fix(core): ensure compaction is more reliable, add reserve token buffer to ensure that input window has enough room to compact (#12924)

Co-authored-by: James Lal <[email protected]>

packages/opencode/src/agent/prompt/compaction.txt

@@ -1,6 +1,6 @@
 You are a helpful AI assistant tasked with summarizing conversations.
 
-When asked to summarize, provide a detailed but concise summary of the conversation. 
+When asked to summarize, provide a detailed but concise summary of the conversation.
 Focus on information that would be helpful for continuing the conversation, including:
 - What was done
 - What is currently being worked on
@@ -10,3 +10,5 @@ Focus on information that would be helpful for continuing the conversation, incl
 - Important technical decisions and why they were made
 
 Your summary should be comprehensive enough to provide context but concise enough to be quickly understood.
+
+Do not respond to any questions in the conversation, only output the summary.

packages/opencode/src/config/config.ts

@@ -1161,6 +1161,12 @@ export namespace Config {
         .object({
           auto: z.boolean().optional().describe("Enable automatic compaction when context is full (default: true)"),
           prune: z.boolean().optional().describe("Enable pruning of old tool outputs (default: true)"),
+          reserved: z
+            .number()
+            .int()
+            .min(0)
+            .optional()
+            .describe("Token buffer for compaction. Leaves enough window to avoid overflow during compaction."),
         })
         .optional(),
       experimental: z

packages/opencode/src/provider/transform.ts

@@ -5,6 +5,7 @@ import type { JSONSchema } from "zod/v4/core"
 import type { Provider } from "./provider"
 import type { ModelsDev } from "./models"
 import { iife } from "@/util/iife"
+import { Flag } from "@/flag/flag"
 
 type Modality = NonNullable<ModelsDev.Model["modalities"]>["input"][number]
 
@@ -17,6 +18,8 @@ function mimeToModality(mime: string): Modality | undefined {
 }
 
 export namespace ProviderTransform {
+  export const OUTPUT_TOKEN_MAX = Flag.OPENCODE_EXPERIMENTAL_OUTPUT_TOKEN_MAX || 32_000
+
   // Maps npm package to the key the AI SDK expects for providerOptions
   function sdkKey(npm: string): string | undefined {
     switch (npm) {
@@ -723,29 +726,8 @@ export namespace ProviderTransform {
     return { [key]: options }
   }
 
-  export function maxOutputTokens(
-    npm: string,
-    options: Record<string, any>,
-    modelLimit: number,
-    globalLimit: number,
-  ): number {
-    const modelCap = modelLimit || globalLimit
-    const standardLimit = Math.min(modelCap, globalLimit)
-
-    if (npm === "@ai-sdk/anthropic" || npm === "@ai-sdk/google-vertex/anthropic") {
-      const thinking = options?.["thinking"]
-      const budgetTokens = typeof thinking?.["budgetTokens"] === "number" ? thinking["budgetTokens"] : 0
-      const enabled = thinking?.["type"] === "enabled"
-      if (enabled && budgetTokens > 0) {
-        // Return text tokens so that text + thinking <= model cap, preferring 32k text when possible.
-        if (budgetTokens + standardLimit <= modelCap) {
-          return standardLimit
-        }
-        return modelCap - budgetTokens
-      }
-    }
-
-    return standardLimit
+  export function maxOutputTokens(model: Provider.Model): number {
+    return Math.min(model.limit.output, OUTPUT_TOKEN_MAX) || OUTPUT_TOKEN_MAX
   }
 
   export function schema(model: Provider.Model, schema: JSONSchema.BaseSchema | JSONSchema7): JSONSchema7 {

packages/opencode/src/session/compaction.ts

@@ -6,7 +6,6 @@ import { Instance } from "../project/instance"
 import { Provider } from "../provider/provider"
 import { MessageV2 } from "./message-v2"
 import z from "zod"
-import { SessionPrompt } from "./prompt"
 import { Token } from "../util/token"
 import { Log } from "../util/log"
 import { SessionProcessor } from "./processor"
@@ -14,6 +13,7 @@ import { fn } from "@/util/fn"
 import { Agent } from "@/agent/agent"
 import { Plugin } from "@/plugin"
 import { Config } from "@/config/config"
+import { ProviderTransform } from "@/provider/transform"
 
 export namespace SessionCompaction {
   const log = Log.create({ service: "session.compaction" })
@@ -27,15 +27,22 @@ export namespace SessionCompaction {
     ),
   }
 
+  const COMPACTION_BUFFER = 20_000
+
   export async function isOverflow(input: { tokens: MessageV2.Assistant["tokens"]; model: Provider.Model }) {
     const config = await Config.get()
     if (config.compaction?.auto === false) return false
     const context = input.model.limit.context
     if (context === 0) return false
-    const count = input.tokens.input + input.tokens.cache.read + input.tokens.output
-    const output = Math.min(input.model.limit.output, SessionPrompt.OUTPUT_TOKEN_MAX) || SessionPrompt.OUTPUT_TOKEN_MAX
-    const usable = input.model.limit.input || context - output
-    return count > usable
+
+    const count =
+      input.tokens.total ||
+      input.tokens.input + input.tokens.output + input.tokens.cache.read + input.tokens.cache.write
+
+    const reserved =
+      config.compaction?.reserved ?? Math.min(COMPACTION_BUFFER, ProviderTransform.maxOutputTokens(input.model))
+    const usable = input.model.limit.input ? input.model.limit.input - reserved : context - reserved
+    return count >= usable
   }
 
   export const PRUNE_MINIMUM = 20_000
@@ -139,8 +146,34 @@ export namespace SessionCompaction {
       { sessionID: input.sessionID },
       { context: [], prompt: undefined },
     )
-    const defaultPrompt =
-      "Provide a detailed prompt for continuing our conversation above. Focus on information that would be helpful for continuing the conversation, including what we did, what we're doing, which files we're working on, and what we're going to do next considering new session will not have access to our conversation."
+    const defaultPrompt = `Provide a detailed prompt for continuing our conversation above.
+Focus on information that would be helpful for continuing the conversation, including what we did, what we're doing, which files we're working on, and what we're going to do next.
+The summary that you construct will be used so that another agent can read it and continue the work.
+
+When constructing the summary, try to stick to this template:
+---
+## Goal
+
+[What goal(s) is the user trying to accomplish?]
+
+## Instructions
+
+- [What important instructions did the user give you that are relevant]
+- [If there is a plan or spec, include information about it so next agent can continue using it]
+
+## Discoveries
+
+[What notable things were learned during this conversation that would be useful for the next agent to know when continuing the work]
+
+## Accomplished
+
+[What work has been completed, what work is still in progress, and what work is left?]
+
+## Relevant files / directories
+
+[Construct a structured list of relevant files that have been read, edited, or created that pertain to the task at hand. If all the files in a directory are relevant, include the path to the directory.]
+---`
+
     const promptText = compacting.prompt ?? [defaultPrompt, ...compacting.context].join("\n\n")
     const result = await processor.process({
       user: userMessage,
@@ -181,7 +214,7 @@ export namespace SessionCompaction {
         sessionID: input.sessionID,
         type: "text",
         synthetic: true,
-        text: "Continue if you have next steps",
+        text: "Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.",
         time: {
           start: Date.now(),
           end: Date.now(),

packages/opencode/src/session/index.ts

@@ -4,7 +4,7 @@ import { BusEvent } from "@/bus/bus-event"
 import { Bus } from "@/bus"
 import { Decimal } from "decimal.js"
 import z from "zod"
-import { type LanguageModelUsage, type ProviderMetadata } from "ai"
+import { type ProviderMetadata } from "ai"
 import { Config } from "../config/config"
 import { Flag } from "../flag/flag"
 import { Identifier } from "../id/id"
@@ -22,6 +22,8 @@ import { Snapshot } from "@/snapshot"
 import type { Provider } from "@/provider/provider"
 import { PermissionNext } from "@/permission/next"
 import { Global } from "@/global"
+import type { LanguageModelV2Usage } from "@ai-sdk/provider"
+import { iife } from "@/util/iife"
 
 export namespace Session {
   const log = Log.create({ service: "session" })
@@ -439,34 +441,58 @@ export namespace Session {
   export const getUsage = fn(
     z.object({
       model: z.custom<Provider.Model>(),
-      usage: z.custom<LanguageModelUsage>(),
+      usage: z.custom<LanguageModelV2Usage>(),
       metadata: z.custom<ProviderMetadata>().optional(),
     }),
     (input) => {
-      const cacheReadInputTokens = input.usage.cachedInputTokens ?? 0
-      const cacheWriteInputTokens = (input.metadata?.["anthropic"]?.["cacheCreationInputTokens"] ??
-        // @ts-expect-error
-        input.metadata?.["bedrock"]?.["usage"]?.["cacheWriteInputTokens"] ??
-        // @ts-expect-error
-        input.metadata?.["venice"]?.["usage"]?.["cacheCreationInputTokens"] ??
-        0) as number
-
-      const excludesCachedTokens = !!(input.metadata?.["anthropic"] || input.metadata?.["bedrock"])
-      const adjustedInputTokens = excludesCachedTokens
-        ? (input.usage.inputTokens ?? 0)
-        : (input.usage.inputTokens ?? 0) - cacheReadInputTokens - cacheWriteInputTokens
       const safe = (value: number) => {
         if (!Number.isFinite(value)) return 0
         return value
       }
+      const inputTokens = safe(input.usage.inputTokens ?? 0)
+      const outputTokens = safe(input.usage.outputTokens ?? 0)
+      const reasoningTokens = safe(input.usage.reasoningTokens ?? 0)
+
+      const cacheReadInputTokens = safe(input.usage.cachedInputTokens ?? 0)
+      const cacheWriteInputTokens = safe(
+        (input.metadata?.["anthropic"]?.["cacheCreationInputTokens"] ??
+          // @ts-expect-error
+          input.metadata?.["bedrock"]?.["usage"]?.["cacheWriteInputTokens"] ??
+          // @ts-expect-error
+          input.metadata?.["venice"]?.["usage"]?.["cacheCreationInputTokens"] ??
+          0) as number,
+      )
+
+      // OpenRouter provides inputTokens as the total count of input tokens (including cached).
+      // AFAIK other providers (OpenRouter/OpenAI/Gemini etc.) do it the same way e.g. vercel/ai#8794 (comment)
+      // Anthropic does it differently though - inputTokens doesn't include cached tokens.
+      // It looks like OpenCode's cost calculation assumes all providers return inputTokens the same way Anthropic does (I'm guessing getUsage logic was originally implemented with anthropic), so it's causing incorrect cost calculation for OpenRouter and others.
+      const excludesCachedTokens = !!(input.metadata?.["anthropic"] || input.metadata?.["bedrock"])
+      const adjustedInputTokens = safe(
+        excludesCachedTokens ? inputTokens : inputTokens - cacheReadInputTokens - cacheWriteInputTokens,
+      )
+
+      const total = iife(() => {
+        // Anthropic doesn't provide total_tokens, also ai sdk will vastly undercount if we
+        // don't compute from components
+        if (
+          input.model.api.npm === "@ai-sdk/anthropic" ||
+          input.model.api.npm === "@ai-sdk/amazon-bedrock" ||
+          input.model.api.npm === "@ai-sdk/google-vertex/anthropic"
+        ) {
+          return adjustedInputTokens + outputTokens + cacheReadInputTokens + cacheWriteInputTokens
+        }
+        return input.usage.totalTokens
+      })
 
       const tokens = {
-        input: safe(adjustedInputTokens),
-        output: safe(input.usage.outputTokens ?? 0),
-        reasoning: safe(input.usage?.reasoningTokens ?? 0),
+        total,
+        input: adjustedInputTokens,
+        output: outputTokens,
+        reasoning: reasoningTokens,
         cache: {
-          write: safe(cacheWriteInputTokens),
-          read: safe(cacheReadInputTokens),
+          write: cacheWriteInputTokens,
+          read: cacheReadInputTokens,
         },
       }
 

packages/opencode/src/session/llm.ts

@@ -25,8 +25,7 @@ import { Auth } from "@/auth"
 
 export namespace LLM {
   const log = Log.create({ service: "llm" })
-
-  export const OUTPUT_TOKEN_MAX = Flag.OPENCODE_EXPERIMENTAL_OUTPUT_TOKEN_MAX || 32_000
+  export const OUTPUT_TOKEN_MAX = ProviderTransform.OUTPUT_TOKEN_MAX
 
   export type StreamInput = {
     user: MessageV2.User
@@ -149,14 +148,7 @@ export namespace LLM {
     )
 
     const maxOutputTokens =
-      isCodex || provider.id.includes("github-copilot")
-        ? undefined
-        : ProviderTransform.maxOutputTokens(
-            input.model.api.npm,
-            params.options,
-            input.model.limit.output,
-            OUTPUT_TOKEN_MAX,
-          )
+      isCodex || provider.id.includes("github-copilot") ? undefined : ProviderTransform.maxOutputTokens(input.model)
 
     const tools = await resolveTools(input)
 

packages/opencode/src/session/message-v2.ts

@@ -210,6 +210,7 @@ export namespace MessageV2 {
     snapshot: z.string().optional(),
     cost: z.number(),
     tokens: z.object({
+      total: z.number().optional(),
       input: z.number(),
       output: z.number(),
       reasoning: z.number(),
@@ -383,6 +384,7 @@ export namespace MessageV2 {
     summary: z.boolean().optional(),
     cost: z.number(),
     tokens: z.object({
+      total: z.number().optional(),
       input: z.number(),
       output: z.number(),
       reasoning: z.number(),

packages/opencode/src/session/processor.ts

@@ -342,6 +342,9 @@ export namespace SessionProcessor {
               stack: JSON.stringify(e.stack),
             })
             const error = MessageV2.fromError(e, { providerID: input.model.providerID })
+            if (MessageV2.ContextOverflowError.isInstance(error)) {
+              // TODO: Handle context overflow error
+            }
             const retry = SessionRetry.retryable(error)
             if (retry !== undefined) {
               attempt++

packages/opencode/src/session/prompt.ts

@@ -52,7 +52,6 @@ globalThis.AI_SDK_LOG_WARNINGS = false
 
 export namespace SessionPrompt {
   const log = Log.create({ service: "session.prompt" })
-  export const OUTPUT_TOKEN_MAX = Flag.OPENCODE_EXPERIMENTAL_OUTPUT_TOKEN_MAX || 32_000
 
   const state = Instance.state(
     () => {

packages/opencode/src/session/retry.ts

@@ -59,9 +59,8 @@ export namespace SessionRetry {
   }
 
   export function retryable(error: ReturnType<NamedError["toObject"]>) {
-    // DO NOT retry context overflow errors
+    // context overflow errors should not be retried
     if (MessageV2.ContextOverflowError.isInstance(error)) return undefined
-
     if (MessageV2.APIError.isInstance(error)) {
       if (!error.data.isRetryable) return undefined
       if (error.data.responseBody?.includes("FreeUsageLimitError"))

packages/opencode/test/provider/transform.test.ts

@@ -175,100 +175,6 @@ describe("ProviderTransform.options - gpt-5 textVerbosity", () => {
   })
 })
 
-describe("ProviderTransform.maxOutputTokens", () => {
-  test("returns 32k when modelLimit > 32k", () => {
-    const modelLimit = 100000
-    const result = ProviderTransform.maxOutputTokens("@ai-sdk/openai", {}, modelLimit, OUTPUT_TOKEN_MAX)
-    expect(result).toBe(OUTPUT_TOKEN_MAX)
-  })
-
-  test("returns modelLimit when modelLimit < 32k", () => {
-    const modelLimit = 16000
-    const result = ProviderTransform.maxOutputTokens("@ai-sdk/openai", {}, modelLimit, OUTPUT_TOKEN_MAX)
-    expect(result).toBe(16000)
-  })
-
-  describe("azure", () => {
-    test("returns 32k when modelLimit > 32k", () => {
-      const modelLimit = 100000
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/azure", {}, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(OUTPUT_TOKEN_MAX)
-    })
-
-    test("returns modelLimit when modelLimit < 32k", () => {
-      const modelLimit = 16000
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/azure", {}, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(16000)
-    })
-  })
-
-  describe("bedrock", () => {
-    test("returns 32k when modelLimit > 32k", () => {
-      const modelLimit = 100000
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/amazon-bedrock", {}, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(OUTPUT_TOKEN_MAX)
-    })
-
-    test("returns modelLimit when modelLimit < 32k", () => {
-      const modelLimit = 16000
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/amazon-bedrock", {}, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(16000)
-    })
-  })
-
-  describe("anthropic without thinking options", () => {
-    test("returns 32k when modelLimit > 32k", () => {
-      const modelLimit = 100000
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/anthropic", {}, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(OUTPUT_TOKEN_MAX)
-    })
-
-    test("returns modelLimit when modelLimit < 32k", () => {
-      const modelLimit = 16000
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/anthropic", {}, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(16000)
-    })
-  })
-
-  describe("anthropic with thinking options", () => {
-    test("returns 32k when budgetTokens + 32k <= modelLimit", () => {
-      const modelLimit = 100000
-      const options = {
-        thinking: {
-          type: "enabled",
-          budgetTokens: 10000,
-        },
-      }
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/anthropic", options, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(OUTPUT_TOKEN_MAX)
-    })
-
-    test("returns modelLimit - budgetTokens when budgetTokens + 32k > modelLimit", () => {
-      const modelLimit = 50000
-      const options = {
-        thinking: {
-          type: "enabled",
-          budgetTokens: 30000,
-        },
-      }
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/anthropic", options, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(20000)
-    })
-
-    test("returns 32k when thinking type is not enabled", () => {
-      const modelLimit = 100000
-      const options = {
-        thinking: {
-          type: "disabled",
-          budgetTokens: 10000,
-        },
-      }
-      const result = ProviderTransform.maxOutputTokens("@ai-sdk/anthropic", options, modelLimit, OUTPUT_TOKEN_MAX)
-      expect(result).toBe(OUTPUT_TOKEN_MAX)
-    })
-  })
-})
-
 describe("ProviderTransform.schema - gemini array items", () => {
   test("adds missing items for array properties", () => {
     const geminiModel = {

packages/opencode/test/session/compaction.test.ts

@@ -15,6 +15,7 @@ function createModel(opts: {
   output: number
   input?: number
   cost?: Provider.Model["cost"]
+  npm?: string
 }): Provider.Model {
   return {
     id: "test-model",
@@ -34,7 +35,7 @@ function createModel(opts: {
       input: { text: true, image: false, audio: false, video: false },
       output: { text: true, image: false, audio: false, video: false },
     },
-    api: { npm: "@ai-sdk/anthropic" },
+    api: { npm: opts.npm ?? "@ai-sdk/anthropic" },
     options: {},
   } as Provider.Model
 }
@@ -70,7 +71,7 @@ describe("session.compaction.isOverflow", () => {
       directory: tmp.path,
       fn: async () => {
         const model = createModel({ context: 100_000, output: 32_000 })
-        const tokens = { input: 50_000, output: 10_000, reasoning: 0, cache: { read: 10_000, write: 0 } }
+        const tokens = { input: 60_000, output: 10_000, reasoning: 0, cache: { read: 10_000, write: 0 } }
         expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
       },
     })
@@ -112,6 +113,86 @@ describe("session.compaction.isOverflow", () => {
     })
   })
 
+  // ─── Bug reproduction tests ───────────────────────────────────────────
+  // These tests demonstrate that when limit.input is set, isOverflow()
+  // does not subtract any headroom for the next model response. This means
+  // compaction only triggers AFTER we've already consumed the full input
+  // budget, leaving zero room for the next API call's output tokens.
+  //
+  // Compare: without limit.input, usable = context - output (reserves space).
+  // With limit.input, usable = limit.input (reserves nothing).
+  //
+  // Related issues: #10634, #8089, #11086, #12621
+  // Open PRs: #6875, #12924
+
+  test("BUG: no headroom when limit.input is set — compaction should trigger near boundary but does not", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Simulate Claude with prompt caching: input limit = 200K, output limit = 32K
+        const model = createModel({ context: 200_000, input: 200_000, output: 32_000 })
+
+        // We've used 198K tokens total. Only 2K under the input limit.
+        // On the next turn, the full conversation (198K) becomes input,
+        // plus the model needs room to generate output — this WILL overflow.
+        const tokens = { input: 180_000, output: 15_000, reasoning: 0, cache: { read: 3_000, write: 0 } }
+        // count = 180K + 3K + 15K = 198K
+        // usable = limit.input = 200K (no output subtracted!)
+        // 198K > 200K = false → no compaction triggered
+
+        // WITHOUT limit.input: usable = 200K - 32K = 168K, and 198K > 168K = true ✓
+        // WITH limit.input: usable = 200K, and 198K > 200K = false ✗
+
+        // With 198K used and only 2K headroom, the next turn will overflow.
+        // Compaction MUST trigger here.
+        expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
+      },
+    })
+  })
+
+  test("BUG: without limit.input, same token count correctly triggers compaction", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Same model but without limit.input — uses context - output instead
+        const model = createModel({ context: 200_000, output: 32_000 })
+
+        // Same token usage as above
+        const tokens = { input: 180_000, output: 15_000, reasoning: 0, cache: { read: 3_000, write: 0 } }
+        // count = 198K
+        // usable = context - output = 200K - 32K = 168K
+        // 198K > 168K = true → compaction correctly triggered
+
+        const result = await SessionCompaction.isOverflow({ tokens, model })
+        expect(result).toBe(true) // ← Correct: headroom is reserved
+      },
+    })
+  })
+
+  test("BUG: asymmetry — limit.input model allows 30K more usage before compaction than equivalent model without it", async () => {
+    await using tmp = await tmpdir()
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        // Two models with identical context/output limits, differing only in limit.input
+        const withInputLimit = createModel({ context: 200_000, input: 200_000, output: 32_000 })
+        const withoutInputLimit = createModel({ context: 200_000, output: 32_000 })
+
+        // 170K total tokens — well above context-output (168K) but below input limit (200K)
+        const tokens = { input: 166_000, output: 10_000, reasoning: 0, cache: { read: 5_000, write: 0 } }
+
+        const withLimit = await SessionCompaction.isOverflow({ tokens, model: withInputLimit })
+        const withoutLimit = await SessionCompaction.isOverflow({ tokens, model: withoutInputLimit })
+
+        // Both models have identical real capacity — they should agree:
+        expect(withLimit).toBe(true) // should compact (170K leaves no room for 32K output)
+        expect(withoutLimit).toBe(true) // correctly compacts (170K > 168K)
+      },
+    })
+  })
+
   test("returns false when model context limit is 0", async () => {
     await using tmp = await tmpdir()
     await Instance.provide({
@@ -290,4 +371,53 @@ describe("session.getUsage", () => {
 
     expect(result.cost).toBe(3 + 1.5)
   })
+
+  test.each(["@ai-sdk/anthropic", "@ai-sdk/amazon-bedrock", "@ai-sdk/google-vertex/anthropic"])(
+    "computes total from components for %s models",
+    (npm) => {
+      const model = createModel({ context: 100_000, output: 32_000, npm })
+      const usage = {
+        inputTokens: 1000,
+        outputTokens: 500,
+        // These providers typically report total as input + output only,
+        // excluding cache read/write.
+        totalTokens: 1500,
+        cachedInputTokens: 200,
+      }
+      if (npm === "@ai-sdk/amazon-bedrock") {
+        const result = Session.getUsage({
+          model,
+          usage,
+          metadata: {
+            bedrock: {
+              usage: {
+                cacheWriteInputTokens: 300,
+              },
+            },
+          },
+        })
+
+        expect(result.tokens.input).toBe(1000)
+        expect(result.tokens.cache.read).toBe(200)
+        expect(result.tokens.cache.write).toBe(300)
+        expect(result.tokens.total).toBe(2000)
+        return
+      }
+
+      const result = Session.getUsage({
+        model,
+        usage,
+        metadata: {
+          anthropic: {
+            cacheCreationInputTokens: 300,
+          },
+        },
+      })
+
+      expect(result.tokens.input).toBe(1000)
+      expect(result.tokens.cache.read).toBe(200)
+      expect(result.tokens.cache.write).toBe(300)
+      expect(result.tokens.total).toBe(2000)
+    },
+  )
 })

packages/opencode/test/session/llm.test.ts

@@ -314,12 +314,7 @@ describe("session.llm.stream", () => {
         expect(body.stream).toBe(true)
 
         const maxTokens = (body.max_tokens as number | undefined) ?? (body.max_output_tokens as number | undefined)
-        const expectedMaxTokens = ProviderTransform.maxOutputTokens(
-          resolved.api.npm,
-          ProviderTransform.options({ model: resolved, sessionID }),
-          resolved.limit.output,
-          LLM.OUTPUT_TOKEN_MAX,
-        )
+        const expectedMaxTokens = ProviderTransform.maxOutputTokens(resolved)
         expect(maxTokens).toBe(expectedMaxTokens)
 
         const reasoning = (body.reasoningEffort as string | undefined) ?? (body.reasoning_effort as string | undefined)
@@ -442,12 +437,7 @@ describe("session.llm.stream", () => {
         expect((body.reasoning as { effort?: string } | undefined)?.effort).toBe("high")
 
         const maxTokens = body.max_output_tokens as number | undefined
-        const expectedMaxTokens = ProviderTransform.maxOutputTokens(
-          resolved.api.npm,
-          ProviderTransform.options({ model: resolved, sessionID }),
-          resolved.limit.output,
-          LLM.OUTPUT_TOKEN_MAX,
-        )
+        const expectedMaxTokens = ProviderTransform.maxOutputTokens(resolved)
         expect(maxTokens).toBe(expectedMaxTokens)
       },
     })
@@ -565,14 +555,7 @@ describe("session.llm.stream", () => {
 
         expect(capture.url.pathname.endsWith("/messages")).toBe(true)
         expect(body.model).toBe(resolved.api.id)
-        expect(body.max_tokens).toBe(
-          ProviderTransform.maxOutputTokens(
-            resolved.api.npm,
-            ProviderTransform.options({ model: resolved, sessionID }),
-            resolved.limit.output,
-            LLM.OUTPUT_TOKEN_MAX,
-          ),
-        )
+        expect(body.max_tokens).toBe(ProviderTransform.maxOutputTokens(resolved))
         expect(body.temperature).toBe(0.4)
         expect(body.top_p).toBe(0.9)
       },
@@ -677,14 +660,7 @@ describe("session.llm.stream", () => {
         expect(capture.url.pathname).toBe(pathSuffix)
         expect(config?.temperature).toBe(0.3)
         expect(config?.topP).toBe(0.8)
-        expect(config?.maxOutputTokens).toBe(
-          ProviderTransform.maxOutputTokens(
-            resolved.api.npm,
-            ProviderTransform.options({ model: resolved, sessionID }),
-            resolved.limit.output,
-            LLM.OUTPUT_TOKEN_MAX,
-          ),
-        )
+        expect(config?.maxOutputTokens).toBe(ProviderTransform.maxOutputTokens(resolved))
       },
     })
   })

packages/opencode/test/session/retry.test.ts

@@ -112,6 +112,15 @@ describe("session.retry.retryable", () => {
     const error = wrap("not-json")
     expect(SessionRetry.retryable(error)).toBeUndefined()
   })
+
+  test("does not retry context overflow errors", () => {
+    const error = new MessageV2.ContextOverflowError({
+      message: "Input exceeds context window of this model",
+      responseBody: '{"error":{"code":"context_length_exceeded"}}',
+    }).toObject() as ReturnType<NamedError["toObject"]>
+
+    expect(SessionRetry.retryable(error)).toBeUndefined()
+  })
 })
 
 describe("session.message-v2.fromError", () => {

packages/sdk/js/src/v2/gen/types.gen.ts

@@ -203,6 +203,7 @@ export type AssistantMessage = {
   summary?: boolean
   cost: number
   tokens: {
+    total?: number
     input: number
     output: number
     reasoning: number
@@ -418,6 +419,7 @@ export type StepFinishPart = {
   snapshot?: string
   cost: number
   tokens: {
+    total?: number
     input: number
     output: number
     reasoning: number
@@ -1822,6 +1824,10 @@ export type Config = {
      * Enable pruning of old tool outputs (default: true)
      */
     prune?: boolean
+    /**
+     * Token buffer for compaction. Leaves enough window to avoid overflow during compaction.
+     */
+    reserved?: number
   }
   experimental?: {
     disable_paste_summary?: boolean

packages/web/src/content/docs/config.mdx

@@ -490,13 +490,15 @@ You can control context compaction behavior through the `compaction` option.
   "$schema": "https://opencode.ai/config.json",
   "compaction": {
     "auto": true,
-    "prune": true
+    "prune": true,
+    "reserved": 10000
   }
 }
 ```
 
 - `auto` - Automatically compact the session when context is full (default: `true`).
 - `prune` - Remove old tool outputs to save tokens (default: `true`).
+- `reserved` - Token buffer for compaction. Leaves enough window to avoid overflow during compaction
 
 ---