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 →

20%
within 3 attempts (c@3), 95% CI 16–24 · thinking on · 26th-highest score of 26 models, its interval overlaps no other model’s
6.6k
output tokens per task · median 5k, p90 14k
447 s
median per task, every attempt counted, including network and queueing · 90th percentile 957 s
0%
no answer within the 32,768-token budget
Every configuration of Nemotron 3.5 Lightning on the API board
ConfigurationRank all configsWithin 3 attempts c@3 · 95% CITokens mean · median · p90No answerLatency median per task, every attempt counted
OpenRouter · NVFP4 · thinking onhighest score40
20%95% CI 16–24
6.6k5k · 14k0%447 s
Suites, at its highest-scoring configuration
thinking on · OpenRouter · NVFP4: within 3 attempts (c@3) on each suite with 95% intervals, against the median of the API board's models, each at its highest-scoring configuration.
  • 95% interval
  • API board median
15%, 95% interval 5–36%
33%, 95% interval 16–56%
18%, 95% interval 6–41%
10%, 95% interval 3–30%
0%, 95% interval 0–18%
6%, 95% interval 1–26%
11%, 95% interval 3–33%
47%, 95% interval 27–68%
11%, 95% interval 3–33%
39%, 95% interval 20–61%
11%, 95% interval 3–33%
7%, 95% interval 1–30%
6%, 95% interval 1–26%
65%, 95% interval 43–82%
22%, 95% interval 9–45%
    Show the numbers
    Per-suite scores of Nemotron 3.5 Lightning's highest-scoring configuration on the API board, with the board's median
    Suitec@3 95% CIAPI medianTasks
    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

    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