@@ -31,3 +31,4 @@ from ml_model.services.stablediffusion import Stablediffusion
 from ml_model.services.upscaleai import Upscaleai
 from ml_model.services.vicuna import Vicuna
 from ml_model.services.whisper import Whisper
+from ml_model.services.geminiimage import Geminiimage
@@ -26,17 +26,45 @@ class Gemini(SimpleService):
         'gemini-2.0-flash-001': {
             'input': Decimal('30'),
             'output': Decimal('120'),
-            'input_imgs': Decimal('7.740'),
+            'input_imgs': Decimal('7.8'),
         },
+        'gemini-2.0-flash-lite-001': {
+            'input': Decimal('22.5'),
+            'output': Decimal('90'),
+        },
+        'gemini-2.5-pro': {
+            'input': Decimal('375'),
+            'output': Decimal('3000'),
+            'input_imgs': Decimal('1548'),
+            'highest_prices': {
+                'input': Decimal('750'),
+                'output': Decimal('4500')
+            }
+        },
+        'gemini-2.5-flash': {
+            'input': Decimal('90'),
+            'output': Decimal('750'),
+            'input_imgs': Decimal('371.4'),
+        },
+        'gemini-2.5-flash-lite': {
+            'input': Decimal('30'),
+            'output': Decimal('120'),
+        }
     }
 
     def calculate_price(
         self, version: str, input_tokens: int, output_tokens: int, image: FieldFile
     ) -> Decimal:
         price_map = self.TOKENS_COST[version.split('/')[1]]
-        price = (
-            input_tokens * price_map['input'] / 1_000_000 + output_tokens * price_map['output'] / 1_000_000
-        )
+        if version.split('/')[1] == 'gemini-2.5-pro' and input_tokens > 200_000:
+            price = (
+                input_tokens * price_map['highest_prices']['input'] / 1_000_000
+                + output_tokens * price_map['highest_prices']['output'] / 1_000_000
+            )
+        else:
+            price = (
+                input_tokens * price_map['input'] / 1_000_000 + output_tokens * price_map['output'] / 1_000_000
+            )
         if image:
             price += price_map['input_imgs'] / 1_000
         return price.quantize(Decimal('0.1'), rounding='ROUND_UP')
@@ -0,0 +1,73 @@
+import base64
+import time
+from datetime import timedelta
+from decimal import Decimal
+from io import BytesIO
+
+import requests
+from django.core.files import File
+import filetype
+
+from messages.models import Message
+from ml_model.services.base import SimpleService
+from ml_model.tasks import fal_ai_run
+
+
+class Geminiimage(SimpleService):
+    """
+    Gemini Service
+    contains abstract method make, which makes a generation
+    """
+
+    TOKENS_COST = Decimal('11.7')
+
+    def calculate_price(self, num_images: int) -> Decimal:
+        price = num_images * self.TOKENS_COST
+        return price.quantize(Decimal('0.1'), rounding='ROUND_UP')
+
+
+    def save_results(self, content: str, t: timedelta, images: list[str], save: bool = True) -> list[Message]:
+        msgs = []
+        for image in images:
+            msgs.append(
+                Message(
+                    content=content,
+                    content_object=self.store,
+                    elapsed_time=t,
+                    file=File(BytesIO(requests.get(image).content), '.png')
+                )
+            )
+        if save:
+            return Message.objects.bulk_create(msgs)
+        return msgs
+
+    def make(self, input_message: Message, save: bool = True) -> list[Message]:
+        callback_data = dict(
+            {
+                'prompt': self.translate_prompt(input_message.content),
+                **input_message.info,
+            }
+        )
+        file = input_message.file
+        if file:
+            kind = filetype.guess(file.read(20))
+            mime = kind.mime if kind else 'application/octet-stream'
+            file.seek(0)
+            image = f'data:{mime};base64,{base64.b64encode(file.read()).decode("utf-8")}'
+            file.close()
+            callback_data.update(
+                {'image_urls': [image]}
+            )
+        start_time = time.time()
+        result = fal_ai_run(
+            'fal-ai/gemini-25-flash-image' if not file else 'fal-ai/gemini-25-flash-image/edit',
+            callback_data
+        )
+        images = [r['url'] for r in result['images']]
+        process_time = timedelta(seconds=(time.time() - start_time))
+        self.handle_invoice(
+            input_message.content_object.model,
+            num_images=input_message.info['num_images'],
+        )
+        msgs = self.save_results(input_message.content, process_time, images, save)
+        return msgs
@@ -142,7 +142,9 @@ def openrouter_run(version: str, messages: list, callback_data: dict, model_name
                 )
                 reasoning = re.sub(r'Вывод:|Основная мысль:|Рассуждение:|\*\*', '', reasoning)
                 answer = reasoning
-                if reasoning and content:
+                if 'google/gemini' in data['model']:
+                    answer = content
+                elif reasoning and content:
                     # TODO: переделать рендеринг сообщения на Jinja 2
                     answer = f'**Рассуждение:**\n\n{reasoning}\n\n**Основная мысль:**\n\n{content}'
                 elif content:
@@ -182,12 +184,11 @@ def fal_ai_run(model, payload):
     while True:
         status = client.get(result['status_url']).json()
         if status.get('status') == 'COMPLETED':
-            break
+            return client.get(status.get('response_url')).json()
         requests_number += 1
         if requests_number == 271:
             raise ModelTimeoutError
         time.sleep(1 / 3)
-    return client.get(result['response_url']).json()['images']
 
 
 @shared_task