Studio: tighten cache-stats comments

studio/frontend/src/components/assistant-ui/message-timing.tsx

@@ -45,14 +45,9 @@ export const MessageTiming: FC<{
       }
     | undefined;
   const st = custom?.serverTimings;
-  // Cache-hit / cache-write counts. llama-server reports hits on
-  // timings.cache_n; external providers (Anthropic / OpenAI Responses /
-  // Gemini) report them on the include_usage envelope that the adapter
-  // normalizes into custom.contextUsage. Prefer the local-runtime value
-  // when present (so llama.cpp keeps populating the badge mid-stream)
-  // and fall back to the external envelope otherwise.
+  // Prefer llama-server timings, fall back to external usage envelope.
   const cacheHits = (st?.cache_n ?? 0) || (custom?.contextUsage?.cachedTokens ?? 0);
-  // Anthropic-only: tokens written into the prompt cache on this turn.
+  // Anthropic-only cache-write count.
   const cacheWrites = custom?.contextUsage?.cacheWriteTokens ?? 0;
 
   // Guard unphysical tok/s: llama.cpp emits predicted_ms=0 on no-op

studio/frontend/src/features/chat/api/chat-adapter.ts

@@ -70,16 +70,9 @@ interface ServerUsage {
   prompt_tokens: number;
   completion_tokens: number;
   total_tokens: number;
-  /**
-   * External providers (Anthropic / OpenAI Responses / Gemini) surface
-   * prompt-cache accounting on the same `usage` envelope via
-   * `_build_usage_chunk` in `studio/backend/core/inference/external_provider.py`.
-   * `prompt_tokens_details.cached_tokens` is the normalised cache-read count
-   * present for every provider that supports it; `cache_creation_input_tokens`
-   * / `cache_read_input_tokens` are the Anthropic-native keys (cache_read
-   * mirrors `prompt_tokens_details.cached_tokens`; cache_creation is
-   * Anthropic-only and billed at the cache-write premium).
-   */
+  // External prompt-cache fields from `_build_usage_chunk` in
+  // studio/backend/core/inference/external_provider.py. cache_read mirrors
+  // prompt_tokens_details.cached_tokens; cache_creation is Anthropic-only.
   prompt_tokens_details?: {
     cached_tokens?: number;
   };
@@ -1896,24 +1889,14 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
         const finalTokPerSec = meta?.timings?.predicted_per_second;
         const serverPromptEvalTime = meta?.timings?.prompt_ms;
 
-        // Cache-hit count. llama-server reports it on `timings.cache_n`;
-        // external providers (Anthropic, OpenAI Responses, Gemini) report
-        // it on `usage.prompt_tokens_details.cached_tokens` -- see the
-        // `_build_usage_chunk` helper in
-        // studio/backend/core/inference/external_provider.py. Prefer the
-        // local-runtime value when it is present (so llama.cpp keeps
-        // populating the bar mid-stream) and fall back to the external
-        // usage envelope otherwise. cache_read_input_tokens is Anthropic's
-        // native key for the same value; read it as a last resort for
-        // providers that only emit the Anthropic shape.
+        // Cache-hit count: prefer llama-server timings, fall back to the
+        // external-provider usage envelope (Anthropic-native key last).
         const cachedTokens =
           meta?.timings?.cache_n ??
           meta?.usage?.prompt_tokens_details?.cached_tokens ??
           meta?.usage?.cache_read_input_tokens ??
           0;
-        // Anthropic-only: tokens written into the prompt cache on this
-        // turn, billed at the cache-write premium. Surfaced separately so
-        // users can tell a cache miss from a cache hit.
+        // Anthropic-only cache-write count (billed at the write premium).
         const cacheWriteTokens = meta?.usage?.cache_creation_input_tokens ?? 0;
 
         // Update context usage in store if we got valid server data

studio/frontend/src/features/chat/chat-page.tsx

@@ -1494,12 +1494,7 @@ export function ChatPage(): ReactElement {
             {view.mode === "single" && contextUsage ? (
               <ContextUsageBar
                 used={contextUsage.totalTokens}
-                // ggufContextLength is the local llama-server's KV-cache size;
-                // external providers (Anthropic / OpenAI Responses / Gemini)
-                // don't expose a stable per-model context window through the
-                // picker, so it is null in that mode. Pass it as-is -- the bar
-                // drops the "/ total" ratio + percentage when total is absent
-                // and still renders the per-turn counters + cache stats.
+                // null on external providers; the bar handles that.
                 total={ggufContextLength}
                 cached={contextUsage.cachedTokens}
                 cacheWrites={contextUsage.cacheWriteTokens}

studio/frontend/src/features/chat/components/context-usage-bar.tsx

@@ -28,21 +28,11 @@ function getSeverityColor(percent: number): {
 
 export const ContextUsageBar: FC<{
   used: number;
-  /**
-   * Context window size. Optional because external providers don't expose a
-   * stable per-model limit through the chat picker -- the local llama-server
-   * path is the only one that populates ggufContextLength. When omitted, the
-   * bar drops the "/ total" ratio + percentage bar and just shows the token
-   * counts (so cache hits / writes from Anthropic / OpenAI Responses still
-   * land in the tooltip).
-   */
+  // Optional: only local llama-server knows the context window. When absent
+  // the bar shows token counts without the "/ total" ratio.
   total?: number | null;
   cached?: number;
-  /**
-   * Anthropic-only cache-write count (tokens written into the prompt cache
-   * on this turn, billed at the cache-write premium). Shown as a separate
-   * tooltip line so users can tell a cache miss from a cache hit.
-   */
+  // Anthropic-only cache-write count (billed at the write premium).
   cacheWrites?: number;
   promptTokens?: number;
   completionTokens?: number;

studio/frontend/src/features/chat/runtime-provider.tsx

@@ -831,11 +831,8 @@ function useStudioRuntimeAdapters(): StudioRuntimeAdapters {
             }
           | undefined;
         const store = useChatRuntimeStore.getState();
-        // External-provider threads have ggufContextLength === null because
-        // external picker selections don't carry a context-window number.
-        // Restore the persisted usage as long as it belongs to the active
-        // checkpoint; when a local GGUF context window IS known, also keep
-        // the original sanity check that the saved total fits inside it.
+        // Only enforce the fits-in-window check when a local GGUF window
+        // is known; external providers have ggufContextLength === null.
         const withinLocalLimit =
           !store.ggufContextLength ||
           (savedUsage?.totalTokens ?? 0) <= store.ggufContextLength;

studio/frontend/src/features/chat/stores/chat-runtime-store.ts

@@ -291,9 +291,7 @@ type ChatRuntimeStore = {
     completionTokens: number;
     totalTokens: number;
     cachedTokens: number;
-    // Anthropic-only cache-write count from
-    // `usage.cache_creation_input_tokens`. Optional so older persisted
-    // entries from llama-server / pre-cache-stats builds keep loading.
+    // Anthropic-only; optional so pre-cache-stats persisted entries load.
     cacheWriteTokens?: number;
   } | null;
   modelLoading: boolean;