Pick Jamba2 Mini for a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant or fast, efficient long-context inference tuned for enterprise workloads. 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).
Jamba2 Mini (AI21 Labs, Israel) and NVIDIA Nemotron 3 Super (NVIDIA, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Jamba2 Mini is aI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. 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. 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 2 months (released March 11, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a Israel-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Jamba2 Mini
NVIDIA Nemotron 3 Super
Provider
AI21 Labs (Israel)
NVIDIA (US)
Released
January 8, 2026
March 11, 2026
Context window
256K (~393 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
Not published
60.47%
MRCR v2 @ 1M
Not published
Not published
Who wins what
A genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant: Jamba2 Mini — Jamba2 Mini lists a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant among its strengths; NVIDIA Nemotron 3 Super does not.
Fast, efficient long-context inference tuned for enterprise workloads: Jamba2 Mini — Jamba2 Mini lists fast, efficient long-context inference tuned for enterprise workloads among its strengths; NVIDIA Nemotron 3 Super does not.
Open weights (Apache 2.0) - free to self-host: Jamba2 Mini — Jamba2 Mini lists open weights (Apache 2.0) - free to self-host among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B): NVIDIA Nemotron 3 Super — Jamba2 Mini is comparatively weak here — tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards
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 Jamba2 Mini'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.
Largest single-prompt input: NVIDIA Nemotron 3 Super — Its 1M window is about 3.8× larger than Jamba2 Mini'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 a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant: Jamba2 Mini — 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.
An enterprise with regional data-residency rules: NVIDIA Nemotron 3 Super or Jamba2 Mini — Origin (Israel vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Jamba2 Mini: where it fits
AI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. Released January 8, 2026 by AI21 Labs, it is built for a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant, fast, efficient long-context inference tuned for enterprise workloads, open weights (Apache 2.0) - free to self-host, and a 52B-total/12B-active MoE design that's cheap to run relative to its capability.
Its trade-offs are real: tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards, no official per-token API price published - self-host or use AI21's platform preview, and newer architecture family means less third-party tooling support than mainstream transformers. 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
This is less "which is smarter" and more "which ecosystem fits." Jamba2 Mini (Israel) and NVIDIA Nemotron 3 Super (US) 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 Jamba2 Mini or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for Jamba2 Mini, so the honest test is your own repository — run an identical real bug through both. By design, Jamba2 Mini leans toward a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant 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, Jamba2 Mini 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 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Jamba2 Mini and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Jamba2 Mini, 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, Jamba2 Mini or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 2 months after Jamba2 Mini.
Jamba2 Mini vs NVIDIA Nemotron 3 Super
AI21 Labs · Israel | NVIDIA · US · Updated June 2026
Quick verdict
Pick Jamba2 Mini for a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant or fast, efficient long-context inference tuned for enterprise workloads. 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).
Jamba2 Mini (AI21 Labs, Israel) and NVIDIA Nemotron 3 Super (NVIDIA, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Jamba2 Mini is aI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. 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. 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 2 months (released March 11, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a Israel-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Jamba2 Mini
NVIDIA Nemotron 3 Super
Provider
AI21 Labs (Israel)
NVIDIA (US)
Released
January 8, 2026
March 11, 2026
Context window
256K (~393 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
Not published
60.47%
MRCR v2 @ 1M
Not published
Not published
Who wins what
A genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant
Jamba2 Mini
Jamba2 Mini lists a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant among its strengths; NVIDIA Nemotron 3 Super does not.
Fast, efficient long-context inference tuned for enterprise workloads
Jamba2 Mini
Jamba2 Mini lists fast, efficient long-context inference tuned for enterprise workloads among its strengths; NVIDIA Nemotron 3 Super does not.
Open weights (Apache 2.0) - free to self-host
Jamba2 Mini
Jamba2 Mini lists open weights (Apache 2.0) - free to self-host among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
Jamba2 Mini is comparatively weak here — tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards
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 Jamba2 Mini'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.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 3.8× larger than Jamba2 Mini'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 a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant
→ Jamba2 Mini
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.
An enterprise with regional data-residency rules
→ NVIDIA Nemotron 3 Super or Jamba2 Mini
Origin (Israel vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Jamba2 Mini: where it fits
AI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. Released January 8, 2026 by AI21 Labs, it is built for a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant, fast, efficient long-context inference tuned for enterprise workloads, open weights (Apache 2.0) - free to self-host, and a 52B-total/12B-active MoE design that's cheap to run relative to its capability.
Its trade-offs are real: tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards, no official per-token API price published - self-host or use AI21's platform preview, and newer architecture family means less third-party tooling support than mainstream transformers. 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
This is less "which is smarter" and more "which ecosystem fits." Jamba2 Mini (Israel) and NVIDIA Nemotron 3 Super (US) 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 Jamba2 Mini 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 Jamba2 Mini or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for Jamba2 Mini, so the honest test is your own repository — run an identical real bug through both. By design, Jamba2 Mini leans toward a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant 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, Jamba2 Mini 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 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Jamba2 Mini and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you Jamba2 Mini, 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, Jamba2 Mini or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super — released March 11, 2026, about 2 months after Jamba2 Mini.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.