Models / nemotron-3-ultra
Nemotron 3 Ultra
NVIDIA's model, with open weights. Forcebench has run it in 1 configuration on the API board. API is models reached through a hosted service; Local, models served on Forcebench’s own hardware.
On the API board
Models reached through a hosted service. Nemotron 3 Ultra is ranked by its one configuration: the 20th-highest score of 26 models, not clearly different from 5 other models. Scores are within 3 attempts (c@3) with 95% intervals; each configuration is compared with the highest-scoring one by whether their 95% intervals overlap. Every configuration on the board → Cost and speed →
| Configuration | Rank all configs | Within 3 attempts c@3 · 95% CI | Tokens mean · median · p90 | No answer | Latency median per task, every attempt counted |
|---|---|---|---|---|---|
| OpenRouter · thinking onhighest score | 33 | 59%95% CI 54–64 | 1.8k1k · 3.7k | 0% | 55 s |
- 95% interval
- API board median
Show the numbers
| Suite | c@3 95% CI | API median | Tasks |
|---|---|---|---|
| Apex | 75%95% CI 53–89 | 90% | 20 |
| Salesforce APIs | 61%95% CI 39–80 | 72% | 18 |
| CI/CD | 71%95% CI 47–87 | 88% | 17 |
| Salesforce CLI | 60%95% CI 39–78 | 90% | 20 |
| Salesforce docs | 24%95% CI 10–47 | 29% | 17 |
| fflib Enterprise Patterns | 33%95% CI 16–56 | 61% | 18 |
| Flow | 22%95% CI 9–45 | 69% | 18 |
| Governor limits & pushback | 79%95% CI 57–91 | 89% | 19 |
| Lightning Web Components | 94%95% CI 74–99 | 100% | 18 |
| Nonprofit Success Pack | 56%95% CI 34–75 | 81% | 18 |
| Packaging | 39%95% CI 20–61 | 69% | 18 |
| Permissions & access | 67%95% CI 42–85 | 87% | 15 |
| Scratch org definitions | 28%95% CI 13–51 | 50% | 18 |
| SOQL | 95%95% CI 76–99 | 100% | 20 |
| Trigger Actions Framework | 83%95% CI 61–94 | 86% | 18 |
In the studies
- Local vs API: where it lands among the configurations of both boards.
- Pushback: whether it pushed back on requests that break at scale.
Private evaluations
Want this for your team?
Azul Labs runs Forcebench privately for two kinds of Salesforce team.
Choosing a model to run yourself?
A private benchmark of your shortlist
Open-weight models at the quantisations and reasoning efforts you could run on infrastructure you control, tested on your kind of Salesforce work, with a written recommendation.
- Private tasks written from your work only ever run on local models; they are never sent to an API.
- Re-testing after any change of model, quantisation or engine is available.
- Typical turnaround is 2–3 weeks; large models take about a day of machine time per configuration.
Building or buying a Salesforce AI tool?
A private evaluation of your tool
Your tool on held-out tasks it has never seen, against API baselines (models reached through a hosted service) and Local ones (served on Forcebench’s own hardware), with a written report on where it holds up and where it fails.
- It can start as a smaller pilot, about 30 held-out tasks with your tool against two baselines, before a full evaluation.
- Repeat evaluations after your releases are available.
- Typical turnaround is 2–3 weeks.
Fixed fee, quoted up front. The quote depends on the number of tasks and the number of models, tools or configurations tested.
Or email leo@azl.au · azl.au