by Gary Worthington, More Than Monkeys

AI coding assistants in 2025 have crossed the line from novelty to necessity. Whether you are shipping production code, wrangling a legacy monolith, or rapidly prototyping an MVP, the right AI tool can speed you up, improve code quality, and reduce cognitive load.

But the right tool isn’t always obvious with the myriad choises available to us. GPT 5 promises all-round excellence, Claude 4 claims unmatched accuracy, Gemini 2.5 Pro reads your entire repo in one go and Copilot feels like autocomplete on overdrive. And the open-source crowd, led by Mistral Devstral and Sourcegraph Cody, is catching up fast.

This article compares the best AI coding assistants in 2025 with a developer’s eye. You’ll see what they’re good at, where they fall short, and real examples of how they can (or can’t) help in everyday coding scenarios.

Why AI Coding Assistants Matter

Time is the currency of development. AI coding assistants are best seen as force multipliers: they do not replace skill, they amplify it.

Example:
Last month, while building a Flask-based microservice, I used GPT 4.1 to generate 80% of the boilerplate, including:

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/predict", methods=["POST"])
def predict():
    data = request.get_json()
    # TODO: call ML model
    return jsonify({"prediction": "cat"})

if __name__ == "__main__":
    app.run(debug=True)

What would have taken 5 minutes by hand was ready in seconds, freeing me to focus on integrating the actual ML model. The same model also generated unit tests in pytest with meaningful coverage.

GPT 5: The New Reigning Champ for AI Assisted Coding

Released: August 2025
Variants: Standard, Mini, Nano, Chat

GPT 5 builds on GPT 4.1 with improved reasoning, fewer hallucinations, and more fluid multi-step dialogue for iterative coding.

What it’s great at:

• Reading and reasoning about large codebases.
• Iterative “conversation coding” — you can start with a vague prompt like “Write me a FastAPI endpoint that validates JWT tokens” and refine it over several turns until it exactly fits your requirements.
• Handling non-trivial refactors across multiple files.

Example:
I gave GPT 5 this prompt:

“Refactor this Django view to use class-based views and add caching.”

It returned:

from django.views import View
from django.http import JsonResponse
from django.utils.decorators import method_decorator
from django.views.decorators.cache import cache_page

@method_decorator(cache_page(60 * 5), name='dispatch')
class MyView(View):
    def get(self, request):
        data = {"message": "Hello, world"}
        return JsonResponse(data)

It also explained why the decorator was applied at the class level, and suggested integrating Redis if cache persistence beyond process restart was needed.

When to choose it:
If you want a single model for almost everything ie. code generation, architecture advice, refactoring, then GPT 5 is the one to beat.

Claude 4 (Opus and Sonnet): High Accuracy and Long Context

Released: May 2025
Variants: Opus 4 (full power), Sonnet 4 (faster and cheaper)

Claude has a reputation for hallucinating less than other models, particularly in code. It is excellent at reading large codebases, producing consistent styles, and generating long-form technical documentation.

Example:
Given 1,200 lines of untyped Python, Claude Opus 4 added full type hints, docstrings, and suggested better function names, all while keeping the existing logic intact.

def process_orders(orders: list[Order]) -> dict[str, int]:
    """
    Process a list of orders and return a summary of items sold.
    """
    summary: dict[str, int] = {}
    for order in orders:
        for item in order.items:
            summary[item.name] = summary.get(item.name, 0) + item.quantity
    return summary

It’s also good at cross-file analysis making it a strong choice for code review automation.

When to choose it:
If accuracy matters more than raw speed, especially on complex projects with lots of interconnected files.

Google Gemini 2.5 Pro: The Context Window King

Gemini 2.5 Pro is the “read your whole repo” model. With a million-token context, it can take in huge projects and reason about them coherently.

Example:
A React Native app with 150+ components had inconsistent prop naming. Gemini scanned the entire codebase and produced a rename plan, including the VS Code search-and-replace regex patterns to apply.

Why it’s unique:
It can combine code with non-code context. Give it Figma mockups and it will output working Flutter or Jetpack Compose screens that match.

When to choose it:
If you’re dealing with large codebases or design-to-code workflows.

GPT 4.1: The Cost-Efficient Middleweight

If GPT 5 is the Ferrari, GPT 4.1 is the well-tuned hatchback (UK readers, think your Vauxhall Nova from the late 90s). It’s cheap, fast, and gets most jobs done.

Example:
A client needed a quick AWS Lambda function to read from S3, parse a CSV, and push to DynamoDB. GPT 4.1 produced it in one go:

import boto3
import csv

s3 = boto3.client('s3')
dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
    bucket = event['bucket']
    key = event['key']
    table_name = event['table']

    obj = s3.get_object(Bucket=bucket, Key=key)
    rows = csv.DictReader(obj['Body'].read().decode('utf-8').splitlines())
    table = dynamodb.Table(table_name)

    with table.batch_writer() as batch:
        for row in rows:
            batch.put_item(Item=row)

    return {"status": "done"}

No fuss, just working code.

When to choose it:
If you’re budget-conscious or working on smaller projects where speed trumps advanced reasoning.

Open Source Options: Mistral Devstral and Sourcegraph Cody

Not every team wants closed, API-only models. Open source is catching up fast.

Mistral Devstral: Apache-licensed, tuned for code, and competitive with smaller proprietary models.

Sourcegraph Cody: Deep repo awareness, integrates into IDEs, and can generate context-aware suggestions.

Example:
With Cody connected to a Go codebase, I asked:

“Which functions are never called, and can they be deleted?”

It returned a list of 14 unused functions with file paths, then offered a patch file removing them.

When to choose it:
If privacy, cost control, or on-prem deployment is critical.

Specialist Tools: Copilot, CodeWhisperer, Cursor, Devmate

GitHub Copilot: Best day-to-day autocomplete. Predicts the next 3–10 lines based on context.

AWS CodeWhisperer: AWS-focused, with API call correctness built-in.

Cursor: An IDE built for AI-first coding. Blends autocomplete with “do this for me” commands.

Meta’s Devmate: Not public, but an indicator of where multi-step AI coding agents are heading.

Example:
In VS Code, Copilot can take:

def calculate_discount(price, discount):

…and complete the whole function body with proper rounding and type hints before you even hit Enter.

Which AI Coding Assistant Should You Use?

If you just want the shortcut guide without reading every section:

• All-round best coding and reasoning: GPT 5 or Claude Opus 4
• Analysing large codebases: Gemini 2.5 Pro or Claude Opus 4
• Fast and cost-efficient development: GPT 4.1
• Open source and self-hosted projects: Mistral Devstral or Sourcegraph Cody
• AWS-specific development: AWS CodeWhisperer
• VS Code power users: GitHub Copilot or Cursor

Final Word

AI coding assistants in 2025 are real productivity tools. My advice:

1. Start with one that fits your workflow.
2. Test it on real tasks, not “toy” examples.
3. Keep reviewing its output. AI can save time, but it can also ship bugs at scale.

Used well, these tools free you to focus on architecture, performance, and solving the right problems - the human parts of coding.

Gary Worthington is a software engineer, delivery consultant, and agile coach who helps teams move fast, learn faster, and scale when it matters. He writes about modern engineering, product thinking, and helping teams ship things that matter.

Through his consultancy, More Than Monkeys, Gary helps startups and scaleups improve how they build software — from tech strategy and agile delivery to product validation and team development.

Visit morethanmonkeys.co.uk to learn how we can help you build better, faster.

Follow Gary on LinkedIn for practical insights into engineering leadership, agile delivery, and team performance