Suites / lwc
Lightning Web Components
Lightning Web Components engineering: public properties and reactivity, templates and conditional rendering, events, parent/child APIs, wire adapters, imperative Apex, toasts, navigation, Lightning Message Service, lightning-datatable, forms, lifecycle cleanup, slots, labels, App Builder metadata and accessibility. Each answer runs under hidden Jest tests (@salesforce/sfdx-lwc-jest); no org is needed.
How it's graded
Each component runs under hidden Jest tests (@salesforce/sfdx-lwc-jest) that render it and check its behaviour. No org is needed.
Difficulty mix
Leaderboard
Who's best at Lightning Web Components
Within three attempts (c@3) on this suite only, with 95% confidence intervals. With 18 tasks the intervals are wide: where two overlap, those configurations are not clearly different. 1 finished configuration isn’t shown yet: its retries haven’t finished. Open in the full leaderboard →
| # | Model and config | Score c@3 · 95% CI | Tasks |
|---|---|---|---|
| 1 | Claude Fable 5.1 effort high | 100%95% CI 82–100 | 18 |
| 1 | Claude Haiku 5.5 effort high | 100%95% CI 82–100 | 18 |
| 1 | Claude Haiku 5.5 effort low | 100%95% CI 82–100 | 18 |
| 1 | Claude Opus 4.8 effort high | 100%95% CI 82–100 | 18 |
| 1 | Claude Opus 4.8 effort low | 100%95% CI 82–100 | 18 |
| 1 | Claude Opus 4.8 thinking off | 100%95% CI 82–100 | 18 |
| 1 | Claude Opus 5.5 effort high | 100%95% CI 82–100 | 18 |
| 1 | Claude Opus 5.5 effort medium | 100%95% CI 82–100 | 18 |
| 1 | Claude Opus 5.5 effort low | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5 effort high | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5 effort medium | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5 thinking off | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5 effort low | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5.5 effort high | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5.5 effort low | 100%95% CI 82–100 | 18 |
| 1 | Claude Sonnet 5.5 effort medium | 100%95% CI 82–100 | 18 |
| 1 | DeepSeek V4 Pro FP8 · effort high | 100%95% CI 82–100 | 18 |
| 1 | DeepSeek V4.1 Flash FP8 · effort high | 100%95% CI 82–100 | 18 |
| 1 | DeepSeek V4.1 Flash Native FP8, FP4 experts · SGLang · effort high | 100%95% CI 82–100 | 18 |
| 1 | Gemini 3.8 Flash effort medium | 100%95% CI 82–100 | 18 |
| 1 | GLM-5.3 Flash EXL3 4.0bpw experts, 4-bit dense · TensorFold · effort high | 100%95% CI 82–100 | 18 |
| 1 | GLM-5.3 Flash EXL3 4.0bpw experts, FP8 dense · vLLM + ExLlamaV3 · effort low | 100%95% CI 82–100 | 18 |
| 1 | GPT-5.6 Sol effort medium | 100%95% CI 82–100 | 18 |
| 1 | GPT-6 Astra effort medium | 100%95% CI 82–100 | 18 |
| 1 | GPT-6 Astra effort high | 100%95% CI 82–100 | 18 |
| 1 | GPT-6 Luna effort medium | 100%95% CI 82–100 | 18 |
| 1 | GPT-6.1 Sol effort high | 100%95% CI 82–100 | 18 |
| 1 | Grok 4.6 effort high | 100%95% CI 82–100 | 18 |
| 1 | Grok 4.7 effort high | 100%95% CI 82–100 | 18 |
| 1 | Inkling Small FP8 · effort high | 100%95% CI 82–100 | 18 |
| 1 | Qwen3.8 Flash-Next NVFP4 · vLLM · effort medium | 100%95% CI 82–100 | 18 |
| 32 | Claude Haiku 5.5 effort medium | 94%95% CI 74–99 | 18 |
| 32 | Claude Opus 4.8 effort medium | 94%95% CI 74–99 | 18 |
| 32 | DeepSeek V4 Flash Vision experimental FP8 + FP4 experts · vLLM · effort high | 94%95% CI 74–99 | 18 |
| 32 | GLM-5.3 Flash FP8 · effort high | 94%95% CI 74–99 | 18 |
| 32 | GLM-5.3 Flash EXL3 4.0bpw experts, FP8 dense · vLLM + ExLlamaV3 · effort high | 94%95% CI 74–99 | 18 |
| 32 | MiMo V2.6 Flash FP8 · thinking on | 94%95% CI 74–99 | 18 |
| 32 | MiMo V2.6 Pro FP8 · thinking on | 94%95% CI 74–99 | 18 |
| 32 | Mistral Large 4 effort high | 94%95% CI 74–99 | 18 |
| 32 | Nemotron 3 Ultra thinking on | 94%95% CI 74–99 | 18 |
| 41 | Claude Haiku 4.5 thinking on | 89%95% CI 67–97 | 18 |
| 41 | DeepSeek V4.1 Flash EXL3 2.9bpw · vLLM + ExLlamaV3 · effort high | 89%95% CI 67–97 | 18 |
| 41 | Gemma 4 31B QAT W4A16 · vLLM · thinking on | 89%95% CI 67–97 | 18 |
| 41 | Qwen3.8 Flash-Next MLX 4-bit · MTPLX · effort medium | 89%95% CI 67–97 | 18 |
| 41 | Qwen3.8 Flash-Next MLX 4-bit · TensorFold · effort medium | 89%95% CI 67–97 | 18 |
| 41 | Qwen3.8 Flash-Next INT4-AutoRound, 5 of 10 experts · TensorFold (Zig) · effort medium | 89%95% CI 67–97 | 18 |
| 47 | Gemma 4 26B-A4B NVFP4 · vLLM · thinking on | 83%95% CI 61–94 | 18 |
| 48 | Claude Haiku 4.5 thinking off | 78%95% CI 55–91 | 18 |
| 48 | MiMo V2.6 Flash FP8 + MXFP4 experts · SGLang · thinking on | 78%95% CI 55–91 | 18 |
| 48 | Qwen3.8 Flash-Next thinking on | 78%95% CI 55–91 | 18 |
| 51 | GLM-5.3 Flash EXL3 4.0bpw experts, FP8 dense · vLLM + ExLlamaV3 · thinking off | 72%95% CI 49–88 | 18 |
| 51 | Ling 3.0 Flash BF16 · thinking on | 72%95% CI 49–88 | 18 |
| 53 | Laguna S 2.1 FP4 · thinking on | 67%95% CI 44–84 | 18 |
| 54 | Qwen3.8 27B AWQ-INT4 · vLLM · effort low | 61%95% CI 39–80 | 18 |
| 55 | Qwen3.8 27B MLX 4-bit · MTPLX · effort low | 50%95% CI 29–71 | 18 |
| 56 | Qwen3.6 35B-A3B NVFP4 · vLLM · thinking on | 44%95% CI 25–66 | 18 |
| 56 | Qwen3.8 27B Splash 4-bit · Splash · effort low | 44%95% CI 25–66 | 18 |
| 58 | Qwen3.8 27B MLX 8-bit · MTPLX · effort low | 39%95% CI 20–61 | 18 |
| 58 | Qwen3.8 27B AWQ-INT4 · vLLM · effort xhigh | 39%95% CI 20–61 | 18 |
| 60 | Qwen3.8 27B AWQ-INT4 · vLLM · effort medium | 33%95% CI 16–56 | 18 |
| 61 | Nemotron 3.5 Lightning NVFP4 · thinking on | 11%95% CI 3–33 | 18 |
Tasks
What's in the suite
Solve rate is the share of finished runs whose first answer to the task passed (pass@1), a rough guide to how hard models find it. Difficulty is the task author’s label; from results is what the solve rate says: easy when two thirds or more of the finished runs pass it, hard at a third or fewer, once five have graded it.
| Task | Difficulty | From results | Solve rate |
|---|---|---|---|
| Accessible collapsible section with slotslwc-collapsible-section-slots | easy | easy | 74% |
| Child-to-parent select event from a product tilelwc-product-tile-select-event | easy | easy | 81% |
| Reactive case status badgelwc-case-status-badge | easy | easy | 79% |
| Sorted contact list from a public arraylwc-contact-list-sorted | easy | easy | 76% |
| Translatable, accessible favourite togglelwc-favorite-toggle-labels | easy | medium | 63% |
| Account header with the getRecord wire adapterlwc-account-header-wire | medium | medium | 63% |
| Debounced search with stale-response protectionlwc-contact-search-debounce | medium | easy | 87% |
| Expose a component to Lightning App Builderlwc-pipeline-card-app-builder | medium | medium | 55% |
| Form validation with custom validity and toastslwc-registration-form-validation | medium | easy | 81% |
| Imperative Apex with loading, empty and error stateslwc-open-opportunities-imperative-apex | medium | easy | 75% |
| Lightning Message Service subscriber with clean lifecyclelwc-lms-account-panel | medium | medium | 47% |
| NavigationMixin page references and generated URLslwc-account-quick-links-navigation | medium | medium | 56% |
| Parent calling child @api methodslwc-order-lines-child-api | medium | easy | 78% |
| Datatable row actions with confirm, delete, navigation and toastslwc-opportunity-row-actions | hard | medium | 65% |
| Fix reactivity and data-flow bugs in a checklist componentlwc-task-checklist-reactivity-fix | hard | easy | 74% |
| Inline-edit datatable saved through LDS with refreshApexlwc-contacts-datatable-inline-save | hard | easy | 73% |
| Keyboard-accessible listbox with aria-activedescendantlwc-status-listbox-keyboard | hard | medium | 61% |
| Polling component with visibility pause and lifecycle cleanuplwc-queue-monitor-polling-lifecycle | hard | easy | 82% |
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