Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. 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; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. 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
Cost model: NVIDIA Nemotron 3 Super ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Gemini 3.1 Pro holds 2× more — 2M (~3,000 pages) vs 1M (~1,500 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 days (released March 11, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 3.1 Pro
NVIDIA Nemotron 3 Super
Provider
Google (US)
NVIDIA (US)
Released
February 19, 2026
March 11, 2026
Context window
2M (~3,000 pages)
1M (~1,500 pages)
Price (in/out)
$2/$12 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
60.47%
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Long video and document analysis: Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
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.
Lowest cost at scale: NVIDIA Nemotron 3 Super — 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: Gemini 3.1 Pro — Its 2M window is about 2× larger, fitting roughly 3,000 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 Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 3.1 Pro — Larger 2M 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; Gemini 3.1 Pro is API-only.
Anyone whose priority is largest mainstream production context (2m): Gemini 3.1 Pro — 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.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs are real: long-context recall drops sharply past 256K, and premium price per token. At $2 in / $12 out per million tokens, it sits in the mid 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. Gemini 3.1 Pro 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 Gemini 3.1 Pro or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for Gemini 3.1 Pro, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.1 Pro leans toward largest mainstream production context (2m) 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, Gemini 3.1 Pro 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 Gemini 3.1 Pro is API-metered at $2/$12 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?
Gemini 3.1 Pro — 2M vs 1M, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, 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, Gemini 3.1 Pro or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 20 days after Gemini 3.1 Pro.
Gemini 3.1 Pro vs NVIDIA Nemotron 3 Super
Google · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. 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; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. 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 Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Gemini 3.1 Pro holds 2× more — 2M (~3,000 pages) vs 1M (~1,500 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 days (released March 11, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 3.1 Pro
NVIDIA Nemotron 3 Super
Provider
Google (US)
NVIDIA (US)
Released
February 19, 2026
March 11, 2026
Context window
2M (~3,000 pages)
1M (~1,500 pages)
Price (in/out)
$2/$12 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
60.47%
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Long video and document analysis
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
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.
Lowest cost at scale
NVIDIA Nemotron 3 Super
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
Gemini 3.1 Pro
Its 2M window is about 2× larger, fitting roughly 3,000 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 Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 3.1 Pro
Larger 2M 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; Gemini 3.1 Pro is API-only.
Anyone whose priority is largest mainstream production context (2m)
→ Gemini 3.1 Pro
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.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs are real: long-context recall drops sharply past 256K, and premium price per token. At $2 in / $12 out per million tokens, it sits in the mid 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. Gemini 3.1 Pro 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 Gemini 3.1 Pro 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 Gemini 3.1 Pro or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for Gemini 3.1 Pro, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.1 Pro leans toward largest mainstream production context (2m) 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, Gemini 3.1 Pro 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 Gemini 3.1 Pro is API-metered at $2/$12 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?
Gemini 3.1 Pro — 2M vs 1M, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, 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, Gemini 3.1 Pro or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 20 days after Gemini 3.1 Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.