@@ -79,6 +79,10 @@ class Chatgpt(SimpleService):
             'input': Decimal('0.001375'),
             'output': Decimal('0.005500'),
         },
+        'gpt-4.5-preview': {
+            'input': Decimal('0.075'),
+            'output': Decimal('0.075'),
+        },
     }
 
     def __init__(self, store: BaseStore) -> None:
@@ -121,7 +125,8 @@ class Chatgpt(SimpleService):
                 image_url = f'data:{mime},base64,{base64.b64encode(buf.getvalue()).decode("utf-8")}'
                 buf.close()
                 image_size = normalized_image.size
-                input_content.append({'type': 'image_url', 'image_url': {'url': image_url}})
+                image_data = {'type': 'image_url', 'image_url': {'url': image_url}}
+                input_content.append(image_data)
             except UnidentifiedImageError:
                 raise Exception(_('Unable to recognize the image. (Supported formats are PNG, JPG, JPEG)'))
         for proxy in Proxy.objects.all():
@@ -153,6 +158,7 @@ class Chatgpt(SimpleService):
                 'o1-preview',
                 'o1-mini',
                 'o3-mini',
+                'gpt-4.5-preview',
             ):
                 self.llm.tiktoken_model_name = 'gpt-4'
             chat_history = self.get_chat_history()
@@ -167,10 +173,48 @@ class Chatgpt(SimpleService):
                 input_tokens = self.count_text_tokens([llm_input])
             else:
                 input_tokens = self.count_text_tokens([*chat_history.messages, llm_input])
+            output_tokens = 0
             self.assert_enough_balance(input_tokens, image_size, model=self.llm.model_name)
-            if image:
+            if image and model_name not in ('o3-mini', 'gpt-4.5-preview'):
                 response = self.llm.invoke([llm_input])
                 chat_history.add_ai_message(response)
+            elif model_name in ('o3-mini', 'gpt-4.5-preview'):
+                with httpx.Client(
+                    base_url='https://api.openai.com/v1',
+                    proxy=f'{proxy.protocol}://{proxy.address}',
+                    headers={'Authorization': f'Bearer {settings.OPENAI_API_KEY}'},
+                    timeout=600,
+                ) as client:
+                    messages = [
+                        {'role': 'user' if isinstance(msg, HumanMessage) else 'assistant', 'content': msg.content}
+                        for msg in chat_history.messages
+                    ]
+                    if image:
+                        messages[-1]['content'] = [
+                            {'type': 'text', 'text': input_message.content},
+                            image_data,
+                        ]
+                    resp = client.post(
+                        'chat/completions',
+                        json={
+                            'model': model_name,
+                            'messages': messages
+                        },
+                    )
+                    if (
+                        (data := resp.json())
+                        and data.get('choices')
+                        and (
+                            content := ','.join(
+                                [choice['message']['content'] for choice in data.get('choices')]
+                            )
+                        )
+                    ):
+                        input_tokens = resp.json()['usage']['prompt_tokens']
+                        output_tokens = resp.json()['usage']['completion_tokens']
+                        response = AIMessage(content=content)
+                    else:
+                        raise Exception('GPT not answer correctly, please retry later')
             elif file:
                 human_messages = []
                 chunk_responses = ['Содержание файла: ']
@@ -226,37 +270,6 @@ class Chatgpt(SimpleService):
                         }
                     )['output']
                 )
-            elif model_name == 'o3-mini':
-                with httpx.Client(
-                    base_url='https://api.openai.com/v1',
-                    proxy=f'{proxy.protocol}://{proxy.address}',
-                    headers={'Authorization': f'Bearer {settings.OPENAI_API_KEY}'},
-                    timeout=600,
-                ) as client:
-                    resp = client.post(
-                        'chat/completions',
-                        json={
-                            'model': model_name,
-                            'messages': [
-                                {
-                                    'role': 'user',
-                                    'content': input_message.content,
-                                },
-                            ],
-                        },
-                    )
-                    if (
-                        (data := resp.json())
-                        and data.get('choices')
-                        and (
-                            content := ','.join(
-                                [choice['message']['content'] for choice in data.get('choices')]
-                            )
-                        )
-                    ):
-                        response = AIMessage(content=content)
-                    else:
-                        raise Exception('GPT not answer correctly, please retry later')
             else:
                 # Somehow this chain doesn't support Vision, even though ChatOpenAI (above) does.
                 response = conversation.invoke(
@@ -265,13 +278,14 @@ class Chatgpt(SimpleService):
                 )
                 chat_history.add_ai_message(response)
 
-            output_tokens = self.count_text_tokens([response])
+            if output_tokens == 0:
+                output_tokens = self.count_text_tokens([response])
             if file and not image:
                 output_tokens += self.count_text_tokens(
                     [AIMessage(chunk_response) for chunk_response in chunk_responses]
                 )
 
-            if image and normalized_image:
+            if image and normalized_image and model_name not in ('o3-mini', 'gpt-4.5-preview'):
                 self.logger.info(f'Input количество токенов БЕЗ картинки {model_name} - {input_tokens}')
                 input_tokens += self.count_image_tokens(normalized_image.size, model_name)
 
@@ -28,7 +28,11 @@ class Claude(SimpleService):
             'input': Decimal('3000'),
             'output': Decimal('3000'),
             'input_imgs': Decimal('960'),
-        },  # 1M tokens
+        },
+        'claude-3.5-haiku': {
+            'input': Decimal('800'),
+            'output': Decimal('800'),
+        }, # 1M tokens
     }
 
     def calculate_price(