Models / claude-haiku-5-5
Claude Haiku 5.5
Anthropic's model, with closed weights. Forcebench has run it in 3 configurations on the API board, plus 1 on the lite subset. 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 5.5 is ranked by the highest-scoring of its 3 configurations: the 14th-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 →
- 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 · mediumhighest score | 22 | 76%95% CI 72–81 | — | 2.8k1.7k · 7k | 0% | 8.7 s | $0.0025 | $0.0032 |
| Anthropic · high | 22 | 76%95% CI 72–80 | not clearly different | 4.5k2.5k · 11k | <1% | 12 s | $0.0039 | $0.0051 |
| Anthropic · low | 25 | 74%95% CI 69–78 | not clearly different | 2.2k1.4k · 4.8k | 0% | 7.5 s | $0.0021 | $0.0028 |
- 95% interval
- API board median
Show the numbers
| Suite | c@3 95% CI | API median | Tasks |
|---|---|---|---|
| Apex | 90%95% CI 70–97 | 90% | 20 |
| Salesforce APIs | 89%95% CI 67–97 | 72% | 18 |
| CI/CD | 94%95% CI 73–99 | 88% | 17 |
| Salesforce CLI | 85%95% CI 64–95 | 90% | 20 |
| Salesforce docs | 29%95% CI 13–53 | 29% | 17 |
| fflib Enterprise Patterns | 61%95% CI 39–80 | 61% | 18 |
| Flow | 78%95% CI 55–91 | 69% | 18 |
| Governor limits & pushback | 95%95% CI 75–99 | 89% | 19 |
| Lightning Web Components | 94%95% CI 74–99 | 100% | 18 |
| Nonprofit Success Pack | 83%95% CI 61–94 | 81% | 18 |
| Packaging | 67%95% CI 44–84 | 69% | 18 |
| Permissions & access | 87%95% CI 62–96 | 87% | 15 |
| Scratch org definitions | 50%95% CI 29–71 | 50% | 18 |
| SOQL | 95%95% CI 76–99 | 100% | 20 |
| Trigger Actions Framework | 50%95% CI 29–71 | 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 |
|---|---|---|---|
| lowbaseline | 59%95% CI 54–64 | — | 2.2k |
| medium | 64%95% CI 59–69 | clearly higher (+5 pts, 95% CI +1 pt to +9 pts) | 2.8k |
| high | 63%95% CI 58–68 | no clear difference (+4 pts, 95% CI −0.3 pts to +7 pts); this data can't rule out a change of up to 7 points | 4.5k |
Show the numbers
| Model | Effort (tier) | pass@1 | 95% CI | Tasks | No answer | Tokens: mean | median | Latency: median per answer |
|---|---|---|---|---|---|---|---|---|
| Claude Haiku 5.5 | low | 59% | 54–64 | 272 | 0% | 2.2k | 1.4k | 6.6 s |
| Claude Haiku 5.5 | medium | 64% | 59–69 | 272 | 0% | 2.8k | 1.7k | 7.7 s |
| Claude Haiku 5.5 | high | 63% | 58–68 | 272 | <1% | 4.5k | 2.5k | 11 s |
In the studies
- Reasoning effort: its score against effort and against output tokens.
- Max effort: what its max effort did against the effort below it.
- 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.
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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