diff --git a/src/shared/model-availability.test.ts b/src/shared/model-availability.test.ts
new file mode 100644
index 00000000..7fca5dd7
--- /dev/null
+++ b/src/shared/model-availability.test.ts
@@ -0,0 +1,251 @@
+import { describe, it, expect, beforeEach } from "bun:test"
+import { fetchAvailableModels, fuzzyMatchModel, __resetModelCache } from "./model-availability"
+
+describe("fetchAvailableModels", () => {
+  let mockClient: any
+
+  beforeEach(() => {
+    __resetModelCache()
+  })
+
+  it("#given API returns list of models #when fetchAvailableModels called #then returns Set of model IDs", async () => {
+    const mockModels = [
+      { id: "openai/gpt-5.2", name: "GPT-5.2" },
+      { id: "anthropic/claude-opus-4-5", name: "Claude Opus 4.5" },
+      { id: "google/gemini-3-pro", name: "Gemini 3 Pro" },
+    ]
+    mockClient = {
+      model: {
+        list: async () => mockModels,
+      },
+    }
+
+    const result = await fetchAvailableModels(mockClient)
+
+    expect(result).toBeInstanceOf(Set)
+    expect(result.size).toBe(3)
+    expect(result.has("openai/gpt-5.2")).toBe(true)
+    expect(result.has("anthropic/claude-opus-4-5")).toBe(true)
+    expect(result.has("google/gemini-3-pro")).toBe(true)
+  })
+
+  it("#given API fails #when fetchAvailableModels called #then returns empty Set without throwing", async () => {
+    mockClient = {
+      model: {
+        list: async () => {
+          throw new Error("API connection failed")
+        },
+      },
+    }
+
+    const result = await fetchAvailableModels(mockClient)
+
+    expect(result).toBeInstanceOf(Set)
+    expect(result.size).toBe(0)
+  })
+
+  it("#given API called twice #when second call made #then uses cached result without re-fetching", async () => {
+    let callCount = 0
+    const mockModels = [
+      { id: "openai/gpt-5.2", name: "GPT-5.2" },
+      { id: "anthropic/claude-opus-4-5", name: "Claude Opus 4.5" },
+    ]
+    mockClient = {
+      model: {
+        list: async () => {
+          callCount++
+          return mockModels
+        },
+      },
+    }
+
+    const result1 = await fetchAvailableModels(mockClient)
+    const result2 = await fetchAvailableModels(mockClient)
+
+    expect(callCount).toBe(1)
+    expect(result1).toEqual(result2)
+    expect(result1.has("openai/gpt-5.2")).toBe(true)
+  })
+
+  it("#given empty model list from API #when fetchAvailableModels called #then returns empty Set", async () => {
+    mockClient = {
+      model: {
+        list: async () => [],
+      },
+    }
+
+    const result = await fetchAvailableModels(mockClient)
+
+    expect(result).toBeInstanceOf(Set)
+    expect(result.size).toBe(0)
+  })
+
+  it("#given API returns models with various formats #when fetchAvailableModels called #then extracts all IDs correctly", async () => {
+    const mockModels = [
+      { id: "openai/gpt-5.2-codex", name: "GPT-5.2 Codex" },
+      { id: "anthropic/claude-sonnet-4-5", name: "Claude Sonnet 4.5" },
+      { id: "google/gemini-3-flash", name: "Gemini 3 Flash" },
+      { id: "opencode/grok-code", name: "Grok Code" },
+    ]
+    mockClient = {
+      model: {
+        list: async () => mockModels,
+      },
+    }
+
+    const result = await fetchAvailableModels(mockClient)
+
+    expect(result.size).toBe(4)
+    expect(result.has("openai/gpt-5.2-codex")).toBe(true)
+    expect(result.has("anthropic/claude-sonnet-4-5")).toBe(true)
+    expect(result.has("google/gemini-3-flash")).toBe(true)
+	expect(result.has("opencode/grok-code")).toBe(true)
+  })
+})
+
+describe("fuzzyMatchModel", () => {
+	// #given available models from multiple providers
+	// #when searching for a substring match
+	// #then return the matching model
+	it("should match substring in model name", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"openai/gpt-5.2-codex",
+			"anthropic/claude-opus-4-5",
+		])
+		const result = fuzzyMatchModel("gpt-5.2", available)
+		expect(result).toBe("openai/gpt-5.2")
+	})
+
+	// #given available models with partial matches
+	// #when searching for a substring
+	// #then return exact match if it exists
+	it("should prefer exact match over substring match", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"openai/gpt-5.2-codex",
+			"openai/gpt-5.2-ultra",
+		])
+		const result = fuzzyMatchModel("gpt-5.2", available)
+		expect(result).toBe("openai/gpt-5.2")
+	})
+
+	// #given available models with multiple substring matches
+	// #when searching for a substring
+	// #then return the shorter model name (more specific)
+	it("should prefer shorter model name when multiple matches exist", () => {
+		const available = new Set([
+			"openai/gpt-5.2-ultra",
+			"openai/gpt-5.2-ultra-mega",
+		])
+		const result = fuzzyMatchModel("gpt-5.2", available)
+		expect(result).toBe("openai/gpt-5.2-ultra")
+	})
+
+	// #given available models with claude variants
+	// #when searching for claude-opus
+	// #then return matching claude-opus model
+	it("should match claude-opus to claude-opus-4-5", () => {
+		const available = new Set([
+			"anthropic/claude-opus-4-5",
+			"anthropic/claude-sonnet-4-5",
+		])
+		const result = fuzzyMatchModel("claude-opus", available)
+		expect(result).toBe("anthropic/claude-opus-4-5")
+	})
+
+	// #given available models from multiple providers
+	// #when providers filter is specified
+	// #then only search models from specified providers
+	it("should filter by provider when providers array is given", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"anthropic/claude-opus-4-5",
+			"google/gemini-3",
+		])
+		const result = fuzzyMatchModel("gpt", available, ["openai"])
+		expect(result).toBe("openai/gpt-5.2")
+	})
+
+	// #given available models from multiple providers
+	// #when providers filter excludes matching models
+	// #then return null
+	it("should return null when provider filter excludes all matches", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"anthropic/claude-opus-4-5",
+		])
+		const result = fuzzyMatchModel("claude", available, ["openai"])
+		expect(result).toBeNull()
+	})
+
+	// #given available models
+	// #when no substring match exists
+	// #then return null
+	it("should return null when no match found", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"anthropic/claude-opus-4-5",
+		])
+		const result = fuzzyMatchModel("gemini", available)
+		expect(result).toBeNull()
+	})
+
+	// #given available models with different cases
+	// #when searching with different case
+	// #then match case-insensitively
+	it("should match case-insensitively", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"anthropic/claude-opus-4-5",
+		])
+		const result = fuzzyMatchModel("GPT-5.2", available)
+		expect(result).toBe("openai/gpt-5.2")
+	})
+
+	// #given available models with exact match and longer variants
+	// #when searching for exact match
+	// #then return exact match first
+	it("should prioritize exact match over longer variants", () => {
+		const available = new Set([
+			"anthropic/claude-opus-4-5",
+			"anthropic/claude-opus-4-5-extended",
+		])
+		const result = fuzzyMatchModel("claude-opus-4-5", available)
+		expect(result).toBe("anthropic/claude-opus-4-5")
+	})
+
+	// #given available models with multiple providers
+	// #when multiple providers are specified
+	// #then search all specified providers
+	it("should search all specified providers", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"anthropic/claude-opus-4-5",
+			"google/gemini-3",
+		])
+		const result = fuzzyMatchModel("gpt", available, ["openai", "google"])
+		expect(result).toBe("openai/gpt-5.2")
+	})
+
+	// #given available models with provider prefix
+	// #when searching with provider filter
+	// #then only match models with correct provider prefix
+	it("should only match models with correct provider prefix", () => {
+		const available = new Set([
+			"openai/gpt-5.2",
+			"anthropic/gpt-something",
+		])
+		const result = fuzzyMatchModel("gpt", available, ["openai"])
+		expect(result).toBe("openai/gpt-5.2")
+	})
+
+	// #given empty available set
+	// #when searching
+	// #then return null
+	it("should return null for empty available set", () => {
+		const available = new Set<string>()
+		const result = fuzzyMatchModel("gpt", available)
+		expect(result).toBeNull()
+	})
+})
diff --git a/src/shared/model-availability.ts b/src/shared/model-availability.ts
new file mode 100644
index 00000000..6f75ed86
--- /dev/null
+++ b/src/shared/model-availability.ts
@@ -0,0 +1,118 @@
+/**
+ * Fuzzy matching utility for model names
+ * Supports substring matching with provider filtering and priority-based selection
+ */
+
+import { log } from "./logger"
+
+/**
+ * Fuzzy match a target model name against available models
+ * 
+ * @param target - The model name or substring to search for (e.g., "gpt-5.2", "claude-opus")
+ * @param available - Set of available model names in format "provider/model-name"
+ * @param providers - Optional array of provider names to filter by (e.g., ["openai", "anthropic"])
+ * @returns The matched model name or null if no match found
+ * 
+ * Matching priority:
+ * 1. Exact match (if exists)
+ * 2. Shorter model name (more specific)
+ * 
+ * Matching is case-insensitive substring match.
+ * If providers array is given, only models starting with "provider/" are considered.
+ * 
+ * @example
+ * const available = new Set(["openai/gpt-5.2", "openai/gpt-5.2-codex", "anthropic/claude-opus-4-5"])
+ * fuzzyMatchModel("gpt-5.2", available) // → "openai/gpt-5.2"
+ * fuzzyMatchModel("claude", available, ["openai"]) // → null (provider filter excludes anthropic)
+ */
+export function fuzzyMatchModel(
+	target: string,
+	available: Set<string>,
+	providers?: string[],
+): string | null {
+	log("[fuzzyMatchModel] called", { target, availableCount: available.size, providers })
+
+	if (available.size === 0) {
+		log("[fuzzyMatchModel] empty available set")
+		return null
+	}
+
+	const targetLower = target.toLowerCase()
+
+	// Filter by providers if specified
+	let candidates = Array.from(available)
+	if (providers && providers.length > 0) {
+		const providerSet = new Set(providers)
+		candidates = candidates.filter((model) => {
+			const [provider] = model.split("/")
+			return providerSet.has(provider)
+		})
+		log("[fuzzyMatchModel] filtered by providers", { candidateCount: candidates.length, candidates: candidates.slice(0, 10) })
+	}
+
+	if (candidates.length === 0) {
+		log("[fuzzyMatchModel] no candidates after filter")
+		return null
+	}
+
+	// Find all matches (case-insensitive substring match)
+	const matches = candidates.filter((model) =>
+		model.toLowerCase().includes(targetLower),
+	)
+
+	log("[fuzzyMatchModel] substring matches", { targetLower, matchCount: matches.length, matches })
+
+	if (matches.length === 0) {
+		return null
+	}
+
+	// Priority 1: Exact match
+	const exactMatch = matches.find((model) => model.toLowerCase() === targetLower)
+	if (exactMatch) {
+		log("[fuzzyMatchModel] exact match found", { exactMatch })
+		return exactMatch
+	}
+
+	// Priority 2: Shorter model name (more specific)
+	const result = matches.reduce((shortest, current) =>
+		current.length < shortest.length ? current : shortest,
+	)
+	log("[fuzzyMatchModel] shortest match", { result })
+	return result
+}
+
+let cachedModels: Set<string> | null = null
+
+export async function fetchAvailableModels(client: any): Promise<Set<string>> {
+	if (cachedModels !== null) {
+		log("[fetchAvailableModels] returning cached models", { count: cachedModels.size, models: Array.from(cachedModels).slice(0, 20) })
+		return cachedModels
+	}
+
+	try {
+		const models = await client.model.list()
+		const modelSet = new Set<string>()
+
+		log("[fetchAvailableModels] raw response", { isArray: Array.isArray(models), length: Array.isArray(models) ? models.length : 0, sample: Array.isArray(models) ? models.slice(0, 5) : models })
+
+		if (Array.isArray(models)) {
+			for (const model of models) {
+				if (model.id && typeof model.id === "string") {
+					modelSet.add(model.id)
+				}
+			}
+		}
+
+		log("[fetchAvailableModels] parsed models", { count: modelSet.size, models: Array.from(modelSet) })
+
+		cachedModels = modelSet
+		return modelSet
+	} catch (err) {
+		log("[fetchAvailableModels] error", { error: String(err) })
+		return new Set<string>()
+	}
+}
+
+export function __resetModelCache(): void {
+	cachedModels = null
+}