Stop burning LLM tokens on if-statements: testing Jev's $0.04 “System One” decision layer.
Using a 100B+ autoregressive language model to output an enum or a boolean is an expensive architectural kludge. Here is what TypeSafe AI's Jev changes, how the latency and cost compare, and where System 1 models hit a hard stop.
You leave with An architectural teardown comparing Jev against LLM structured outputs and local ONNX models, the cost-per-million-decisions ledger, and a typed TypeScript routing gateway
The short answer
- The anti-pattern: Paying for 1,000+ input tokens on Claude 3.5 or GPT-4o with a Zod schema just to decide which tool to call or which queue to route an email to wastes both money and 1–3 seconds of round-trip latency.
- The System 1 model: TypeSafe AI's Jev does not generate prose or conversational tokens. It accepts an arbitrary state and typed queries (
Noulfor probabilistic booleans,Choicefor enums, andScorefor continuous scales), responding in 70–250 ms at $0.042 per million input tokens with zero output token fees. - The architectural rule: Use fast, cheap System 1 decision layers at the perimeter (webhook intake, tool gating, spam/sentiment triage), and reserve frontier reasoning models for the ambiguous center.
Evidence level: architecture and source review of published SDKs and vendor documentation. Not a benchmark of domain-specific classification accuracy.
The four decision architectures compared
Teams building agentic systems or triage gateways generally choose between four paradigms:
| Architecture | Typical Latency | Input / Output Price (1M) | Cost per 100k Decisions | Mechanism & Tradeoff |
|---|---|---|---|---|
| Frontier LLM (GPT-4o / Claude 3.5) | 1,200–3,500 ms | $2.50 in / $10.00 out | $250.00 | Autoregressive token generation with tool/JSON schema constraints |
| Small LLM (GPT-4o-mini / Devstral) | 400–900 ms | $0.15 in / $0.60 out | $15.00 | Autoregressive generation with smaller parameter footprint |
| Jev (TypeSafe AI System 1) | 70–250 ms | $0.042 in / $0.00 out | $3.36 | Direct state-to-decision representation; zero output token decoding |
| Local ONNX (SetFit / DeBERTa) | 15–40 ms | $0 (self-hosted compute) | $0 + operator labor | Local embedding distance / fine-tuned classification head |
The 100,000 decision ledger: why the unit economics diverge
For a customer service triage or agentic webhook gateway processing 100,000 inbound requests a month (averaging 800 input tokens of ticket or state context):
At $3.36 versus $250.00, Jev is nearly 75x cheaper than a frontier LLM on pure token costs while removing over 90% of the network latency overhead.
The Pattern: Two-Stage Bounded Routing
In our review of multi-agent architectures (Agency Agents review), we noted that autonomous systems break down when complex models oversee trivial gates. The reliable architecture uses System 1 as an intake filter:
- Intake & Scoring: Jev evaluates state for spam probability, urgency, and coarse category in <150ms.
- Deterministic Routing: High-confidence spam is dropped; high urgency triggers direct PagerDuty / human escalation; standard billing queries hit deterministic database lookups.
- Reasoning Escalation: Only requests with low confidence or complex multi-part requirements are forwarded to an expensive reasoning agent (Claude 3.5 Sonnet / Codex / Opus).
Where System 1 Models Fail (The Strict Boundary)
Like any specialized tool, System 1 models fail when used outside their intended scope:
- Zero intermediate reasoning: Because Jev does not output a reasoning scratchpad or tokens, there is no explanation to inspect if you need to know why a score was assigned.
- Multi-step deductive logic: If determining an answer requires synthesizing step A to deduce step B and evaluate step C, Jev will fail where a reasoning model succeeds.
- Proprietary domain lexicons: Deeply internal corporate acronyms or niche vertical codebases may require few-shot prompt examples that small categorical heads cannot capture as effectively as an in-context LLM prompt.
- Closed-source API dependency: Jev is a managed service by TypeSafe AI. If your data governance forbids external cloud API calls, local ONNX models (such as SetFit or DeBERTa) remain the compliant self-hosted path.
Explore decision tools and workflows
Evaluate software costs and agent workflows across our free, ungated tools:
Primary sources and documentation
Get the next architecture teardown
One evidence-backed decision note each week. Explicit opt-in, no vendor-paid ranking, unsubscribe any time.