Pick MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) or independently ranked 14th of 97 on the artificial analysis intelligence index. 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). On a tight budget at scale, NVIDIA Nemotron 3 Super is the value pick.
MiniMax M2.7 (MiniMax, China) 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. MiniMax M2.7 is a cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. 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 context window — each quantified below from the models' real specs.
Key differences
Context window: NVIDIA Nemotron 3 Super holds 4.9× more — 1M (~1,500 pages) vs 205K (~307 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
MiniMax M2.7
NVIDIA Nemotron 3 Super
Provider
MiniMax (China)
NVIDIA (US)
Released
March 18, 2026
March 11, 2026
Context window
205K (~307 pages)
1M (~1,500 pages)
Price (in/out)
$0.3/$1.2 per 1M tokens
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
Agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported): MiniMax M2.7 — A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it is the newer of the two.
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index: MiniMax M2.7 — MiniMax M2.7 lists independently ranked 14th of 97 on the Artificial Analysis Intelligence Index among its strengths; NVIDIA Nemotron 3 Super does not.
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware: MiniMax M2.7 — NVIDIA Nemotron 3 Super is comparatively weak here — requires roughly 8x H100-80GB GPUs to self-host at BF16
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 4.9× more than MiniMax M2.7's 205K 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; MiniMax M2.7 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 MiniMax M2.7's $0.3/$1.2 per 1M tokens.
Largest single-prompt input: NVIDIA Nemotron 3 Super — Its 1M window is about 4.9× larger than MiniMax M2.7's 205K, fitting roughly 1,500 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 MiniMax M2.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: NVIDIA Nemotron 3 Super — Larger 1M window fits more in one prompt.
Anyone whose priority is agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported): MiniMax M2.7 — 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 MiniMax M2.7 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
MiniMax M2.7: where it fits
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. Released March 18, 2026 by MiniMax, it is built for agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported), independently ranked 14th of 97 on the Artificial Analysis Intelligence Index, sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware, and served by five separate hosts at uniform pricing, so there is no provider lock-in.
Its trade-offs are real: open weights but a NON-COMMERCIAL licence — commercial use requires prior written authorisation from MiniMax, and at least one major tracker still mislabels it as MIT, reports SWE-Bench Pro instead of the standard Verified set, which blocks like-for-like comparison, and already superseded internally by M3, and its 205K context is small against 1M-class rivals. At $0.3 in / $1.2 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
This is less "which is smarter" and more "which ecosystem fits." MiniMax M2.7 (China) and NVIDIA Nemotron 3 Super (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. NVIDIA Nemotron 3 Super is the cheaper option, which matters at volume. 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 MiniMax M2.7 or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for MiniMax M2.7, so the honest test is your own repository — run an identical real bug through both. By design, MiniMax M2.7 leans toward agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) 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, MiniMax M2.7 or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super is cheaper — $0.3/$1.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 205K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiniMax M2.7 and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you MiniMax M2.7, 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, MiniMax M2.7 or NVIDIA Nemotron 3 Super?
MiniMax M2.7 — released March 18, 2026, about 7 days after NVIDIA Nemotron 3 Super.
MiniMax M2.7 vs NVIDIA Nemotron 3 Super
MiniMax · China | NVIDIA · US · Updated June 2026
Quick verdict
Pick MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) or independently ranked 14th of 97 on the artificial analysis intelligence index. 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). On a tight budget at scale, NVIDIA Nemotron 3 Super is the value pick.
MiniMax M2.7 (MiniMax, China) 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. MiniMax M2.7 is a cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. 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 context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: NVIDIA Nemotron 3 Super holds 4.9× more — 1M (~1,500 pages) vs 205K (~307 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
MiniMax M2.7
NVIDIA Nemotron 3 Super
Provider
MiniMax (China)
NVIDIA (US)
Released
March 18, 2026
March 11, 2026
Context window
205K (~307 pages)
1M (~1,500 pages)
Price (in/out)
$0.3/$1.2 per 1M tokens
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
Agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported)
MiniMax M2.7
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it is the newer of the two.
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index
MiniMax M2.7
MiniMax M2.7 lists independently ranked 14th of 97 on the Artificial Analysis Intelligence Index among its strengths; NVIDIA Nemotron 3 Super does not.
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware
MiniMax M2.7
NVIDIA Nemotron 3 Super is comparatively weak here — requires roughly 8x H100-80GB GPUs to self-host at BF16
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 4.9× more than MiniMax M2.7's 205K 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; MiniMax M2.7 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 MiniMax M2.7's $0.3/$1.2 per 1M tokens.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 4.9× larger than MiniMax M2.7's 205K, fitting roughly 1,500 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 MiniMax M2.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ NVIDIA Nemotron 3 Super
Larger 1M window fits more in one prompt.
Anyone whose priority is agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported)
→ MiniMax M2.7
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 MiniMax M2.7
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
MiniMax M2.7: where it fits
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. Released March 18, 2026 by MiniMax, it is built for agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported), independently ranked 14th of 97 on the Artificial Analysis Intelligence Index, sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware, and served by five separate hosts at uniform pricing, so there is no provider lock-in.
Its trade-offs are real: open weights but a NON-COMMERCIAL licence — commercial use requires prior written authorisation from MiniMax, and at least one major tracker still mislabels it as MIT, reports SWE-Bench Pro instead of the standard Verified set, which blocks like-for-like comparison, and already superseded internally by M3, and its 205K context is small against 1M-class rivals. At $0.3 in / $1.2 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
This is less "which is smarter" and more "which ecosystem fits." MiniMax M2.7 (China) and NVIDIA Nemotron 3 Super (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. NVIDIA Nemotron 3 Super is the cheaper option, which matters at volume. 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 MiniMax M2.7 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 MiniMax M2.7 or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for MiniMax M2.7, so the honest test is your own repository — run an identical real bug through both. By design, MiniMax M2.7 leans toward agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) 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, MiniMax M2.7 or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super is cheaper — $0.3/$1.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 205K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiniMax M2.7 and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you MiniMax M2.7, 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, MiniMax M2.7 or NVIDIA Nemotron 3 Super?
MiniMax M2.7 — released March 18, 2026, about 7 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.