The 5 AI Models I'd Put Behind a Developer Agent

Before you start

You are building a developer agent that can edit code, run tests, and explain errors. The model you choose determines whether your agent feels like a junior intern or a senior engineer. This article walks through the five models I would put behind that agent, based on real API calls, latency, and output quality as of June 2025.

You will need an OpenAI, Anthropic, Google, or Meta API key. I will show you a minimal routing script you can run today to compare models on your own tasks.

mkdir agent-lab && cd agent-lab
python -m venv venv
source venv/bin/activate
pip install openai anthropic google-generativeai together

• Install Python 3.10+ and the openai and anthropic SDKs.
• Set environment variables: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, and optionally TOGETHER_API_KEY for open models.
• Create a working directory called agent-lab and keep all scripts there.

Step 1: Define your agent's workload

A developer agent does three things: it writes code, it reads and explains code, and it fixes errors. Each model has different strengths. I benchmarked them on a simple task: fix a bug in a Python function and explain the change.

Here is the buggy function I used for all tests.

def calculate_total(items):
    total = 0
    for item in items:
        total += item['price'] * item['quantity']
    return total

# Bug: if quantity is missing, it throws KeyError

• The function should handle missing quantity by defaulting to 1.
• The agent should produce a fix and a one-line explanation.
• Measure time to first token and total output tokens.

Step 2: The five models and why they made the list

After testing, I narrowed it down to five models. They cover the spectrum from frontier API models to open-weight models you can self-host.

Here is the list with my verdict.

• GPT-4o: best all-around for code generation and tool use. Low latency, good JSON mode.
• Claude 3.5 Sonnet: excellent at reasoning and long context. My choice for code review and refactoring.
• Gemini 1.5 Pro: huge context window (1M tokens). Good for whole-repo analysis.
• Llama 3.1 405B: open-weight, runs on your own hardware. Strong for privacy-sensitive work.
• DeepSeek Coder V2: specialized for code, very cheap API, surprisingly good at function-level edits.

Step 3: Set up a model router

You do not want to hardcode one model. A router picks the model based on the task. Here is a minimal Python router that the rest of the article uses.

Save this as router.py.

import os
from openai import OpenAI
from anthropic import Anthropic

openai_client = OpenAI()
anthropic_client = Anthropic()

def route(task):
    if task == 'codegen':
        return 'gpt-4o'
    elif task == 'review':
        return 'claude-3-5-sonnet-20240620'
    elif task == 'repo_analysis':
        return 'gemini-1.5-pro'
    elif task == 'local':
        return 'llama-3.1-405b'
    else:
        return 'deepseek-coder'

def call_model(model, prompt):
    if model.startswith('gpt'):
        resp = openai_client.chat.completions.create(
            model=model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        return resp.choices[0].message.content
    elif model.startswith('claude'):
        resp = anthropic_client.messages.create(
            model=model,
            max_tokens=1024,
            messages=[{'role': 'user', 'content': prompt}]
        )
        return resp.content[0].text
    # Add other providers similarly
    return 'Unsupported model'

• The router returns a model ID string based on the task.
• Each provider has its own SDK; the router hides that complexity.
• You can extend it to use LiteLLM for a unified interface.

Step 4: Test each model on the same bug

Run the same prompt through each model and compare the fixes. Use this script to call the router with the buggy function.

Save as test_models.py.

from router import route, call_model

prompt = """Fix the following Python function. The function should handle missing 'quantity' key by defaulting to 1.
Return only the corrected function and a one-line explanation.

```python
def calculate_total(items):
    total = 0
    for item in items:
        total += item['price'] * item['quantity']
    return total
```
"""

for task in ['codegen', 'review', 'repo_analysis', 'local']:
    model = route(task)
    print(f"--- {model} ---")
    print(call_model(model, prompt))
    print()

• Run it with python test_models.py.
• The output shows each model's fix and explanation.
• Notice which models add type hints or handle edge cases beyond the prompt.

Step 5: Measure latency and cost

Latency and cost matter when you run an agent loop with many calls. I wrote a quick script that measures time to first token for each model.

Save as measure.py.

import time
from router import route, call_model

prompt = "Write a Python function to reverse a linked list."

for task in ['codegen', 'review', 'repo_analysis', 'local']:
    model = route(task)
    start = time.time()
    call_model(model, prompt)
    elapsed = time.time() - start
    print(f"{model}: {elapsed:.2f} seconds")

• Run it a few times to get a feel for variability.
• Expect GPT-4o and DeepSeek to be fastest; Gemini may be slower on first token due to long context processing.
• For a developer agent, latency under 2 seconds is ideal for interactive use.

Step 6: Build a minimal agent loop

Now we put the model inside a simple agent that can run a command and feed the output back. This is the core loop of any coding agent.

Save as agent.py.

import subprocess
from router import route, call_model

def run_command(cmd):
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
    return result.stdout + result.stderr

def agent_loop(task, max_steps=3):
    model = route(task)
    messages = [{'role': 'user', 'content': f'Task: {task}\n\nUse the command tool to explore and fix.'}]
    for step in range(max_steps):
        response = call_model(model, messages)
        print(f"Step {step+1}: {response}")
        if '```bash' in response:
            cmd = response.split('```bash')[1].split('```')[0].strip()
            output = run_command(cmd)
            messages.append({'role': 'assistant', 'content': response})
            messages.append({'role': 'user', 'content': f'Command output: {output}'})
        else:
            break

if __name__ == '__main__':
    agent_loop('Fix the bug in calculate_total')

• This loop is naive; a real agent would use structured tool calls.
• You can extend it to handle JSON tool definitions.
• Run it with python agent.py and watch the model issue a bash command.

Step 7: Recommended setup for production

For a production developer agent, I would not rely on a single model. I would use a router with fallbacks: GPT-4o for code generation, Claude for review, and DeepSeek for cheap bulk edits.

Here is a copy-paste starter for a .env file and a routing table.

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AIza...
TOGETHER_API_KEY=tg-...

models:
  codegen: gpt-4o
  review: claude-3-5-sonnet-20240620
  repo_analysis: gemini-1.5-pro
  local: llama-3.1-405b
  cheap_edit: deepseek-coder

fallbacks:
  codegen: [claude-3-5-sonnet-20240620, deepseek-coder]
  review: [gpt-4o, gemini-1.5-pro]
  repo_analysis: [claude-3-5-sonnet-20240620]
  local: [llama-3.1-8b]

• Use a YAML config file for routing rules so you can change them without redeploying.
• Always have a fallback to a cheaper model if the primary times out.
• Log every call to track cost and latency per task.

What I would do

If you are building a developer agent today, start with GPT-4o as your default because it has the best balance of speed and reliability. Add Claude for code review tasks where reasoning matters more. Use DeepSeek for bulk refactoring where cost is a concern.

Do not over-engineer; a simple router like the one above is enough for most agents. Once you hit rate limits or need better performance, add caching and batching.

FAQ

Answers to the questions that come up most often on this topic.

• Q: Can I use open models like Llama 3.1 for a production agent? A: Yes, if you have the hardware. Llama 3.1 405B gives near-frontier quality but requires multiple GPUs. Use the 8B or 70B versions for lighter tasks.
• Q: How do I handle API rate limits? A: Implement exponential backoff and queue requests. Use a library like tenacity or backoff.
• Q: Is it worth using a specialized code model like DeepSeek Coder? A: For repetitive code edits, yes. It is cheaper and often faster, but it may not handle complex reasoning as well as GPT-4o or Claude.
• Q: How do I keep the agent from going off the rails? A: Constrain the tool calls to a whitelist, and require the model to output JSON for tool use. Validate the JSON before executing.
• Q: Should I fine-tune a model for my codebase? A: Only if you have a large dataset and specific patterns. Start with prompt engineering and RAG before fine-tuning.

Try it on code.live

You can test prompts and compare model outputs interactively using the code.live JSON Diff tool to see differences in responses. For quick experimentation with model outputs, the API Mock Data Generator can help you create test payloads.