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Route language-model calls by the work they must complete, not by a single application-wide default. In Weio’s 2026 production measurements, a frontier model handled 77% of calls but consumed 97% of model spending; cheaper models handled the remaining 23% of calls for about 3% of spend. The practical policy is simple: send requests with a defined, testable path to a small or mid-tier model, and reserve frontier capacity for ambiguous decisions, subtle debugging, security-sensitive changes, and mistakes that are costly to find or undo. That approach can lower spend without treating quality as optional, provided you measure retries, escalations, validation failures, and the cost of actually completing each task.
Why did one model consume almost the entire budget?
A frontier model can dominate spending even when many requests do not need frontier-level reasoning. Weio reports that its default frontier model, gpt-4o, processed 77% of production calls and 97% of model spend in 2026. Cheaper models processed the other 23% of calls and about 3% of spend.
Those percentages are Weio’s own production measurements, not an industry benchmark. The source does not publish an independently audited sample size, quality score, or reproducibility package, so use the figures as a warning about default-model economics rather than as a promise of identical savings in another workload.
| Measure | Weio result | How to interpret it |
|---|---|---|
| Production calls handled by frontier model | 77% | Most requests still used the default tier. |
| Share of model spend from frontier model | 97% | Spend was much more concentrated than traffic. |
| Production calls handled by cheaper models | 23% | A substantial minority was already routed away from the default. |
| Share of spend from cheaper models | About 3% | Lower-priced tiers handled those calls at a small fraction of spend. |
The imbalance is caused by price per call, token volume, and the fact that a default route sends every uncertain request to the most expensive option. A router changes that default: it treats model capability as a resource to allocate where errors or judgment have the highest cost.
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What does “is the path already decided?” mean?
“Is the path already decided?” is the central routing question in Weio’s guide. If a specification, ticket, diff description, template, or known procedure determines what should happen, a smaller model can usually execute the work while tests or a reviewer catch an error. If the model must decide the path itself, the request belongs in the frontier tier.
Requests that usually fit a small or mid-tier model
- Editing named files to satisfy a clear requirement.
- Applying a refactor described in a written specification.
- Reading documents and producing a summary.
- Drafting text that a human or automated review will check.
- Routine operations, formatting, data transformation, and glue code.
“Small” does not mean unimportant. A large refactor may affect critical code, yet remain safely routable when its target, acceptance tests, and rollback path are explicit. Checkability is the deciding property, not the apparent size of the task.
Requests that warrant a frontier model
- Debugging where the failure mechanism is unclear or intermittent.
- Architecture and design choices that establish a future implementation path.
- Security-sensitive changes, threat analysis, or permission decisions.
- Ambiguous requirements that need interpretation and trade-offs.
- Any task where a wrong answer is expensive to detect, reverse, or disclose.
A request can move between tiers as its context changes. Once an architect has fixed the design and supplied acceptance criteria, implementation calls may become small-model work. Conversely, a routine-looking change should escalate if tests repeatedly fail or the model encounters an unexpected dependency.
How should you choose a routing signal?
Use the most reliable signal available at the point where the call is made. A known endpoint is preferable to asking a second model to classify every request, but a lightweight classifier is useful when one endpoint serves mixed work.
Route by call site when the endpoint already identifies the job
Separate calls such as summarize_document, apply_refactor, and design_auth_flow carry useful intent in their names and contracts. Assign a default tier to each endpoint, document the reason, and allow escalation when validation fails. This approach adds little latency and makes the policy easy to audit.
Route by request shape when a call site is mixed
Inspect structured fields rather than relying only on prose. A target-file list, acceptance test, patch description, or template indicates an execution path. A bare question, an unresolved constraint, or a request to choose among architectures indicates that the model must establish the path.
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Use a two-label classifier for genuinely mixed traffic
A first-pass classifier should return only two operational labels, such as EXECUTE and DECIDE. Keep the prompt and output schema stable, log the classifier result, and define a conservative fallback: uncertain or malformed classifications should go to the frontier tier rather than silently receiving the cheapest model.
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Make the model tier an explicit parameter on every model call. Do not hide routing in a global client default, because explicit parameters make logs, retries, and policy changes attributable to a task.
- Describe the task contract. Capture the operation, inputs, target resources, acceptance checks, data sensitivity, and whether a reviewer will inspect the result.
- Assign an initial tier. Use the call site or request-shape rules to choose a small, mid-tier, or frontier model.
- Run validation. Tests, schema checks, static analysis, policy checks, or a reviewer should produce a machine-readable pass or fail result whenever possible.
- Retry within a limit. Give the small-model route a finite number of attempts. A retry limit prevents an apparently cheap task from accumulating hidden cost.
- Escalate on evidence. Repeated validation failures, unresolved tool errors, or a newly discovered security concern should promote the task to the frontier tier.
- Record the handoff. Preserve the original prompt, model tier, failure reason, retry count, escalation decision, and final result identifier according to your data-retention policy.
A one-action promotion path matters operationally. An operator or evaluator should be able to select “promote to frontier,” carrying the existing context forward instead of starting a new task with missing history.
tier = classify(request) # EXECUTE or DECIDE
model = frontier if tier == "DECIDE" else economical
attempts = 0
while attempts < retry_limit:
result = call(model=model, request=request, tier=tier)
record(result, model=model, tier=tier, attempt=attempts)
if validate(result):
return complete(result)
attempts += 1
promote(reason="validation_failed", prior_attempts=attempts)
return complete(call(model=frontier, request=request, tier="ESCALATED"))The pseudocode illustrates the control flow, not a vendor-specific SDK. Your implementation still needs timeout handling, idempotency for side effects, redaction rules, and a safe behavior when the frontier service is unavailable.
What is the difference between cost per call and cost per completed task?
Cost per call measures only the initial model invocation. The useful metric is cost per completed task: the total model and operational cost required to obtain an accepted result, including retries, escalations, failed tool calls, and human correction.
cost_per_completed_task =
(initial_calls + retry_calls + escalation_calls
+ review_or_correction_cost) / completed_tasksTrack at least these fields for every task:
- Task or workflow identifier.
- Model tier and exact model name.
- Input and output token counts.
- Latency and timeout status.
- Validation result and failure category.
- Retry count and escalation reason.
- Whether a human corrected or rejected the result.
- Final completion status and total cost.
A cheaper model that fails often can have a higher completed-task cost than a frontier model that succeeds once. Conversely, a frontier model used for every routine call can inflate cost without improving the accepted-result rate. Compare both cost and completion quality before moving the routing boundary.
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What did Weio measure about tokens and per-call cost?
Adil Sadqi reports an average call shape of 1,657 input tokens and 110 output tokens in 2026. At that measured shape, the reported frontier-model cost was $0.00524 per call, compared with $0.00011 for a capable open-weight model—roughly a 48-fold difference.
| Measured item | Reported value | Qualification |
|---|---|---|
| Average input tokens per call | 1,657 | Weio workload average reported by Adil Sadqi in 2026. |
| Average output tokens per call | 110 | Same workload and reporting period. |
| Frontier-model cost per call | $0.00524 | Calculated for the measured token shape; not a universal price. |
| Capable open-weight-model cost per call | $0.00011 | Calculated for the measured token shape; not a quality guarantee. |
| Approximate cost multiple | 48× | Comparison of those reported per-call estimates. |
Token shape matters. A router that sends long-context analysis to a cheaper tier may save less than expected, while a short, repetitive operation may produce a larger difference. The figures above do not establish that the open-weight model matches the frontier model’s quality, latency, data terms, or tool reliability; they only show the price gap for Weio’s measured call shape.
How do you measure a routing change safely?
Take a baseline before changing the default. Weio recommends pulling one recent week of usage and calculating each model’s share of calls and share of spend. After the routing change, calculate the same shares again and add cost per completed task.
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- Segment by task type. Separate summaries, code edits, debugging, design work, and other workflows; an aggregate average can hide a failing category.
- Launch with a narrow policy. Route only clearly specified execution tasks first, leaving ambiguous cases on the frontier tier.
- Compare quality and economics. Review completion rate, correction rate, latency, retry rate, escalation rate, and completed-task cost.
- Adjust the boundary using evidence. Promote categories with repeated failures; demote categories that pass validation reliably without human correction.
Keep both snapshots. Without the baseline, a lower spend total could simply reflect fewer requests, shorter prompts, or a change in traffic mix rather than a better routing policy.
How does this policy work in an AI-operated company?
Weio describes itself as an AI-run C corporation with one human owner and officer. Its agents handle coding, drafting, research, and routine operations, while operator sessions use the same routing table with escalation and handoff records. That arrangement makes explicit tier selection especially important: when many tasks are executed by agents, logs are the practical record of why a model was chosen and when a human took control.
The broader discipline is to apply the policy to internal work as well as customer-facing calls. As Weio puts it, “A cost discipline you will not apply to yourself is a slogan, not a discipline.” Internal automation can otherwise become an unmeasured source of frontier-model spend.
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What can go wrong with difficulty-based routing?
Calling everything “complex”
Teams often classify a task by business importance rather than by how clearly its path and acceptance criteria are specified. A high-impact but deterministic migration can be economical to route, while a low-volume security question may require frontier reasoning.
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Malformed labels, missing fields, or classifier uncertainty should not silently select the cheapest model. Validate the label against an allowlist and send unknown cases to the safer tier.
Allowing unlimited retries
Unlimited retries turn a low per-call price into an unpredictable task cost. Set a limit, classify repeated failures as routing evidence, and escalate with the accumulated context.
Ignoring hidden human work
If reviewers spend several minutes repairing outputs from the economical tier, the invoice alone understates the cost. Record correction time or use a consistent internal rate when comparing completed-task economics.
Forgetting data and operational constraints
Price is only one routing axis. Include data residency, retention, security controls, latency targets, tool permissions, and outage behavior in the policy. A cheaper model that cannot legally or reliably process a request is not an available route.
Which routing strategy should you choose?
| Strategy | Best fit | Main advantage | Main risk |
|---|---|---|---|
| Call-site defaults | Separate endpoints with stable contracts | Low latency and simple auditing | Misses mixed intent inside one endpoint |
| Request-shape rules | One endpoint with structured task metadata | Uses explicit targets, specs, and templates | Requires consistent request schemas |
| Two-label classifier | Mixed or unstructured requests | Handles intent that rules cannot capture | Adds a call, failure mode, and monitoring burden |
| Always-frontier fallback | High-risk or poorly instrumented workflows | Conservative quality and safety posture | Can recreate the original spend concentration |
Evaluate each strategy on completion quality, cost per completed task, retry and escalation rates, latency, token shape, security constraints, and operational complexity. The best router is the least complicated policy that reliably keeps expensive reasoning for work that actually needs it.
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What should you do first?
Start with measurement, not model shopping. Export a week of calls, identify which operations have explicit paths and cheap validation, and calculate both traffic share and spend share by model. Then route one well-bounded category to an economical tier, impose a retry limit, provide a one-action escalation path, and review completed-task cost before expanding the policy.
Weio’s experience shows why this sequence matters: a frontier default can process most traffic and nearly all spending even when a meaningful portion of that work is routine. The durable lesson is to make routing an observable decision, not an invisible default.
Frequently Asked Questions
Can the same routing policy serve real-time and batch workloads?
Use separate latency budgets and fallback rules for each workload. A batch job can tolerate queueing or a second validation pass, while an interactive request may need a faster tier even when its reasoning is straightforward.
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Should token count determine which model receives a request?
Token count is a useful cost and context signal, but it does not indicate whether the model must make a difficult decision. Combine length with path clarity, reversibility, validation coverage, and data sensitivity.
What happens if the frontier model is unavailable during escalation?
Pause side effects and place the task in a durable queue, or invoke a preapproved equivalent tier only when its data and quality constraints match. Do not silently downgrade a security-sensitive or ambiguous task just to keep throughput moving.
How often should routing rules be revisited?
Review them after a material model-price change, a new workflow, or a sustained change in retry and correction rates. A monthly review is a reasonable operating rhythm for stable systems, with immediate review for safety incidents.
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