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 Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
NVIDIA Nemotron 3 Super (NVIDIA, US) and Qwen3.6 35B A3B (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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. 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 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.
Coding: Qwen3.6 35B A3B leads SWE-Bench Verified by 12.9 points (60.47% vs 73.4%) — a real edge on hard, real-world software tasks.
Recency: Qwen3.6 35B A3B is the newer model by about 36 days (released April 16, 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
Qwen3.6 35B A3B
Provider
NVIDIA (US)
Alibaba (China)
Released
March 11, 2026
April 16, 2026
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, image, code
SWE-Bench Verified
60.47%
73.4%
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 — 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.
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 Qwen3.6 35B A3B's 256K in a single prompt.
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; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost: Qwen3.6 35B A3B — A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it leads SWE-Bench Verified 73.4% to 60.47%.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU: Qwen3.6 35B A3B — NVIDIA Nemotron 3 Super is comparatively weak here — requires roughly 8x H100-80GB GPUs to self-host at BF16
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN: Qwen3.6 35B A3B — A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it is the newer of the two.
Largest single-prompt input: NVIDIA Nemotron 3 Super — Its 1M window is about 3.8× larger than Qwen3.6 35B A3B'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 extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost: Qwen3.6 35B A3B — That is its strongest area.
An enterprise with regional data-residency rules: NVIDIA Nemotron 3 Super or Qwen3.6 35B A3B — 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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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 Qwen3.6 35B A3B (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 Qwen3.6 35B A3B better for coding?
On SWE-Bench Verified, NVIDIA Nemotron 3 Super scores 60.47% and Qwen3.6 35B A3B scores 73.4% — Qwen3.6 35B A3B has the measurable edge.
Which is cheaper, NVIDIA Nemotron 3 Super or Qwen3.6 35B A3B?
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 Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you NVIDIA Nemotron 3 Super, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B?
Qwen3.6 35B A3B — released April 16, 2026, about 36 days after NVIDIA Nemotron 3 Super.
NVIDIA Nemotron 3 Super vs Qwen3.6 35B A3B
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 Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
NVIDIA Nemotron 3 Super (NVIDIA, US) and Qwen3.6 35B A3B (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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. 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 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.
▸Coding: Qwen3.6 35B A3B leads SWE-Bench Verified by 12.9 points (60.47% vs 73.4%) — a real edge on hard, real-world software tasks.
▸Recency: Qwen3.6 35B A3B is the newer model by about 36 days (released April 16, 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
Qwen3.6 35B A3B
Provider
NVIDIA (US)
Alibaba (China)
Released
March 11, 2026
April 16, 2026
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, image, code
SWE-Bench Verified
60.47%
73.4%
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
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.
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 Qwen3.6 35B A3B's 256K in a single prompt.
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; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost
Qwen3.6 35B A3B
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it leads SWE-Bench Verified 73.4% to 60.47%.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU
Qwen3.6 35B A3B
NVIDIA Nemotron 3 Super is comparatively weak here — requires roughly 8x H100-80GB GPUs to self-host at BF16
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN
Qwen3.6 35B A3B
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it is the newer of the two.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 3.8× larger than Qwen3.6 35B A3B'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 extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost
→ Qwen3.6 35B A3B
That is its strongest area.
An enterprise with regional data-residency rules
→ NVIDIA Nemotron 3 Super or Qwen3.6 35B A3B
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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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 Qwen3.6 35B A3B (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 Qwen3.6 35B A3B 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 Qwen3.6 35B A3B better for coding?
On SWE-Bench Verified, NVIDIA Nemotron 3 Super scores 60.47% and Qwen3.6 35B A3B scores 73.4% — Qwen3.6 35B A3B has the measurable edge.
Which is cheaper, NVIDIA Nemotron 3 Super or Qwen3.6 35B A3B?
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 Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you NVIDIA Nemotron 3 Super, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B?
Qwen3.6 35B A3B — released April 16, 2026, about 36 days 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.