GLM 5.2 vs NVIDIA Nemotron 3 Super
Z.ai · China | NVIDIA · US · Updated June 2026
Quick verdict
Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. 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.
GLM 5.2 (Z.ai, 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. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. 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 price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: GLM 5.2 is the newer model by about 3 months (released June 16, 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 | GLM 5.2 | NVIDIA Nemotron 3 Super |
|---|---|---|
| Provider | Z.ai (China) | NVIDIA (US) |
| Released | June 16, 2026 | March 11, 2026 |
| Context window | 1M (~1,500 pages) | 1M (~1,500 pages) |
| Price (in/out) | $1.4/$4.4 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
Long-horizon agentic coding
GLM 5.2
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap — and it is the newer of the two.
Project-level software engineering
GLM 5.2
GLM 5.2 lists project-level software engineering among its strengths; NVIDIA Nemotron 3 Super does not.
Tool use across long-running tasks
GLM 5.2
GLM 5.2 lists tool use across long-running tasks among its strengths; NVIDIA Nemotron 3 Super does not.
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists high-throughput agentic reasoning (up to 2.2x GPT-OSS-120B) among its strengths; GLM 5.2 does not.
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists 1M-token context with strong long-context retrieval (91.6% RULER @ 1M) among its strengths; GLM 5.2 does not.
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; GLM 5.2 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 GLM 5.2's $1.4/$4.4 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ NVIDIA Nemotron 3 Super
At Open weight (self-host / free) it undercuts GLM 5.2, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is long-horizon agentic coding
→ GLM 5.2
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 GLM 5.2
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5.2: where it fits
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Released June 16, 2026 by Z.ai, it is built for long-horizon agentic coding, project-level software engineering, tool use across long-running tasks, and tops the open-weight intelligence index (SWE-bench Pro 62.1).
Its trade-offs are real: text-only — no native multimodal input, and new release with a limited third-party track record. At $1.4 in / $4.4 out per million tokens, it sits in the mid 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." GLM 5.2 (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 GLM 5.2 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.
See pricingFrequently asked questions
Is GLM 5.2 or NVIDIA Nemotron 3 Super better for coding?
Public SWE-Bench figures are not available for GLM 5.2, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.2 leans toward long-horizon agentic coding 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, GLM 5.2 or NVIDIA Nemotron 3 Super?
NVIDIA Nemotron 3 Super is cheaper — $1.4/$4.4 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GLM 5.2 and NVIDIA Nemotron 3 Super together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, 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, GLM 5.2 or NVIDIA Nemotron 3 Super?
GLM 5.2 — released June 16, 2026, about 3 months after NVIDIA Nemotron 3 Super.
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.