You don't call the chief surgeon for a sprained ankle. Why call GPT-5.1 for routine prompts?
TokenGrill acts as an automated intake nurse for your AI stack. We lint, summarize and route low-level tasks to lightning-fast SLMs — reserving expensive frontier models only for critical reasoning. Cut AI compute costs by 70–90% without losing quality.
The schematic
One prompt in. The right model for every piece of it.
TokenGrill sits between you and every frontier lab — ChatGPT, Gemini, Claude, Grok — plus the small, cheap models that quietly handle most of the work.
“Review this 80-page docket, flag breach clauses, and draft a client memo.”
Merged memo with clause citations — assembled from five model calls you never had to think about.
- Cost
- $0.48 vs $4.20
- Latency
- 3.1s vs 22s
- Saved
- 88%
Decompose
One prompt is split into the smallest independent sub-tasks — extract, reason, summarize, code.
Predict
Tokens and cost are estimated per sub-task before a single API call is made.
Route
Each sub-task goes to the cheapest model that can do it well, and independent tasks run in parallel.
Synthesize
Sub-answers are merged into one coherent response, cited back to their source model.
Tailored triage for every domain
The same routing engine, tuned to the shape of your work. Pick an industry to see the strategy and the numbers.
Legal & litigation support
“Analyze this 80-page discovery document and extract breach of contract clauses.”
Lightweight SLMs parse sections, filter out standard boilerplate, and pass only high-risk clauses to Claude 3.5 Sonnet.
Two ways to answer. One prompt. Live.
Pick your model on the left. The TokenGrill Orchestrator decomposes the same prompt into routed sub-tasks — both run live with real tokens and cost.
How the Orchestrator works
TokenGrill is a 4-step dynamic multi-model prompt router: decompose, predict, route and execute, then synthesize.
A fast orchestrator parses the request into a directed acyclic graph of sub-tasks with explicit dependencies.
Input tokens are counted, output tokens projected with empirical task multipliers (α), and the API cost is priced before a single expensive call runs.
Each sub-task is matched to the cheapest model that can do it well. Independent tasks run concurrently; dependent ones receive upstream output injected into their prompt.
Sub-task completions are validated, formatted and merged into one unified answer for the end user.
| Task type | Routed model | α | Why |
|---|---|---|---|
| extract | Gemini 3.1 Flash Lite | 0.8 | Lightweight extraction — cheapest capable model |
| summarize | GPT-5.6 Luna | 1.1 | Fast summarisation at low cost |
| reason | GPT-5.6 Terra | 2.4 | Complex logic — frontier reasoning quality |
| code | GPT-5.6 Terra | 2.2 | Code generation and review |
| creative | Gemini 3.6 Flash | 1.9 | Fluent long-form drafting |