// head_to_head
Nemotron 3.5 Super VL vs GLM 5.3 Flash
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.
Nemotron 3.5 Super VL and GLM 5.3 Flash are priced within ~10% of each other.
Nemotron 3.5 Super VL 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 3.5 Super VL
- Blended / 1M
- $0.237
- Context
- 1.0M
- Released
- Oct 15, 2026
- Overall score
- Not evaluated
z-ai
GLM 5.3 Flash
- Blended / 1M
- $0.237
- Context
- 1.0M
- Released
- Aug 26, 2026
- Overall score
- 70.6
Specs and pricing
| Metric | Nemotron 3.5 Super VL | GLM 5.3 Flash |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 70.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1008 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.237 | $0.237 |
| Input price / 1M | $0.150 | $0.150 |
| Output price / 1M | $0.500 | $0.500 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $0.030 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 236K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Nemotron 3.5 Super VL on top, GLM 5.3 Flash 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 3.5 Super VL | GLM 5.3 Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $66.64/mo | $67.36/mo |
| RAG assistant 8K in / 600 out × 100K requests | $98.00/mo | $102.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $87.20/mo | $92.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $181.00/mo | $181.50/mo |
| Bulk classification 500 in / 20 out × 5M requests | $360.00/mo | $365.00/mo |
Nemotron 3.5 Super VL vs GLM 5.3 Flash FAQ
Which is better, Nemotron 3.5 Super VL or GLM 5.3 Flash?
Nemotron 3.5 Super VL and GLM 5.3 Flash are priced within ~10% of each other. Nemotron 3.5 Super VL 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 3.5 Super VL cheaper than GLM 5.3 Flash?
They cost about the same. Both land near $0.237 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Nemotron 3.5 Super VL or GLM 5.3 Flash have a bigger context window?
They are effectively the same — 1.0M for Nemotron 3.5 Super VL and 1.0M for GLM 5.3 Flash.
Do Nemotron 3.5 Super VL and GLM 5.3 Flash support prompt caching?
Both publish a cached-input rate: $0.020 per million for Nemotron 3.5 Super VL and $0.030 for GLM 5.3 Flash, against full input rates of $0.150 and $0.150. 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 2026-09-02, 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.