diff --git a/packages/opencode/test/session/llm.test.ts b/packages/opencode/test/session/llm.test.ts
index 5202c06dd9..d393560720 100644
--- a/packages/opencode/test/session/llm.test.ts
+++ b/packages/opencode/test/session/llm.test.ts
@@ -3,7 +3,6 @@ import path from "path"
 import { tool, type ModelMessage } from "ai"
 import z from "zod"
 import { LLM } from "../../src/session/llm"
-import { Global } from "../../src/global"
 import { Instance } from "../../src/project/instance"
 import { Provider } from "../../src/provider/provider"
 import { ProviderTransform } from "../../src/provider/transform"
@@ -535,6 +534,130 @@ describe("session.llm.stream", () => {
     })
   })
 
+  test("accepts user image attachments as data URLs for OpenAI models", async () => {
+    const server = state.server
+    if (!server) {
+      throw new Error("Server not initialized")
+    }
+
+    const source = await loadFixture("openai", "gpt-5.2")
+    const model = source.model
+    const chunks = [
+      {
+        type: "response.created",
+        response: {
+          id: "resp-data-url",
+          created_at: Math.floor(Date.now() / 1000),
+          model: model.id,
+          service_tier: null,
+        },
+      },
+      {
+        type: "response.output_text.delta",
+        item_id: "item-data-url",
+        delta: "Looks good",
+        logprobs: null,
+      },
+      {
+        type: "response.completed",
+        response: {
+          incomplete_details: null,
+          usage: {
+            input_tokens: 1,
+            input_tokens_details: null,
+            output_tokens: 1,
+            output_tokens_details: null,
+          },
+          service_tier: null,
+        },
+      },
+    ]
+    const request = waitRequest("/responses", createEventResponse(chunks, true))
+    const image = `data:image/png;base64,${Buffer.from(
+      await Bun.file(path.join(import.meta.dir, "../tool/fixtures/large-image.png")).arrayBuffer(),
+    ).toString("base64")}`
+
+    await using tmp = await tmpdir({
+      init: async (dir) => {
+        await Bun.write(
+          path.join(dir, "opencode.json"),
+          JSON.stringify({
+            $schema: "https://opencode.ai/config.json",
+            enabled_providers: ["openai"],
+            provider: {
+              openai: {
+                name: "OpenAI",
+                env: ["OPENAI_API_KEY"],
+                npm: "@ai-sdk/openai",
+                api: "https://api.openai.com/v1",
+                models: {
+                  [model.id]: model,
+                },
+                options: {
+                  apiKey: "test-openai-key",
+                  baseURL: `${server.url.origin}/v1`,
+                },
+              },
+            },
+          }),
+        )
+      },
+    })
+
+    await Instance.provide({
+      directory: tmp.path,
+      fn: async () => {
+        const resolved = await Provider.getModel(ProviderID.openai, ModelID.make(model.id))
+        const sessionID = SessionID.make("session-test-data-url")
+        const agent = {
+          name: "test",
+          mode: "primary",
+          options: {},
+          permission: [{ permission: "*", pattern: "*", action: "allow" }],
+        } satisfies Agent.Info
+
+        const user = {
+          id: MessageID.make("user-data-url"),
+          sessionID,
+          role: "user",
+          time: { created: Date.now() },
+          agent: agent.name,
+          model: { providerID: ProviderID.make("openai"), modelID: resolved.id },
+        } satisfies MessageV2.User
+
+        const stream = await LLM.stream({
+          user,
+          sessionID,
+          model: resolved,
+          agent,
+          system: ["You are a helpful assistant."],
+          abort: new AbortController().signal,
+          messages: [
+            {
+              role: "user",
+              content: [
+                { type: "text", text: "Describe this image" },
+                {
+                  type: "file",
+                  mediaType: "image/png",
+                  filename: "large-image.png",
+                  data: image,
+                },
+              ],
+            },
+          ] as ModelMessage[],
+          tools: {},
+        })
+
+        for await (const _ of stream.fullStream) {
+        }
+
+        const capture = await request
+        expect(capture.url.pathname.endsWith("/responses")).toBe(true)
+      },
+    })
+  })
+
   test("sends messages API payload for Anthropic models", async () => {
     const server = state.server
     if (!server) {