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In Ken Imoto’s September 29, 2026 experience report, 96 of 100 whole requests were judged to need a frontier model, while 97 of 200 individual agent steps were judged suitable for local execution. Those are separate measures—not a 96% local failure rate and a 48.5% success rate from one test. Imoto’s results support a hybrid approach for his workload, not a general verdict on local LLMs or Claude Code.
What Imoto measured—and what the numbers mean
Imoto reports running a local qwen3.5:4b model on an RTX 4070. That describes his setup; his article does not establish a minimum hardware requirement or show that another machine will produce the same results.
| Unit measured | Imoto’s reported result | How to interpret it |
|---|---|---|
| Whole requests | 96 of 100 were judged to require a frontier model. | His assessment of his request sample—not a general local-model failure rate. |
| Individual agent steps | 97 of 200 were judged suitable for local execution. | A separate step-level assessment with a different denominator; it does not mean half of the whole requests succeeded locally. |
The distinction matters because a short request can require substantial planning, even if one operation inside it is straightforward. A model may struggle to decide how to solve a task while still being capable of extracting a few relevant details from a fetched page. Imoto’s central point is that model choice can be made per step rather than once for an entire request.
These are self-reported results from one developer’s experience article, not a controlled comparison or independently replicated benchmark. They should not be generalized to other models, task mixes, hardware, or Claude Code versions. Read Imoto’s original DEV Community post.
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Why a hybrid route may work better than local-only
Imoto’s example assigns bounded work locally while leaving harder orchestration with a frontier model. For instance, extracting information from fetched content may be more constrained than planning a multi-step coding task. He describes Claude Code’s WebFetch tool as using a small, fast model for fetched-page processing; treat that as his account of the behavior he encountered, not a guarantee about the current product.
In 20 real WebFetch examples, Imoto says extraction was imperfect: some pages failed to load, and some answers were partly wrong. A local route therefore needs a defined response to incomplete or incorrect output, such as retrying, escalating to a stronger model, or asking a person to verify the result.
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What the rules-first routing example does
Rather than letting a probability-based router decide everything, Imoto’s example uses explicit rules and keeps the frontier model as the default:
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| Step or tool | Example route |
|---|---|
WebFetch |
Local model |
Agent and Task |
Frontier model |
| Commit-message generation | Frontier model in the shown example |
| Unmatched work | Frontier model by default |
| Probability routing | Off by default |
Imoto says one explicit WebFetch rule performed better in his case than an earlier 9B probability router. That is an observation about the case he tested, not evidence that fixed rules always outperform probability-based routing. The practical lesson is to start with a narrow, understandable rule for a step you can evaluate, then inspect its errors before expanding local routing.
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Why the local secret judge is not a security control
Imoto reports a serious false negative: his local judge approved a note containing a production database password, assigning it 69% confidence. In his labeled examples, a 0.5 cutoff missed 20 secrets; a threshold selected from those same examples missed 2. These figures describe his judge and data only. They do not establish a safe threshold or quantify the risk of other systems.
His article describes using Ollama’s native /api/chat endpoint and log probabilities for a constrained Y/N judge. It also reports implementation pitfalls in his setup: the OpenAI-compatible endpoint dropped probabilities, thinking models might not put the answer in the first token, and candidate tokens absent from the returned top-logprob set could appear to have zero probability. These details are version- and implementation-sensitive, so they should not be treated as current API guarantees.
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- Do not use a local model as the sole detector for passwords, keys, or other secrets.
- Keep an independent, tested secret-scanning control in the workflow, and decide explicitly what happens when a result is uncertain.
- Do not copy Imoto’s example cutoff as a security setting; performance on a labeled sample does not establish protection against new or different secrets.
“Free” local tokens do not guarantee a cheaper workflow
Imoto says the local-worker arrangement he tested was the most expensive configuration in his comparison because the orchestrator reread the worker’s output. As he puts it: “The local model’s tokens were free. The orchestrator re-reading everything the worker sent back was not.” The relevant cost is the whole workflow: local runtime and hardware, frontier-model calls, retries, and the amount of worker output passed back through orchestration.
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How to decide what to route locally
Use your own task history rather than Imoto’s percentages as a forecast. Begin with a small category of bounded work and evaluate outcomes separately from whole-request success.
- Choose a narrow step. Pick a repeatable operation with a clear expected output, such as extracting specified fields from retrieved text.
- Record outcomes by unit. Track whether the individual step was correct, and separately whether the complete request required escalation or correction.
- Set explicit failure handling. Decide when a failed fetch, malformed result, or low-confidence classification goes to a frontier model or a human.
- Measure end-to-end cost. Include retries and orchestrator rereading, not only the local model’s token charge.
- Keep sensitive-data protections independent. Use controls that do not rely solely on the local model approving its own output or input.
- Expand only when evidence supports it. Compare results on your workload before routing more complex steps locally.
Imoto says his companion scripts expect Ollama 0.12.11 or later. That is a requirement reported for those scripts, not a statement about the current minimum version for Ollama generally. Because endpoint behavior and product integrations can change, check the current Ollama documentation and the companion project before using the implementation details.
Quick Recap
When this approach is a fit
- Consider hybrid routing when some steps are simple and measurable, but planning or high-impact decisions still warrant a frontier model.
- Prefer frontier execution for steps where errors are costly, the task is open-ended, or you cannot define a reliable fallback.
- Do not assume local-only is cheaper or safer based solely on local token costs or a model’s confidence score.
- Treat hardware as workload-specific. Imoto’s RTX 4070 is a concrete example, not a recommendation or a validated hardware floor.
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