Muse Spark 1.3 vs NVIDIA Nemotron 3 Ultra

Meta · US  |  NVIDIA · US · Updated June 2026

Quick verdict

Pick Muse Spark 1.3 for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 or deepswe v1.1 long-horizon software engineering (75.4). 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. Choose NVIDIA Nemotron 3 Ultra if you need self-hosting or data privacy; Muse Spark 1.3 if you want a managed API.

Muse Spark 1.3 (Meta) and NVIDIA Nemotron 3 Ultra (NVIDIA) are two of the models people most often weigh against each other in 2026. Muse Spark 1.3 is meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. 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 open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecMuse Spark 1.3NVIDIA Nemotron 3 Ultra
ProviderMeta (US) NVIDIA (US)
ReleasedSeptember 2, 2026 June 4, 2026
Context window1M tokens (~1,500 pages) 1M (~1,500 pages)
Price (in/out)$1.25/$4.25 per 1M tokens Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, video text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1M98.1% Not published

Who wins what

Agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2

Muse Spark 1.3

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review — and it is the newer of the two.

DeepSWE v1.1 long-horizon software engineering (75.4)

Muse Spark 1.3

Muse Spark 1.3 lists deepSWE v1.1 long-horizon software engineering (75.4) among its strengths; NVIDIA Nemotron 3 Ultra does not.

Near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1)

Muse Spark 1.3

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

Open weights make this possible at all — Muse Spark 1.3 is API-only, so it cannot leave the vendor's servers.

Fast, efficient long-horizon agentic reasoning via a hybrid Mamba-Transformer design

NVIDIA Nemotron 3 Ultra

Muse Spark 1.3 is comparatively weak here — strongest 'max' reasoning configuration still gated pending additional safety testing

A fully open release — weights, training data, and recipes under a permissive license

NVIDIA Nemotron 3 Ultra

Muse Spark 1.3 is comparatively weak here — not yet open-weight, despite Meta roadmapping a future Muse Spark weights release

Lowest cost at scale

NVIDIA Nemotron 3 Ultra

Its weights are open, so at volume you pay for your own hardware instead of Muse Spark 1.3's $1.25/$4.25 per 1M tokens.

Which should you pick?

A cost-sensitive startup shipping high volume

NVIDIA Nemotron 3 Ultra

At Open weight (self-host / free) it undercuts Muse Spark 1.3, and on millions of tokens that margin decides the monthly bill.

A team with data-privacy or self-hosting needs

NVIDIA Nemotron 3 Ultra

Open weights let you run it on your own hardware; Muse Spark 1.3 is API-only.

Anyone whose priority is agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2

Muse Spark 1.3

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.

Muse Spark 1.3: where it fits

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. Released September 2, 2026 by Meta, it is built for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2, deepSWE v1.1 long-horizon software engineering (75.4), near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1), and ranks third overall on the Artificial Analysis Intelligence Index (score 61, xhigh variant; the limited-preview max variant scores 62) - behind only Claude Fable 5.1 and Claude Opus 5.

Its trade-offs are real: strongest 'max' reasoning configuration still gated pending additional safety testing, not yet open-weight, despite Meta roadmapping a future Muse Spark weights release, and no official SWE-bench Verified score published. At $1.25 in / $4.25 out per million tokens, it sits in the mid 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

The defining split here is open vs. closed. NVIDIA Nemotron 3 Ultra gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.3 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Muse Spark 1.3 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.

See pricing

Frequently asked questions

Is Muse Spark 1.3 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, Muse Spark 1.3 leans toward agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 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, Muse Spark 1.3 or NVIDIA Nemotron 3 Ultra?

NVIDIA Nemotron 3 Ultra is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.3 is API-metered at $1.25/$4.25 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Both advertise 1M tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Muse Spark 1.3 and NVIDIA Nemotron 3 Ultra together?

Yes — a multi-model platform like LumiChats gives you Muse Spark 1.3, 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, Muse Spark 1.3 or NVIDIA Nemotron 3 Ultra?

Muse Spark 1.3 — released September 2, 2026, about 3 months after NVIDIA Nemotron 3 Ultra.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.