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). Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench).
NVIDIA Nemotron 3 Super (NVIDIA, US) and Qwen3 235B A22B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: NVIDIA Nemotron 3 Super holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 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 8 months (released March 11, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
NVIDIA Nemotron 3 Super
Qwen3 235B A22B
Provider
NVIDIA (US)
Alibaba (China)
Released
March 11, 2026
July 21, 2025
Context window
1M (~1,500 pages)
256K (~393 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
60.47%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B): NVIDIA Nemotron 3 Super — Qwen3 235B A22B is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
1M-token context with strong long-context retrieval (91.6% RULER @ 1M): NVIDIA Nemotron 3 Super — Its 1M window holds about 3.8× more than Qwen3 235B A22B's 256K 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.
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B — Qwen3 235B A22B lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; NVIDIA Nemotron 3 Super does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B — Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; NVIDIA Nemotron 3 Super does not.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B — Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; NVIDIA Nemotron 3 Super does not.
Largest single-prompt input: NVIDIA Nemotron 3 Super — Its 1M window is about 3.8× larger than Qwen3 235B A22B's 256K, 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 high-throughput agentic reasoning (up to 2.2x gpt-oss-120b): NVIDIA Nemotron 3 Super — It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B — That is its strongest area.
An enterprise with regional data-residency rules: NVIDIA Nemotron 3 Super or Qwen3 235B A22B — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." NVIDIA Nemotron 3 Super (US) and Qwen3 235B A22B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is NVIDIA Nemotron 3 Super or Qwen3 235B A22B better for coding?
Public SWE-Bench figures are not available for Qwen3 235B A22B, so the honest test is your own repository — run an identical real bug through both. By design, NVIDIA Nemotron 3 Super leans toward high-throughput agentic reasoning (up to 2.2x gpt-oss-120b) while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, NVIDIA Nemotron 3 Super or Qwen3 235B A22B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both NVIDIA Nemotron 3 Super and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you NVIDIA Nemotron 3 Super, Qwen3 235B A22B 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, NVIDIA Nemotron 3 Super or Qwen3 235B A22B?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 8 months after Qwen3 235B A22B.
NVIDIA Nemotron 3 Super vs Qwen3 235B A22B
NVIDIA · US | Alibaba · China · Updated June 2026
Quick verdict
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). Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench).
NVIDIA Nemotron 3 Super (NVIDIA, US) and Qwen3 235B A22B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: NVIDIA Nemotron 3 Super holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 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 8 months (released March 11, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
NVIDIA Nemotron 3 Super
Qwen3 235B A22B
Provider
NVIDIA (US)
Alibaba (China)
Released
March 11, 2026
July 21, 2025
Context window
1M (~1,500 pages)
256K (~393 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
60.47%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
Qwen3 235B A22B is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
Its 1M window holds about 3.8× more than Qwen3 235B A22B's 256K 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.
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B
Qwen3 235B A22B lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; NVIDIA Nemotron 3 Super does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B
Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; NVIDIA Nemotron 3 Super does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B
Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; NVIDIA Nemotron 3 Super does not.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 3.8× larger than Qwen3 235B A22B's 256K, 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 high-throughput agentic reasoning (up to 2.2x gpt-oss-120b)
→ NVIDIA Nemotron 3 Super
It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B
That is its strongest area.
An enterprise with regional data-residency rules
→ NVIDIA Nemotron 3 Super or Qwen3 235B A22B
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." NVIDIA Nemotron 3 Super (US) and Qwen3 235B A22B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both NVIDIA Nemotron 3 Super and Qwen3 235B A22B 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 NVIDIA Nemotron 3 Super or Qwen3 235B A22B better for coding?
Public SWE-Bench figures are not available for Qwen3 235B A22B, so the honest test is your own repository — run an identical real bug through both. By design, NVIDIA Nemotron 3 Super leans toward high-throughput agentic reasoning (up to 2.2x gpt-oss-120b) while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, NVIDIA Nemotron 3 Super or Qwen3 235B A22B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both NVIDIA Nemotron 3 Super and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you NVIDIA Nemotron 3 Super, Qwen3 235B A22B 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, NVIDIA Nemotron 3 Super or Qwen3 235B A22B?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 8 months after Qwen3 235B A22B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.