt-oss-120b or NVIDIA Nemotron 3 Super better for coding?
On SWE-Bench Verified, gpt-oss-120b scores 62.4% and NVIDIA Nemotron 3 Super scores 60.47% — effectively a tie, so pick on price and ecosystem.
Which is cheaper, gpt-oss-120b or NVIDIA Nemotron 3 Super?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, 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-oss-120b or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 7 months after gpt-oss-120b.
gpt-oss-120b vs NVIDIA Nemotron 3 Super
OpenAI · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). 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).
gpt-oss-120b (OpenAI) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. 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 context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: NVIDIA Nemotron 3 Super holds 7.6× more — 1M (~1,500 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Coding: a near dead heat on SWE-Bench Verified (62.4% vs 60.47%) — both are top-tier coders.
▸Recency: NVIDIA Nemotron 3 Super is the newer model by about 7 months (released March 11, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
gpt-oss-120b
NVIDIA Nemotron 3 Super
Provider
OpenAI (US)
NVIDIA (US)
Released
August 5, 2025
March 11, 2026
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
62.4%
60.47%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
A core design strength of gpt-oss-120b.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
A core design strength of gpt-oss-120b.
Agentic tool use, function calling, and code execution
gpt-oss-120b
A core design strength of gpt-oss-120b.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
A core design strength of NVIDIA Nemotron 3 Super.
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
A core design strength of NVIDIA Nemotron 3 Super.
Strong math reasoning (90.21% AIME 2025)
NVIDIA Nemotron 3 Super
A core design strength of NVIDIA Nemotron 3 Super.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 7.6× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ NVIDIA Nemotron 3 Super
Larger 1M window fits more in one prompt.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
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-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
gpt-oss-120b and NVIDIA Nemotron 3 Super overlap enough that the right pick depends on your specific job. NVIDIA Nemotron 3 Super holds the larger context; and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, NVIDIA Nemotron 3 Super for high-throughput agentic reasoning (up to 2.2x gpt-oss-120b). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both gpt-oss-120b 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.
Is gpt-oss-120b or NVIDIA Nemotron 3 Super better for coding?
On SWE-Bench Verified, gpt-oss-120b scores 62.4% and NVIDIA Nemotron 3 Super scores 60.47% — effectively a tie, so pick on price and ecosystem.
Which is cheaper, gpt-oss-120b or NVIDIA Nemotron 3 Super?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, 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-oss-120b or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 7 months after gpt-oss-120b.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.