Models / claude-opus-4-8
Claude Opus 4.8
Anthropic's model, with closed weights. Forcebench has run it in 4 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 Opus 4.8 is ranked by the highest-scoring of its 4 configurations: the joint eighth-highest score of 26 models, not clearly different from 9 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 | 12 | 88%95% CI 84–91 | — | 1.6k828 · 4.6k | 0% | 11 s | $0.081 | $0.092 |
| Anthropic · high | 16 | 87%95% CI 83–91 | not clearly different | 2k1k · 5.8k | 0% | 13 s | $0.11 | $0.13 |
| Anthropic · low | 17 | 85%95% CI 81–89 | not clearly different | 940573 · 2.4k | 0% | 7.3 s | $0.054 | $0.063 |
| Anthropic · thinking off | 18 | 82%95% CI 78–86 | not clearly different | 882549 · 2.2k | 0% | 7.3 s | $0.051 | $0.062 |
- 95% interval
- API board median
Show the numbers
| Suite | c@3 95% CI | API median | Tasks |
|---|---|---|---|
| Apex | 100%95% CI 84–100 | 90% | 20 |
| Salesforce APIs | 94%95% CI 74–99 | 72% | 18 |
| CI/CD | 100%95% CI 82–100 | 88% | 17 |
| Salesforce CLI | 100%95% CI 84–100 | 90% | 20 |
| Salesforce docs | 53%95% CI 31–74 | 29% | 17 |
| fflib Enterprise Patterns | 94%95% CI 74–99 | 61% | 18 |
| Flow | 100%95% CI 82–100 | 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 | 89%95% CI 67–97 | 81% | 18 |
| Packaging | 83%95% CI 61–94 | 69% | 18 |
| Permissions & access | 93%95% CI 70–99 | 87% | 15 |
| Scratch org definitions | 50%95% CI 29–71 | 50% | 18 |
| SOQL | 100%95% CI 84–100 | 100% | 20 |
| Trigger Actions Framework | 72%95% CI 49–88 | 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 off | 69%95% CI 64–73 | no clear difference (−1 pt, 95% CI −5 pts to +2 pts); this data can't rule out a change of up to 5 points | 882 |
| lowbaseline | 70%95% CI 65–75 | — | 940 |
| medium | 75%95% CI 71–80 | clearly higher (+5 pts, 95% CI +1 pt to +9 pts) | 1.6k |
| high | 77%95% CI 73–81 | clearly higher (+7 pts, 95% CI +3 pts to +11 pts) | 2k |
Show the numbers
| Model | Effort (tier) | pass@1 | 95% CI | Tasks | No answer | Tokens: mean | median | Latency: median per answer |
|---|---|---|---|---|---|---|---|---|
| Claude Opus 4.8 | thinking off | 69% | 64–73 | 272 | 0% | 882 | 549 | 6.8 s |
| Claude Opus 4.8 | low | 70% | 65–75 | 272 | 0% | 940 | 573 | 7.1 s |
| Claude Opus 4.8 | medium | 75% | 71–80 | 272 | 0% | 1.6k | 828 | 9.9 s |
| Claude Opus 4.8 | high | 77% | 73–81 | 272 | 0% | 2k | 1k | 12 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