fix: remove name-based image-input detection (#874) (#877)

supportsImageInput() guessed multimodal capability from the model id
string. The heuristic misfired on newer models (e.g. kimi-k3.6, qwen36),
either wrongly rejecting images for capable models or letting them through.

The AI SDK does not emit a warning when an OpenAI-compatible endpoint
silently drops an image, so the guess was the only signal — but an
unreliable one. Drop the detection entirely and let the real provider
error surface instead (already translated to a friendly message in
chat-panel.tsx). Validation falls back to "valid" on any model error.

- Remove supportsImageInput() and its pre-send check in chat route
- Drop the vision-capability throw in getValidationModel()
- Remove the corresponding unit tests

lib/ai-providers.ts

@@ -1419,77 +1419,14 @@ export function supportsPromptCaching(modelId: string): boolean {
     )
 }
 
-/**
- * Check if a model supports image/vision input.
- * Some models silently drop image parts without error (AI SDK warning only).
- */
-export function supportsImageInput(modelId: string): boolean {
-    const lowerModelId = modelId.toLowerCase()
-
-    // Helper to check if model has vision capability indicator
-    const hasVisionIndicator =
-        lowerModelId.includes("vision") || lowerModelId.includes("vl")
-
-    // Models that DON'T support image/vision input (unless vision variant)
-    // Kimi K2 doesn't support images, but K2.5 does
-    // Only block kimi-k2 specifically, not other Kimi models
-    if (
-        (lowerModelId.includes("kimi-k2") ||
-            lowerModelId.includes("kimi_k2")) &&
-        !hasVisionIndicator &&
-        !lowerModelId.includes("2.5") &&
-        !lowerModelId.includes("k2.5")
-    ) {
-        return false
-    }
-
-    // Moonshot text models (moonshot-v1 series are text-only)
-    if (lowerModelId.includes("moonshot-v1") && !hasVisionIndicator) {
-        return false
-    }
-
-    // MiniMax text models (MiniMax-M2.x series are text-only; M3 supports image input)
-    if (
-        lowerModelId.includes("minimax") &&
-        !hasVisionIndicator &&
-        !lowerModelId.includes("m3")
-    ) {
-        return false
-    }
-
-    // DeepSeek text models (not vision variants)
-    if (lowerModelId.includes("deepseek") && !hasVisionIndicator) {
-        return false
-    }
-
-    // Qwen text models (not vision variants like qwen-vl)
-    // Qwen3.5 series (qwen3.5, qwen3.5-plus, qwen3.5-flash) natively support image input
-    // QvQ (Qwen Visual QA) models are vision models — exclude them even when prefixed with "qwen/"
-    if (
-        lowerModelId.includes("qwen") &&
-        !hasVisionIndicator &&
-        !lowerModelId.includes("qwen3.5") &&
-        !lowerModelId.includes("qvq")
-    ) {
-        return false
-    }
-
-    // GLM text models (not vision variants)
-    // GLM vision models: glm-4v, glm-4v-9b, glm-4.1v-9b-thinking
-    if (lowerModelId.includes("glm") && !hasVisionIndicator) {
-        if (!/[\d.]v/.test(lowerModelId)) {
-            return false
-        }
-    }
-
-    // Default: assume model supports images
-    return true
-}
-
 /**
  * Get the AI model for diagram validation.
  * Uses VALIDATION_MODEL env var if set, otherwise falls back to AI_MODEL.
- * Throws if the model doesn't support image input.
+ *
+ * Note: we no longer guess whether the model supports image input from its
+ * name — that heuristic misfired on newer models (see issue #874). If a
+ * configured validation model can't handle images, the API call simply errors
+ * and the validate-diagram route falls back to "valid".
  */
 export function getValidationModel(): ReturnType<typeof getAIModel>["model"] {
     // AI_MODEL may be comma-separated (multi-model fallback); pick the first.
@@ -1502,12 +1439,6 @@ export function getValidationModel(): ReturnType<typeof getAIModel>["model"] {
         )
     }
 
-    if (!supportsImageInput(modelId)) {
-        throw new Error(
-            `Validation requires a vision-capable model. Model "${modelId}" does not support image input.`,
-        )
-    }
-
     const { model } = getAIModel({ modelId })
     return model
 }