Models / nemotron-3-5-lightning
Nemotron 3.5 Lightning
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 3.5 Lightning is ranked by its one configuration: the 26th-highest score of 26 models, its interval overlaps no other model’s. 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 · NVFP4 · thinking onhighest score | 40 | 20%95% CI 16–24 | 6.6k5k · 14k | 0% | 447 s |
- 95% interval
- API board median
Show the numbers
| Suite | c@3 95% CI | API median | Tasks |
|---|---|---|---|
| Apex | 15%95% CI 5–36 | 90% | 20 |
| Salesforce APIs | 33%95% CI 16–56 | 72% | 18 |
| CI/CD | 18%95% CI 6–41 | 88% | 17 |
| Salesforce CLI | 10%95% CI 3–30 | 90% | 20 |
| Salesforce docs | 0%95% CI 0–18 | 29% | 17 |
| fflib Enterprise Patterns | 6%95% CI 1–26 | 61% | 18 |
| Flow | 11%95% CI 3–33 | 69% | 18 |
| Governor limits & pushback | 47%95% CI 27–68 | 89% | 19 |
| Lightning Web Components | 11%95% CI 3–33 | 100% | 18 |
| Nonprofit Success Pack | 39%95% CI 20–61 | 81% | 18 |
| Packaging | 11%95% CI 3–33 | 69% | 18 |
| Permissions & access | 7%95% CI 1–30 | 87% | 15 |
| Scratch org definitions | 6%95% CI 1–26 | 50% | 18 |
| SOQL | 65%95% CI 43–82 | 100% | 20 |
| Trigger Actions Framework | 22%95% CI 9–45 | 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