LLM Deep Dive 2025: Why Claude 4 and GPT-5.1 Change Everything

Source: Dev.to

Context Management: From Token Limits to Intelligent Summarization

Maintaining coherent, relevant context across extended interactions has always been a challenge. By late 2025 both OpenAI and Anthropic have moved beyond merely increasing token limits to implementing smarter context‑management strategies.

OpenAI – The Responses API

• Shift in strategy – The original Assistants API (v1, deprecated in late 2024) gave way to Assistants API v2, and now the Responses API (released Mar 11 2025).
• Why it matters – The Responses API is built from the ground up to handle conversation history and context more efficiently, abstracting away much of the manual state management developers previously had to implement.
• Technical underpinnings –
  • Sliding‑window attention: the model focuses on a recent segment of the conversation while intelligently summarizing or discarding less‑relevant older information.
  • Quadratic cost mitigation: despite massive token‑window increases, attention remains quadratic, so summarization is essential.
• Model highlight – GPT‑4.1 excels at coding tasks and demonstrates improved context retention over long codebases.

Anthropic – Claude’s Expanding Windows

• Why it matters – These windows enable digestion of entire books, extensive codebases, or multi‑hour refactoring sessions.
• SDK helper – The Anthropic SDK ships with a compaction_control helper that automatically summarizes and clears context when predefined thresholds are reached, eliminating the need for custom compaction logic.

Using compaction_control with Claude (Python Example)

import anthropic
import os

# Ensure your ANTHROPIC_API_KEY is set as an environment variable
# os.environ["ANTHROPIC_API_KEY"] = "YOUR_ANTHROPIC_API_KEY"

client = anthropic.Anthropic()

# ----------------------------------------------------------------------
# Configuration for automatic context compaction
# ----------------------------------------------------------------------
compaction_settings = {
    "token_threshold": 5_000,                     # Summarize when history > 5k tokens
    "summarization_model": "claude-sonnet-4.5-20251130",
    "summarization_prompt": (
        "Summarize the preceding conversation for Claude, focusing on key facts "
        "and the user's ultimate goal to help it continue accurately."
    ),
}

def chat_with_claude_with_compaction(user_message: str, history: list):
    """Send a message to Claude, automatically compacting the history if needed."""
    # Append the new user message to the history
    history.append({"role": "user", "content": user_message})

    try:
        response = client.messages.create(
            model="claude-opus-4.1-20250805",
            max_tokens=1_024,
            messages=history,
            compaction_control=compaction_settings,
        )
        assistant_response = response.content[0].text
        history.append({"role": "assistant", "content": assistant_response})
        return assistant_response
    except Exception as e:
        print(f"An error occurred: {e}")
        return "Sorry, I encountered an error."

# ----------------------------------------------------------------------
# Example usage
# ----------------------------------------------------------------------
conversation_history = []

print("User: Hello, I need help planning a complex project.")
resp = chat_with_claude_with_compaction(
    "Hello, I need help planning a complex project. It involves multiple stakeholders and strict deadlines.",
    conversation_history,
)
print(f"Claude: {resp}")

print("\nUser: The project scope has expanded significantly. We now need to integrate three new modules.")
resp = chat_with_claude_with_compaction(
    "The project scope has expanded significantly. We now need to integrate three new modules. How does this impact our timeline?",
    conversation_history,
)
print(f"Claude: {resp}")

Note: Even with massive context windows, prompting for optimal retrieval and synthesis remains a skill. Careful prompt engineering is still required, especially when “needle‑in‑a‑haystack” retrieval is critical.

Tool Use & Agentic Workflows

The ability for LLMs to interact with external systems—databases, APIs, code interpreters—has turned them into powerful agents. Both OpenAI and Anthropic have refined their tool‑use capabilities, moving toward more autonomous and efficient orchestration.

Anthropic’s Recent Enhancements (Nov 2025)

1. Programmatic Tool Calling – Claude can now generate and execute code that invokes multiple tools directly within a managed execution environment, dramatically reducing latency and token consumption by eliminating round‑trips through the API.
2. (Additional features would be listed here…)

These improvements further the vision of truly autonomous AI assistants that can manage complex, multi‑step tasks with minimal developer overhead.

Tool Search and Use Enhancements

• Tool Search – Addresses the challenge of managing vast numbers of tools. Instead of loading all tool definitions upfront, Claude can dynamically discover and load only the tools it needs via a new search capability.
• Tool Use Examples – Developers can now add concrete usage patterns directly into tool definitions. These examples, formatted exactly as real LLM output, improve Claude’s tool‑use performance by demonstrating when and how to use a tool.

OpenAI’s Responses API

OpenAI’s approach, particularly with the new Responses API, also emphasizes robust tool integration.

• The Assistants API v2 already provided improved function calling and access to OpenAI‑hosted tools like Code Interpreter and File Search.
• The Responses API is designed to integrate these tools even more seamlessly.
• It continues to allow developers to define custom tools using JSON schemas, which the model can then call.

Claude – Programmatic Tool Calling (Python Example)

import anthropic
import json
import os

client = anthropic.Anthropic()

def get_user_profile(user_id: str):
    if user_id == "user123":
        return {
            "id": "user123",
            "name": "Alice Smith",
            "email": "alice@example.com",
            "plan": "premium"
        }
    return {"error": "User not found"}

def update_user_subscription(user_id: str, new_plan: str):
    if user_id == "user123":
        return {
            "status": "success",
            "user_id": user_id,
            "old_plan": "premium",
            "new_plan": new_plan
        }
    return {"error": "User not found"}

tools = [
    {
        "name": "get_user_profile",
        "description": "Retrieves the profile information for a given user ID.",
        "input_schema": {
            "type": "object",
            "properties": {
                "user_id": {
                    "type": "string",
                    "description": "The ID of the user."
                }
            },
            "required": ["user_id"]
        }
    },
    {
        "name": "update_user_subscription",
        "description": "Updates the subscription plan for a user.",
        "input_schema": {
            "type": "object",
            "properties": {
                "user_id": {
                    "type": "string",
                    "description": "The ID of the user."
                },
                "new_plan": {
                    "type": "string",
                    "description": "The new subscription plan."
                }
            },
            "required": ["user_id", "new_plan"]
        }
    }
]

def chat_with_claude_tools(user_message: str, history: list):
    """Send a message to Claude with tool definitions attached."""
    history.append({"role": "user", "content": user_message})
    response = client.messages.create(
        model="claude-opus-4.1-20250805",
        max_tokens=2048,
        messages=history,
        tools=tools
    )
    # Logic to handle `tool_code` or `tool_use` stop reasons would follow here
    return response.content[0].text

This programmatic approach signifies a move toward more robust, less error‑prone agentic behavior, where the LLM’s reasoning is expressed in code rather than just natural‑language prompts for tool invocation.

Multimodal Capabilities (2025)

Multimodal capabilities have shifted from futuristic demos to practical, API‑driven applications.

OpenAI – GPT‑4o & GPT‑5.1 Series

• GPT‑4o (“Omni”) – Released May 2024; unifies text, audio, and image modalities in a single neural network.
• API deprecation – GPT‑4o’s API access ends February 2026, making way for the more powerful GPT‑5.1 series and specialized models o3 and o4‑mini (released April 2025).
• Key features – Accept image inputs, respond with text and images; support “multi‑modal chain‑of‑thought” reasoning across modalities.

Anthropic – Claude Vision

• Claude Opus & Sonnet models now include vision capabilities.
• Useful for document analysis, diagram interpretation, and visual content moderation.

OpenAI Multimodal Example (Python)

import openai
import base64
import requests
import os

def encode_image(image_path):
    """Read an image file and return a base64‑encoded string."""
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

def analyze_image_with_openai_multimodal(image_path: str, prompt: str):
    """Send an image + text prompt to a multimodal OpenAI model."""
    base64_image = encode_image(image_path)

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"
    }

    payload = {
        "model": "gpt-5.1-latest-20251115",
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        "max_tokens": 500
    }

    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers=headers,
        json=payload
    )
    return response.json()["choices"][0]["message"]["content"]

While impressive, multimodal models still present challenges:

• Fine‑grained object recognition or complex spatial reasoning can be less robust than dedicated computer‑vision models.
• Ambiguous visual cues or highly domain‑specific imagery may still lead to “hallucinations.”

The Rise of Autonomous Agentic Workflows (Late 2025)

The shift from simple prompt‑response cycles to complex, autonomous agentic workflows defines the current era.

• Developers are building multi‑step systems where LLMs act as intelligent orchestrators: they reason over tasks, select tools, execute actions, and refine their approach based on feedback.
• Research such as AI Agents 2025: Why AutoGPT and CrewAI Still Struggle with Autonomy highlights existing limitations of self‑directed systems.
• Native platforms from OpenAI and Anthropic aim to bridge those gaps, offering tighter tool integration, dynamic tool discovery, and richer multimodal reasoning.

AI Agents Landscape (2025)

New Agents platform – built upon the Responses API – is at the forefront of this movement. It introduces concepts such as:

• Persistent threads for conversational memory
• Access to OpenAI‑hosted tools: Web Search, File Search, and Computer Use

The Agents SDK with Tracing provides crucial observability into these complex workflows, allowing developers to debug and understand an agent’s decision‑making process.

Anthropic’s Agentic Capabilities

Anthropic is heavily invested in agentic solutions, especially for enterprise use cases:

• Claude Code – a specialized agent for programming assistance (now bundled into Team and Enterprise subscriptions)
• Claude Artifacts – another specialized agent for creative tasks

The Compliance API lets IT and security leaders programmatically access usage and content metrics, which is essential for governing AI‑assisted coding across large teams.

Popular Agentic AI Frameworks

A robust ecosystem of frameworks has matured, providing the architectural scaffolding needed to build sophisticated agents while abstracting away much of the complexity of state management and tool orchestration:

• LangChain
• CrewAI
• AutoGen (Microsoft)
• Phidata
• LlamaIndex
• LangGraph (part of LangChain)

These frameworks are widely adopted across the industry.

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