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 Ultra for the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48) or fast, efficient long-horizon agentic reasoning via a hybrid mamba-transformer design. On a tight budget at scale, NVIDIA Nemotron 3 Ultra is the value pick.
MiniMax M2.7 (MiniMax, China) and NVIDIA Nemotron 3 Ultra (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 Ultra is nVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: NVIDIA Nemotron 3 Ultra 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.
Recency: NVIDIA Nemotron 3 Ultra is the newer model by about 3 months (released June 4, 2026), usually meaning fresher training data and capabilities.
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 Ultra
Provider
MiniMax (China)
NVIDIA (US)
Released
March 18, 2026
June 4, 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
Not published
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 — MiniMax M2.7 lists agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported) among its strengths; NVIDIA Nemotron 3 Ultra does not.
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index: MiniMax M2.7 — NVIDIA Nemotron 3 Ultra is comparatively weak here — trails the best Chinese open models on overall intelligence
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware: MiniMax M2.7 — NVIDIA Nemotron 3 Ultra is comparatively weak here — a 550B mixture-of-experts is heavy to self-host, and the 1M context is rarely served in full
The most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48): NVIDIA Nemotron 3 Ultra — MiniMax M2.7 is comparatively weak here — 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
Fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design: NVIDIA Nemotron 3 Ultra — Its 1M window holds about 4.9× more than MiniMax M2.7's 205K in a single prompt.
A fully open release — weights, training data, and recipes under a permissive license: NVIDIA Nemotron 3 Ultra — NVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents — and it carries the larger 1M context.
Lowest cost at scale: NVIDIA Nemotron 3 Ultra — 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 Ultra — 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 Ultra — 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 Ultra — 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 the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48): NVIDIA Nemotron 3 Ultra — That is its strongest area.
An enterprise with regional data-residency rules: NVIDIA Nemotron 3 Ultra 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 Ultra: where it fits
NVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents. Released June 4, 2026 by NVIDIA, it is built for the most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48), fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design, a fully open release — weights, training data, and recipes under a permissive license, and strong coding for an open model (SWE-Bench Verified in the high 60s).
Its trade-offs: trails the best Chinese open models on overall intelligence, and a 550B mixture-of-experts is heavy to self-host, and the 1M context is rarely served in full. 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 Ultra (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. NVIDIA Nemotron 3 Ultra 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 Ultra better for coding?
Public SWE-Bench figures are not available for either model, 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 Ultra leans toward the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MiniMax M2.7 or NVIDIA Nemotron 3 Ultra?
NVIDIA Nemotron 3 Ultra is cheaper — $0.3/$1.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
NVIDIA Nemotron 3 Ultra — 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 Ultra together?
Yes — a multi-model platform like LumiChats gives you MiniMax M2.7, NVIDIA Nemotron 3 Ultra 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 Ultra?
NVIDIA Nemotron 3 Ultra — released June 4, 2026, about 3 months after MiniMax M2.7.
MiniMax M2.7 vs NVIDIA Nemotron 3 Ultra
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 Ultra for the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48) or fast, efficient long-horizon agentic reasoning via a hybrid mamba-transformer design. On a tight budget at scale, NVIDIA Nemotron 3 Ultra is the value pick.
MiniMax M2.7 (MiniMax, China) and NVIDIA Nemotron 3 Ultra (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 Ultra is nVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents. 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 Ultra 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.
▸Recency: NVIDIA Nemotron 3 Ultra is the newer model by about 3 months (released June 4, 2026), usually meaning fresher training data and capabilities.
▸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 Ultra
Provider
MiniMax (China)
NVIDIA (US)
Released
March 18, 2026
June 4, 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
Not published
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
MiniMax M2.7 lists agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported) among its strengths; NVIDIA Nemotron 3 Ultra does not.
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index
MiniMax M2.7
NVIDIA Nemotron 3 Ultra is comparatively weak here — trails the best Chinese open models on overall intelligence
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware
MiniMax M2.7
NVIDIA Nemotron 3 Ultra is comparatively weak here — a 550B mixture-of-experts is heavy to self-host, and the 1M context is rarely served in full
The most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48)
NVIDIA Nemotron 3 Ultra
MiniMax M2.7 is comparatively weak here — 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
Fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design
NVIDIA Nemotron 3 Ultra
Its 1M window holds about 4.9× more than MiniMax M2.7's 205K in a single prompt.
A fully open release — weights, training data, and recipes under a permissive license
NVIDIA Nemotron 3 Ultra
NVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents — and it carries the larger 1M context.
Lowest cost at scale
NVIDIA Nemotron 3 Ultra
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 Ultra
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 Ultra
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 Ultra
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 the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48)
→ NVIDIA Nemotron 3 Ultra
That is its strongest area.
An enterprise with regional data-residency rules
→ NVIDIA Nemotron 3 Ultra 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 Ultra: where it fits
NVIDIA's open-weight reasoning flagship (about 550B total, 55B active) — the most capable open model from a US lab, built for long-running agents. Released June 4, 2026 by NVIDIA, it is built for the most capable open-weight model from a US lab (Artificial Analysis Intelligence Index of about 48), fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design, a fully open release — weights, training data, and recipes under a permissive license, and strong coding for an open model (SWE-Bench Verified in the high 60s).
Its trade-offs: trails the best Chinese open models on overall intelligence, and a 550B mixture-of-experts is heavy to self-host, and the 1M context is rarely served in full. 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 Ultra (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. NVIDIA Nemotron 3 Ultra 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 Ultra 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 Ultra better for coding?
Public SWE-Bench figures are not available for either model, 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 Ultra leans toward the most capable open-weight model from a us lab (artificial analysis intelligence index of about 48), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MiniMax M2.7 or NVIDIA Nemotron 3 Ultra?
NVIDIA Nemotron 3 Ultra is cheaper — $0.3/$1.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
NVIDIA Nemotron 3 Ultra — 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 Ultra together?
Yes — a multi-model platform like LumiChats gives you MiniMax M2.7, NVIDIA Nemotron 3 Ultra 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 Ultra?
NVIDIA Nemotron 3 Ultra — released June 4, 2026, about 3 months after MiniMax M2.7.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.