Models / claude-haiku-4-5
Claude Haiku 4.5
Anthropic's model, with closed weights. Forcebench has run it in 2 configurations 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. Claude Haiku 4.5 is ranked by the highest-scoring of its 2 configurations: the 22nd-highest score of 26 models, not clearly different from 3 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 →
- 95% interval
Show the numbers
| Configuration | Rank all configs | Within 3 attempts c@3 · 95% CI | Against the highest by 95% interval overlap | Tokens mean · median · p90 | No answer | Latency median per task, every attempt counted | Cost per task | Cost per solved task |
|---|---|---|---|---|---|---|---|---|
| Anthropic · thinking onhighest score | 35 | 50%95% CI 45–55 | — | 5.1k4.6k · 10k | 0% | 41 s | $0.044 | $0.089 |
| Anthropic · thinking off | 36 | 46%95% CI 41–52 | not clearly different | 1.1k657 · 2.3k | 0% | 8.0 s | $0.015 | $0.032 |
- 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 | 44%95% CI 25–66 | 72% | 18 |
| CI/CD | 65%95% CI 41–83 | 88% | 17 |
| Salesforce CLI | 60%95% CI 39–78 | 90% | 20 |
| Salesforce docs | 0%95% CI 0–18 | 29% | 17 |
| fflib Enterprise Patterns | 44%95% CI 25–66 | 61% | 18 |
| Flow | 17%95% CI 6–39 | 69% | 18 |
| Governor limits & pushback | 79%95% CI 57–91 | 89% | 19 |
| Lightning Web Components | 89%95% CI 67–97 | 100% | 18 |
| Nonprofit Success Pack | 56%95% CI 34–75 | 81% | 18 |
| Packaging | 33%95% CI 16–56 | 69% | 18 |
| Permissions & access | 73%95% CI 48–89 | 87% | 15 |
| Scratch org definitions | 11%95% CI 3–33 | 50% | 18 |
| SOQL | 70%95% CI 48–85 | 100% | 20 |
| Trigger Actions Framework | 28%95% CI 13–51 | 86% | 18 |
By reasoning effort
The same configuration at several efforts, each against its lowest reasoning effort on the same tasks (a paired 95% interval), and charted against the effort tier and against the output tokens it costs. These scores are pass@1, the first answer alone: a paired comparison needs both efforts' answers to the same tasks, and not every effort has had its retries. Lite-subset sweeps (4 tasks per suite) are never plotted with the full benchmark. The effort study has every model.
| Configuration · effort · full benchmark | pass@1 95% CI | Against the lowest reasoning effort same tasks, paired | Tokens |
|---|---|---|---|
| thinking offbaseline | 32%95% CI 27–37 | — | 1.1k |
| thinking on | 37%95% CI 32–42 | clearly higher (+5 pts, 95% CI +0.4 pts to +9 pts) | 5.1k |
Show the numbers
| Model | Effort (tier) | pass@1 | 95% CI | Tasks | No answer | Tokens: mean | median | Latency: median per answer |
|---|---|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | thinking off · vendor default | 32% | 27–37 | 272 | 0% | 1.1k | 657 | 5.8 s |
| Claude Haiku 4.5 | thinking on | 37% | 32–42 | 272 | 0% | 5.1k | 4.6k | 33 s |
In the studies
- Reasoning effort: its score against effort and against output tokens.
- 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