Pick Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1) or cost-efficient workhorse performance beating larger models at same price as 3.7 flash. 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 3.8 Flash if you want a managed API.
Gemini 3.8 Flash (Google DeepMind) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. Gemini 3.8 Flash is google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. 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 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 3.8 Flash is API-metered at $0.75/$3.75 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: both advertise 1M tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Gemini 3.8 Flash is the newer model by about 6 months (released September 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 3.8 Flash
NVIDIA Nemotron 3 Super
Provider
Google DeepMind (US)
NVIDIA (US)
Released
September 2, 2026
March 11, 2026
Context window
1M tokens (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.75/$3.75 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video
text, code
SWE-Bench Verified
Not published
60.47%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic coding (DeepSWE v1.1): Gemini 3.8 Flash — Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research — and it is the newer of the two.
Cost-efficient workhorse performance beating larger models at same price as 3.7 Flash: Gemini 3.8 Flash — Gemini 3.8 Flash lists cost-efficient workhorse performance beating larger models at same price as 3.7 Flash among its strengths; NVIDIA Nemotron 3 Super does not.
Vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%): Gemini 3.8 Flash — Gemini 3.8 Flash lists vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%) among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B): NVIDIA Nemotron 3 Super — Gemini 3.8 Flash is comparatively weak here — hLE-Verified score (54.9%) trails top frontier reasoning models
1M-token context with strong long-context retrieval (91.6% RULER @ 1M): 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 its weights are open while Gemini 3.8 Flash is API-only.
Strong math reasoning (90.21% AIME 2025): NVIDIA Nemotron 3 Super — NVIDIA Nemotron 3 Super lists strong math reasoning (90.21% AIME 2025) among its strengths; Gemini 3.8 Flash does not.
Lowest cost at scale: NVIDIA Nemotron 3 Super — Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.8 Flash's $0.75/$3.75 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: NVIDIA Nemotron 3 Super — At Open weight (self-host / free) it undercuts Gemini 3.8 Flash, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs: NVIDIA Nemotron 3 Super — Open weights let you run it on your own hardware; Gemini 3.8 Flash is API-only.
Anyone whose priority is long-horizon agentic coding (deepswe v1.1): Gemini 3.8 Flash — 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 3.8 Flash: where it fits
Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Released September 2, 2026 by Google DeepMind, it is built for long-horizon agentic coding (DeepSWE v1.1), cost-efficient workhorse performance beating larger models at same price as 3.7 Flash, vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%), and prompt-injection robustness (Gray Swan benchmark).
Its trade-offs are real: still a mid-tier 'Flash' model, not Google's frontier flagship (which remains unreleased), introductory price doubles on January 1, 2027, hLE-Verified score (54.9%) trails top frontier reasoning models, and built on the same base model as Gemini 3.7 Flash (a post-training update, not a freshly pretrained model). At $0.75 in / $3.75 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. Gemini 3.8 Flash 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 3.8 Flash or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for Gemini 3.8 Flash, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.8 Flash leans toward long-horizon agentic coding (deepswe v1.1) 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 3.8 Flash 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 3.8 Flash is API-metered at $0.75/$3.75 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?
Both advertise 1M tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.8 Flash and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.8 Flash, 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 3.8 Flash or NVIDIA Nemotron 3 Super?
Gemini 3.8 Flash — released September 2, 2026, about 6 months after NVIDIA Nemotron 3 Super.
Gemini 3.8 Flash vs NVIDIA Nemotron 3 Super
Google DeepMind · US | NVIDIA · US · Updated June 2026
Quick verdict
Pick Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1) or cost-efficient workhorse performance beating larger models at same price as 3.7 flash. 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 3.8 Flash if you want a managed API.
Gemini 3.8 Flash (Google DeepMind) and NVIDIA Nemotron 3 Super (NVIDIA) are two of the models people most often weigh against each other in 2026. Gemini 3.8 Flash is google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. 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 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 3.8 Flash is API-metered at $0.75/$3.75 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: both advertise 1M tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Gemini 3.8 Flash is the newer model by about 6 months (released September 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 3.8 Flash
NVIDIA Nemotron 3 Super
Provider
Google DeepMind (US)
NVIDIA (US)
Released
September 2, 2026
March 11, 2026
Context window
1M tokens (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.75/$3.75 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video
text, code
SWE-Bench Verified
Not published
60.47%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic coding (DeepSWE v1.1)
Gemini 3.8 Flash
Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research — and it is the newer of the two.
Cost-efficient workhorse performance beating larger models at same price as 3.7 Flash
Gemini 3.8 Flash
Gemini 3.8 Flash lists cost-efficient workhorse performance beating larger models at same price as 3.7 Flash among its strengths; NVIDIA Nemotron 3 Super does not.
Vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%)
Gemini 3.8 Flash
Gemini 3.8 Flash lists vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%) among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
Gemini 3.8 Flash is comparatively weak here — hLE-Verified score (54.9%) trails top frontier reasoning models
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
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 its weights are open while Gemini 3.8 Flash is API-only.
Strong math reasoning (90.21% AIME 2025)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists strong math reasoning (90.21% AIME 2025) among its strengths; Gemini 3.8 Flash does not.
Lowest cost at scale
NVIDIA Nemotron 3 Super
Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.8 Flash's $0.75/$3.75 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ NVIDIA Nemotron 3 Super
At Open weight (self-host / free) it undercuts Gemini 3.8 Flash, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ NVIDIA Nemotron 3 Super
Open weights let you run it on your own hardware; Gemini 3.8 Flash is API-only.
Anyone whose priority is long-horizon agentic coding (deepswe v1.1)
→ Gemini 3.8 Flash
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 3.8 Flash: where it fits
Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Released September 2, 2026 by Google DeepMind, it is built for long-horizon agentic coding (DeepSWE v1.1), cost-efficient workhorse performance beating larger models at same price as 3.7 Flash, vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%), and prompt-injection robustness (Gray Swan benchmark).
Its trade-offs are real: still a mid-tier 'Flash' model, not Google's frontier flagship (which remains unreleased), introductory price doubles on January 1, 2027, hLE-Verified score (54.9%) trails top frontier reasoning models, and built on the same base model as Gemini 3.7 Flash (a post-training update, not a freshly pretrained model). At $0.75 in / $3.75 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. Gemini 3.8 Flash 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 3.8 Flash 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 3.8 Flash or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for Gemini 3.8 Flash, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.8 Flash leans toward long-horizon agentic coding (deepswe v1.1) 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 3.8 Flash 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 3.8 Flash is API-metered at $0.75/$3.75 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?
Both advertise 1M tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.8 Flash and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.8 Flash, 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 3.8 Flash or NVIDIA Nemotron 3 Super?
Gemini 3.8 Flash — released September 2, 2026, about 6 months after NVIDIA Nemotron 3 Super.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.