fix: report cache reads in streaming and correct cost calculation (#577)

* fix: report cache reads in streaming and correct cost calculation

Fix two bugs in how the OpenAI-to-Anthropic shim handles cached tokens:

1. codexShim: streaming message_delta missing cache_read_input_tokens
   The codexStreamToAnthropic() function builds the final message_delta
   usage object inline (not through makeUsage()), and only included
   input_tokens and output_tokens. cache_read_input_tokens was always 0,
   so /cost never showed cache reads for Responses API models (GPT-5+).

   Also fix makeUsage() to read input_tokens_details.cached_tokens and
   prompt_tokens_details.cached_tokens for the non-streaming path.

2. Both shims: cost double-counting from convention mismatch
   OpenAI includes cached tokens in input_tokens/prompt_tokens (i.e.,
   input_tokens = uncached + cached). Anthropic treats input_tokens as
   uncached only. The cost formula was:
     cost = input_tokens * inputRate + cache_read * cacheRate
   This double-counts cached tokens. Fix by subtracting cached from
   input during the conversion:
     input_tokens = prompt_tokens - cached_tokens

   In practice this was inflating reported costs by ~2x for sessions
   with high cache hit rates (which is most sessions, since Copilot
   auto-caches server-side).

Fixes #515

* fix: omit zero cache read/write fields from /cost output

Only show "cache read" and "cache write" in /cost per-model usage when
the value is > 0. Providers like GitHub Copilot never report
cache_creation_input_tokens (the server manages its own cache), so
showing "0 cache write" on every line is misleading — it implies caching
is not working when it actually is.

Before:
  claude-haiku:  2.6k input, 151 output, 39.8k cache read, 0 cache write ($0.04)

After:
  claude-haiku:  2.6k input, 151 output, 39.8k cache read ($0.04)

---------

Co-authored-by: Zartris <14197299+Zartris@users.noreply.github.com>

src/cost-tracker.ts

@@ -181,7 +181,7 @@ function formatCost(cost: number, maxDecimalPlaces: number = 4): string {
 function formatModelUsage(): string {
   const modelUsageMap = getModelUsage()
   if (Object.keys(modelUsageMap).length === 0) {
-    return 'Usage:                 0 input, 0 output, 0 cache read, 0 cache write'
+    return 'Usage:                 0 input, 0 output'
   }
 
   // Accumulate usage by short name
@@ -211,15 +211,19 @@ function formatModelUsage(): string {
 
   let result = 'Usage by model:'
   for (const [shortName, usage] of Object.entries(usageByShortName)) {
-    const usageString =
+    let usageString =
       `  ${formatNumber(usage.inputTokens)} input, ` +
-      `${formatNumber(usage.outputTokens)} output, ` +
-      `${formatNumber(usage.cacheReadInputTokens)} cache read, ` +
-      `${formatNumber(usage.cacheCreationInputTokens)} cache write` +
-      (usage.webSearchRequests > 0
-        ? `, ${formatNumber(usage.webSearchRequests)} web search`
-        : '') +
-      ` (${formatCost(usage.costUSD)})`
+      `${formatNumber(usage.outputTokens)} output`
+    if (usage.cacheReadInputTokens > 0) {
+      usageString += `, ${formatNumber(usage.cacheReadInputTokens)} cache read`
+    }
+    if (usage.cacheCreationInputTokens > 0) {
+      usageString += `, ${formatNumber(usage.cacheCreationInputTokens)} cache write`
+    }
+    if (usage.webSearchRequests > 0) {
+      usageString += `, ${formatNumber(usage.webSearchRequests)} web search`
+    }
+    usageString += ` (${formatCost(usage.costUSD)})`
     result += `\n` + `${shortName}:`.padStart(21) + usageString
   }
   return result

src/services/api/codexShim.ts

@@ -80,12 +80,17 @@ type CodexSseEvent = {
 function makeUsage(usage?: {
   input_tokens?: number
   output_tokens?: number
+  input_tokens_details?: { cached_tokens?: number }
+  prompt_tokens_details?: { cached_tokens?: number }
 }): AnthropicUsage {
   return {
     input_tokens: usage?.input_tokens ?? 0,
     output_tokens: usage?.output_tokens ?? 0,
     cache_creation_input_tokens: 0,
-    cache_read_input_tokens: 0,
+    cache_read_input_tokens:
+      usage?.input_tokens_details?.cached_tokens ??
+      usage?.prompt_tokens_details?.cached_tokens ??
+      0,
   }
 }
 
@@ -890,8 +895,16 @@ export async function* codexStreamToAnthropic(
       stop_sequence: null,
     },
     usage: {
-      input_tokens: finalResponse?.usage?.input_tokens ?? 0,
+      // Subtract cached tokens: OpenAI includes them in input_tokens,
+      // but Anthropic convention treats input_tokens as non-cached only.
+      input_tokens: (finalResponse?.usage?.input_tokens ?? 0) -
+        (finalResponse?.usage?.input_tokens_details?.cached_tokens ??
+         finalResponse?.usage?.prompt_tokens_details?.cached_tokens ?? 0),
       output_tokens: finalResponse?.usage?.output_tokens ?? 0,
+      cache_read_input_tokens:
+        finalResponse?.usage?.input_tokens_details?.cached_tokens ??
+        finalResponse?.usage?.prompt_tokens_details?.cached_tokens ??
+        0,
     },
   }
   yield { type: 'message_stop' }

src/services/api/openaiShim.ts

@@ -564,11 +564,14 @@ function convertChunkUsage(
 ): Partial<AnthropicUsage> | undefined {
   if (!usage) return undefined
 
+  const cached = usage.prompt_tokens_details?.cached_tokens ?? 0
   return {
-    input_tokens: usage.prompt_tokens ?? 0,
+    // Subtract cached tokens: OpenAI includes them in prompt_tokens,
+    // but Anthropic convention treats input_tokens as non-cached only.
+    input_tokens: (usage.prompt_tokens ?? 0) - cached,
     output_tokens: usage.completion_tokens ?? 0,
     cache_creation_input_tokens: 0,
-    cache_read_input_tokens: usage.prompt_tokens_details?.cached_tokens ?? 0,
+    cache_read_input_tokens: cached,
   }
 }