diff --git a/src/capi_frontend/server_settings.hpp b/src/capi_frontend/server_settings.hpp
index 1cfb9919ec..4b8f851184 100644
--- a/src/capi_frontend/server_settings.hpp
+++ b/src/capi_frontend/server_settings.hpp
@@ -226,6 +226,10 @@ struct ServerSettingsImpl {
     std::optional<std::vector<std::string>> allowedMediaDomains;
     std::string logLevel = "INFO";
     std::string logPath;
+    // When enabled, LLM chat/text completion unary responses include an extra
+    // "__verbose" object with the raw model input (after chat template) and the
+    // raw model output (before tool/reasoning parsing). Inspired by llama.cpp -v.
+    bool verboseResponse = false;
     bool allowCredentials = false;
     std::string allowedOrigins{"*"};
     std::string allowedMethods{"*"};
diff --git a/src/cli_parser.cpp b/src/cli_parser.cpp
index e6f6365c26..6eb770d1b6 100644
--- a/src/cli_parser.cpp
+++ b/src/cli_parser.cpp
@@ -105,6 +105,12 @@ std::variant<bool, std::pair<int, std::string>> CLIParser::parse(int argc, char*
             ("log_path",
                 "Optional path to the log file",
                 cxxopts::value<std::string>(), "LOG_PATH")
+            ("verbose_response",
+                "When enabled, LLM chat/text completion unary responses include an extra "
+                "\"__verbose\" object with the raw prompt (after chat template) and the raw "
+                "model output (before tool/reasoning parsing). Useful for debugging.",
+                cxxopts::value<bool>()->default_value("false"),
+                "VERBOSE_RESPONSE")
 #ifdef MTR_ENABLED
             ("trace_path",
                 "Path to the trace file",
@@ -522,6 +528,8 @@ void CLIParser::prepareServer(ServerSettingsImpl& serverSettings) {
         serverSettings.logLevel = result->operator[]("log_level").as<std::string>();
     if (result->count("log_path"))
         serverSettings.logPath = result->operator[]("log_path").as<std::string>();
+    if (result->count("verbose_response"))
+        serverSettings.verboseResponse = result->operator[]("verbose_response").as<bool>();
 
     if (result->count("grpc_channel_arguments"))
         serverSettings.grpcChannelArguments = result->operator[]("grpc_channel_arguments").as<std::string>();
diff --git a/src/llm/apis/openai_api_handler.hpp b/src/llm/apis/openai_api_handler.hpp
index 7c56bcbf95..8c1dd0357a 100644
--- a/src/llm/apis/openai_api_handler.hpp
+++ b/src/llm/apis/openai_api_handler.hpp
@@ -99,6 +99,16 @@ class OpenAIApiHandler {
     // Output parser is used to parse chat completions response to extract specific fields like tool calls and reasoning.
     std::unique_ptr<OutputParser> outputParser = nullptr;
 
+    // Verbose response support (enabled via --verbose_response). When set, the
+    // serialized unary response includes a "__verbose" object with the raw prompt
+    // (post chat template) and raw decoded model output (before tool/reasoning
+    // parsing). Inspired by llama.cpp -v.
+    bool verboseResponse = false;
+    std::string verbosePrompt;
+    // Streaming accumulators for raw model output.
+    std::vector<int64_t> verboseRawTokens;
+    std::string verboseRawText;
+
     // Shared parsing helpers
     absl::Status parseCommonPart(std::optional<uint32_t> maxTokensLimit, uint32_t bestOfLimit, std::optional<uint32_t> maxModelLength);
     absl::Status parseResponseFormat();
@@ -156,6 +166,26 @@ class OpenAIApiHandler {
     std::string getToolChoice() const;
     const std::unique_ptr<OutputParser>& getOutputParser() const;
 
+    // Verbose response configuration
+    void enableVerboseResponse(const std::string& promptAfterTemplate) {
+        verboseResponse = true;
+        verbosePrompt = promptAfterTemplate;
+    }
+    bool isVerboseResponse() const { return verboseResponse; }
+    const std::string& getVerbosePrompt() const { return verbosePrompt; }
+    // Accumulators used to assemble the "raw model output" for streaming responses.
+    void appendVerboseRawTokens(const std::vector<int64_t>& tokens) {
+        verboseRawTokens.insert(verboseRawTokens.end(), tokens.begin(), tokens.end());
+    }
+    void appendVerboseRawText(const std::string& chunk) {
+        verboseRawText.append(chunk);
+    }
+    void setVerboseRawText(std::string text) {
+        verboseRawText = std::move(text);
+    }
+    const std::vector<int64_t>& getVerboseRawTokens() const { return verboseRawTokens; }
+    const std::string& getVerboseRawText() const { return verboseRawText; }
+
     // Usage tracking
     void setPromptTokensUsage(size_t promptTokens);
     void setCompletionTokensUsage(size_t completionTokens);
diff --git a/src/llm/apis/openai_completions.cpp b/src/llm/apis/openai_completions.cpp
index 2fd5e12005..89009c0d74 100644
--- a/src/llm/apis/openai_completions.cpp
+++ b/src/llm/apis/openai_completions.cpp
@@ -394,6 +394,17 @@ std::string OpenAIChatCompletionsHandler::serializeUnaryResponse(const std::vect
     // TODO: system_fingerprint: string; This fingerprint represents the backend configuration that the model runs with.
     // Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
 
+    if (isVerboseResponse()) {
+        jsonResponse.StartObject("__verbose");
+        jsonResponse.String("prompt", getVerbosePrompt());
+        std::string rawContent;
+        if (!generationOutputs.empty()) {
+            rawContent = tokenizer.decode(generationOutputs.front().generated_ids, ov::genai::skip_special_tokens(false));
+        }
+        jsonResponse.String("content", rawContent);
+        jsonResponse.EndObject();
+    }
+
     // finish response object
     jsonResponse.EndObject();
     return jsonResponse.ToString();
@@ -458,6 +469,17 @@ std::string OpenAIChatCompletionsHandler::serializeUnaryResponse(ov::genai::Enco
     // TODO: system_fingerprint: string; This fingerprint represents the backend configuration that the model runs with.
     // Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
 
+    if (isVerboseResponse()) {
+        jsonResponse.StartObject("__verbose");
+        jsonResponse.String("prompt", getVerbosePrompt());
+        std::string rawContent;
+        if (!results.tokens.empty()) {
+            rawContent = tokenizer.decode(results.tokens.front(), ov::genai::skip_special_tokens(false));
+        }
+        jsonResponse.String("content", rawContent);
+        jsonResponse.EndObject();
+    }
+
     // finish response object
     jsonResponse.EndObject();
     return jsonResponse.ToString();
@@ -528,6 +550,14 @@ std::string OpenAIChatCompletionsHandler::serializeUnaryResponse(ov::genai::VLMD
     // TODO: system_fingerprint: string; This fingerprint represents the backend configuration that the model runs with.
     // Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
 
+    if (isVerboseResponse()) {
+        jsonResponse.StartObject("__verbose");
+        jsonResponse.String("prompt", getVerbosePrompt());
+        // For VLM the raw decoded text is provided by GenAI directly.
+        jsonResponse.String("content", textResponse);
+        jsonResponse.EndObject();
+    }
+
     // finish response object
     jsonResponse.EndObject();
     return jsonResponse.ToString();
@@ -622,6 +652,21 @@ std::string OpenAIChatCompletionsHandler::serializeStreamingChunk(const std::str
     // TODO: system_fingerprint: string; This fingerprint represents the backend configuration that the model runs with.
     // Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
 
+    // Verbose mode: attach prompt and raw model output to the FINAL chunk only.
+    if (isVerboseResponse() && finishReason != ov::genai::GenerationFinishReason::NONE) {
+        std::string rawOutput;
+        if (!getVerboseRawTokens().empty()) {
+            rawOutput = tokenizer.decode(getVerboseRawTokens(), ov::genai::skip_special_tokens(false));
+        } else {
+            rawOutput = getVerboseRawText();
+        }
+
+        Value verboseObject(kObjectType);
+        verboseObject.AddMember("prompt", Value(getVerbosePrompt().c_str(), allocator), allocator);
+        verboseObject.AddMember("content", Value(rawOutput.c_str(), allocator), allocator);
+        doc.AddMember("__verbose", verboseObject, allocator);
+    }
+
     StringBuffer buffer;
     Writer<StringBuffer> writer(buffer);
     doc.Accept(writer);
diff --git a/src/llm/language_model/legacy/servable.cpp b/src/llm/language_model/legacy/servable.cpp
index 8e244df219..7dffb22afe 100644
--- a/src/llm/language_model/legacy/servable.cpp
+++ b/src/llm/language_model/legacy/servable.cpp
@@ -234,6 +234,9 @@ absl::Status LegacyServable::preparePartialResponse(std::shared_ptr<GenAiServabl
         }
         // Legacy generation path always runs with batch=1, so we read the single finish reason at index 0.
         ov::genai::GenerationFinishReason finishReason = legacyExecutionContext->results.finish_reasons.empty() ? ov::genai::GenerationFinishReason::STOP : legacyExecutionContext->results.finish_reasons[0];
+        if (executionContext->apiHandler->isVerboseResponse() && !legacyExecutionContext->results.tokens.empty()) {
+            executionContext->apiHandler->appendVerboseRawTokens(legacyExecutionContext->results.tokens[0]);
+        }
         std::string serializedChunk = executionContext->apiHandler->serializeStreamingChunk(lastTextChunk, finishReason);
         if (!serializedChunk.empty()) {
             executionContext->response = wrapTextInServerSideEventMessage(serializedChunk);
diff --git a/src/llm/servable.cpp b/src/llm/servable.cpp
index cb8d87fcee..b4d5ca8185 100644
--- a/src/llm/servable.cpp
+++ b/src/llm/servable.cpp
@@ -269,6 +269,9 @@ absl::Status GenAiServable::prepareInputs(std::shared_ptr<GenAiServableExecution
     case Endpoint::TOKENIZE:
         return absl::InternalError("Tokenize endpoint should not reach prepareInputs stage");
     }
+    if (Config::instance().getServerSettings().verboseResponse) {
+        executionContext->apiHandler->enableVerboseResponse(inputText);
+    }
     bool encodeAddSpecialTokens = (executionContext->endpoint == Endpoint::COMPLETIONS);
     executionContext->inputIds = getProperties()->tokenizer.encode(inputText, ov::genai::add_special_tokens(encodeAddSpecialTokens)).input_ids;
     if (getProperties()->maxModelLength.has_value()) {
@@ -305,6 +308,9 @@ absl::Status GenAiServable::preparePartialResponse(std::shared_ptr<GenAiServable
     }
     auto& generationOutput = executionContext->generationOutputs[0];
     executionContext->apiHandler->incrementProcessedTokens(generationOutput.generated_ids.size());
+    if (executionContext->apiHandler->isVerboseResponse()) {
+        executionContext->apiHandler->appendVerboseRawTokens(generationOutput.generated_ids);
+    }
 
     std::stringstream ss;
     executionContext->textStreamer->write(generationOutput.generated_ids);
diff --git a/src/llm/visual_language_model/continuous_batching/servable.cpp b/src/llm/visual_language_model/continuous_batching/servable.cpp
index 13b7e73a62..7779d9c0be 100644
--- a/src/llm/visual_language_model/continuous_batching/servable.cpp
+++ b/src/llm/visual_language_model/continuous_batching/servable.cpp
@@ -22,6 +22,7 @@
 #include <unordered_map>
 #include <vector>
 
+#include "../../../config.hpp"
 #include "../../../logging.hpp"
 #include "../../text_utils.hpp"
 #include "../../../tokenize/tokenize_parser.hpp"
@@ -121,6 +122,10 @@ absl::Status VisualLanguageModelServable::prepareInputs(std::shared_ptr<GenAiSer
         return absl::InvalidArgumentError("Unsupported endpoint");
     }
 
+    if (Config::instance().getServerSettings().verboseResponse) {
+        vlmExecutionContext->apiHandler->enableVerboseResponse(vlmExecutionContext->inputText);
+    }
+
     // Below logic is used only for the statistics and debugging purposes and does not affect the model execution.
     SPDLOG_LOGGER_TRACE(llm_calculator_logger, "VLM input text: {}", vlmExecutionContext->inputText);
     bool encodeAddSpecialTokens = false;  // assuming chat template application added special tokens
diff --git a/src/llm/visual_language_model/legacy/servable.cpp b/src/llm/visual_language_model/legacy/servable.cpp
index 033cb8641d..9c8e02c5df 100644
--- a/src/llm/visual_language_model/legacy/servable.cpp
+++ b/src/llm/visual_language_model/legacy/servable.cpp
@@ -224,6 +224,9 @@ absl::Status VisualLanguageModelLegacyServable::preparePartialResponse(std::shar
         lastTextChunk = executionContext->lastStreamerCallbackOutput;
         executionContext->lastStreamerCallbackOutput = "";
     }
+    if (executionContext->apiHandler->isVerboseResponse() && !lastTextChunk.empty()) {
+        executionContext->apiHandler->appendVerboseRawText(lastTextChunk);
+    }
     if (generationStatus != std::future_status::ready) {  // continue
         // For RESPONSES endpoint, always call serializeStreamingChunk so that
         // output item initialization events are emitted even before the tokenizer produces text.
@@ -244,6 +247,9 @@ absl::Status VisualLanguageModelLegacyServable::preparePartialResponse(std::shar
         // if streamer::put returned a value, streamer::end() result will not contain it, so we add it manually
         if (!executionContext->lastStreamerCallbackOutput.empty()) {
             lastTextChunk = lastTextChunk + executionContext->lastStreamerCallbackOutput;
+            if (executionContext->apiHandler->isVerboseResponse()) {
+                executionContext->apiHandler->appendVerboseRawText(executionContext->lastStreamerCallbackOutput);
+            }
         }
         if (legacyExecutionContext->results.finish_reasons.empty()) {
             SPDLOG_LOGGER_DEBUG(llm_calculator_logger, "Missing finish reason in legacy VLM streaming generation result, defaulting to STOP");
@@ -316,6 +322,10 @@ absl::Status VisualLanguageModelLegacyServable::prepareInputs(std::shared_ptr<Ge
         return absl::InvalidArgumentError("Unsupported endpoint");
     }
 
+    if (Config::instance().getServerSettings().verboseResponse) {
+        vlmExecutionContext->apiHandler->enableVerboseResponse(vlmExecutionContext->inputText);
+    }
+
     // Below logic is used only for the statistics and debugging purposes and does not affect the model execution.
     SPDLOG_LOGGER_TRACE(llm_calculator_logger, "VLM input text: {}", vlmExecutionContext->inputText);
     bool encodeAddSpecialTokens = false;  // assuming chat template application added special tokens