// head_to_head
Nemotron 3 Ultra vs GLM 5
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
GLM 5 is the cheaper of the two; neither can be ranked on quality here.
GLM 5 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
nvidia
Nemotron 3 Ultra
- Blended / 1M
- $1.05
- Context
- 262K
- Released
- Jun 4, 2026
- Overall score
- 67.4
z-ai
GLM 5
- Blended / 1M
- $0.930
- Context
- 205K
- Released
- Feb 11, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Nemotron 3 Ultra | GLM 5 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 67.4 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.2118 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.05 | $0.930win |
| Input price / 1M | $0.600 | $0.600 |
| Output price / 1M | $2.40 | $1.92win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.120 | $0.120 |
| Context window | 262Kwin | 205K |
| Max output tokens | 183Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Nemotron 3 Ultra on top, GLM 5 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Nemotron 3 Ultra | GLM 5 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $301.44/mo | $263.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $432.00/mo | $403.20/mo |
| Coding agent 40K in / 4K out × 20K requests | $403.20/mo | $364.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $756.00/mo | $720.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,500/mo | $1,452/mo |
Which should you pick?
You need to fit large documents in one call
Nemotron 3 Ultra
Wider context window — 262K against 205K.
You are cost-constrained
GLM 5
Cheaper on blended list price at $0.930 per million tokens.
Nemotron 3 Ultra vs GLM 5 FAQ
Which is better, Nemotron 3 Ultra or GLM 5?
GLM 5 is the cheaper of the two; neither can be ranked on quality here. GLM 5 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Nemotron 3 Ultra cheaper than GLM 5?
GLM 5 is cheaper. On a 3:1 input:output blend, Nemotron 3 Ultra lists at $1.05 per million tokens and GLM 5 at $0.930 — GLM 5 is 13% cheaper. Input and output are priced separately — Nemotron 3 Ultra charges $0.600 in and $2.40 out, GLM 5 charges $0.600 and $1.92 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Nemotron 3 Ultra or GLM 5 have a bigger context window?
Nemotron 3 Ultra has the larger context window: 262K for Nemotron 3 Ultra against 205K for GLM 5. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Nemotron 3 Ultra and GLM 5 support prompt caching?
Both publish a cached-input rate: $0.120 per million for Nemotron 3 Ultra and $0.120 for GLM 5, against full input rates of $0.600 and $0.600. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.