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* fix(gateway): attribute token usage to actual models
Capture per-call model names from LLM response metadata for lead, middleware, and subagent calls.
Persist a per-run token_usage_by_model breakdown and aggregate by that map in both SQL and memory stores, with legacy fallback to the run-level model_name for older rows.
Add regression coverage for by_model totals, caller consistency, active progress snapshots, store parity, and SubagentTokenCollector model propagation.
* fix(gateway): harden by-model token aggregation
Use usage.get("total_tokens", 0) when reducing per-model token usage maps so aggregation tolerates partially written or manually edited JSON blobs without changing behavior for journal-written rows.
* docs(gateway): clarify by-model run count semantics
Document that by_model[*].runs counts the number of runs in which a model appeared, so multi-model runs can increment multiple model buckets.