Charm Hyper × WorkDaddy: LLM Provider Integration (2026)
Use Charm Hyper as the engine behind your WorkDaddy agent team: 24 models available, context windows up to 1M tokens, connected in minutes with your own key.
Charm Hyper is one of 178 LLM providers supported by WorkDaddy’s model layer in 2026, in the More Providers group. The catalog currently lists 24 models from Charm Hyper: 9 with explicit reasoning, 8 with vision input, and 24 with tool calling — the capability that matters most for agent work. The largest context window reaches 1M tokens.
At a glance
| Models available | 24 |
|---|---|
| Reasoning models | 9 |
| Vision models | 8 |
| Tool-calling models | 24 |
| Largest context window | 1M tokens |
| API endpoint | https://hyper.charm.land/v1 |
| API key variable | HYPER_API_KEY |
| SDK adapter | @ai-sdk/openai-compatible |
| Provider docs | hyper.charm.land ↗ |
Why run WorkDaddy agents on Charm Hyper in 2026
Tool calling, context size, and cost decide how well a provider drives an agent team. Charm Hyper brings 24 tool-calling models to WorkDaddy — enough to power the full loop of storefront edits, analytics queries, and content production — with 1M tokens of context at the top end for whole-codebase and long-report work. Because WorkDaddy is model-agnostic, you can route heavy reasoning to Charm Hyper’s strongest model and bulk work to its cheapest, inside one team.
Best Charm Hyper models for e-commerce agents (2026)
The current flagships from Charm Hyper in the WorkDaddy catalog, newest first:
DeepSeek V4 Flash 0731 — Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding (1M context)
MiniMax-M3 — MiniMax multimodal model for long-context coding, perception, and agent planning (512K context)
Qwen3.7 Flash — Efficient model for low-latency assistance, extraction, and routine automation (1M context)
Kimi K3 — Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work (1M context)
DeepSeek V4 Flash — Fast DeepSeek V4 lane for economical reasoning, coding, and long-context work (1M context)
DeepSeek V4 Pro — Open MoE flagship with million-token context for coding and long agent runs (1M context)
How to connect Charm Hyper to WorkDaddy
WorkDaddy talks to Charm Hyper at hyper.charm.land via the @ai-sdk/openai-compatible adapter. Add your credential (typically HYPER_API_KEY) in Settings → Models, pick a default model, and every agent can use it immediately — or add Charm Hyper as one engine among several and let tasks route to the best fit.
Featured models
| Model | Context | Reasoning | Released |
|---|---|---|---|
| DeepSeek V4 Flash 0731 | 1M | Yes | 2026-08-02 |
| MiniMax-M3 | 512K | Yes | 2026-07-30 |
| Qwen3.7 Flash | 1M | No | 2026-07-27 |
| Kimi K3 | 1M | Yes | 2026-07-27 |
| DeepSeek V4 Flash | 1M | Yes | 2026-07-06 |
| DeepSeek V4 Pro | 1M | Yes | 2026-07-06 |
How it works
Connect
In WorkDaddy, open Settings → Models, choose Charm Hyper, and paste your API key (HYPER_API_KEY). No key? Start on the managed gateway instead.
Put agents to work
Pick DeepSeek V4 Flash 0731 or any of the 24 available models as your default — per-agent overrides let you match model to task.
Review and approve
Agent output stays draft-first regardless of the model: review diffs and approve actions exactly as before.
Frequently asked questions
Does WorkDaddy support Charm Hyper in 2026?
Yes — Charm Hyper is a supported LLM provider with 24 models in the WorkDaddy catalog, connected with your own API key via the @ai-sdk/openai-compatible adapter.
How many Charm Hyper models can I use with WorkDaddy?
24 models are listed for Charm Hyper, including 9 reasoning models and 8 vision-capable models. The flagship lineup above shows the newest.
What context window do Charm Hyper models offer?
Up to 1M tokens on the largest model — relevant for whole-repo storefront work and long analytics reports.
Is Charm Hyper the best LLM provider for e-commerce agents?
It depends on the task mix — Charm Hyper sits in the More Providers group. WorkDaddy is model-agnostic, so the practical answer in 2026 is to combine providers: route each agent task to whichever model fits best.
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Put the team to work on your store
Connect your storefront, analytics, and email stack, set a goal, and let the agents run the work end to end. Start on the free plan with your own model key.