Best AI Coding Assistants by Language: Python, JavaScript, Rust, Go, and More

If you have spent any time with AI coding assistants, you have probably noticed something: they are not equally good at everything. A tool that writes flawless Python might stumble on Rust lifetimes. An assistant that nails React components might generate awkward Go code. The model architecture, training data, and editor integration all influence how well an AI assistant performs in a specific language ecosystem.

We spent three weeks testing the major AI coding assistants — Claude Code, Cursor, GitHub Copilot, Windsurf, and JetBrains AI — across seven programming languages. For each language, we evaluated code generation quality, framework awareness, idiomatic style, error handling, and how well the tool understands language-specific patterns like Rust ownership or Go concurrency.

Here is our language-by-language breakdown of which AI coding tool performs best — and why.

Python — Best: Claude Code (9.5/10)

Python is the lingua franca of AI-assisted development, and every tool performs reasonably well here. But Claude Code stands apart with its deep understanding of Python idioms, framework conventions, and the broader ecosystem.

Where Claude Code excels is in framework-aware generation. Ask it to build a FastAPI endpoint with Pydantic validation, SQLAlchemy async queries, and proper error handling, and you get production-quality code on the first attempt:

from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from pydantic import BaseModel, Field, validator
from typing import Optional
from datetime import datetime

router = APIRouter(prefix="/api/v1/users", tags=["users"])

class UserCreate(BaseModel):
    email: str = Field(..., max_length=255)
    display_name: str = Field(..., min_length=2, max_length=100)
    role: Optional[str] = "member"

    @validator("email")
    def validate_email(cls, v):
        if "@" not in v or "." not in v.split("@")[-1]:
            raise ValueError("Invalid email format")
        return v.lower().strip()

class UserResponse(BaseModel):
    id: int
    email: str
    display_name: str
    role: str
    created_at: datetime

    class Config:
        from_attributes = True

@router.post("/", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def create_user(user: UserCreate, db: AsyncSession = Depends(get_db)):
    existing = await db.execute(
        select(User).where(User.email == user.email)
    )
    if existing.scalar_one_or_none():
        raise HTTPException(
            status_code=status.HTTP_409_CONFLICT,
            detail="A user with this email already exists"
        )
    db_user = User(**user.dict())
    db.add(db_user)
    await db.commit()
    await db.refresh(db_user)
    return db_user

Notice the details: proper use of from_attributes (not the deprecated orm_mode), async SQLAlchemy patterns, field validation with custom validators, and appropriate HTTP status codes. Claude Code consistently generates code that follows current best practices rather than outdated patterns.

It also handles Django, Flask, pandas, and scientific Python (NumPy, scipy) with strong idiomatic awareness. When you ask for a Django view, you get class-based views with proper mixins, not function-based views from 2015.

Runner-up: GitHub Copilot (9.0/10) — Copilot benefits from being trained on an enormous corpus of Python code from GitHub. Its completions are fast and usually correct, especially for common patterns. It falls slightly behind Claude Code on framework-specific nuances and complex architectural decisions, but for day-to-day Python development, it is excellent.

JavaScript/TypeScript — Best: Cursor (9.5/10)

Cursor dominates the JavaScript and TypeScript space, and the reason is its deep editor integration with the TypeScript language server. Cursor does not just generate code — it generates code that is aware of your projects type definitions, component hierarchy, and import structure.

Where Cursor really shines is React and Next.js development. Ask it to build a data fetching component with proper loading states, error boundaries, and TypeScript generics, and it produces code that actually compiles without manual type fixes:

import { useState, useEffect, useCallback } from "react";

interface UseFetchResult<T> {
  data: T | null;
  error: Error | null;
  loading: boolean;
  refetch: () => void;
}

function useFetch<T>(url: string, options?: RequestInit): UseFetchResult<T> {
  const [data, setData] = useState<T | null>(null);
  const [error, setError] = useState<Error | null>(null);
  const [loading, setLoading] = useState<boolean>(true);

  const fetchData = useCallback(async () => {
    setLoading(true);
    setError(null);
    try {
      const response = await fetch(url, options);
      if (!response.ok) {
        throw new Error(`HTTP ${response.status}: ${response.statusText}`);
      }
      const result: T = await response.json();
      setData(result);
    } catch (err) {
      setError(err instanceof Error ? err : new Error(String(err)));
    } finally {
      setLoading(false);
    }
  }, [url]);

  useEffect(() => {
    fetchData();
  }, [fetchData]);

  return { data, error, loading, refetch: fetchData };
}

// Usage with full type safety
interface User {
  id: number;
  name: string;
  email: string;
}

function UserProfile({ userId }: { userId: number }) {
  const { data: user, loading, error } = useFetch<User>(
    `/api/users/${userId}`
  );

  if (loading) return <div className="animate-pulse">Loading...</div>;
  if (error) return <div className="text-red-500">Error: {error.message}</div>;
  if (!user) return null;

  return (
    <div className="p-4">
      <h2 className="text-xl font-bold">{user.name}</h2>
      <p className="text-gray-600">{user.email}</p>
    </div>
  );
}

Cursor handles TypeScript generics, utility types, and discriminated unions better than any competitor. It understands the difference between Partial<T> and Pick<T, K> and uses them appropriately. Its Tab completion in .tsx files is remarkably accurate at predicting the next prop or JSX element you need.

Runner-up: Claude Code (9.2/10) — Claude Code handles complex TypeScript generics and type inference well, often generating types that are more precise than what Cursor produces. Where it falls slightly behind is the real-time editor integration — Cursors awareness of your open files and project structure gives it an edge for in-context completions. For standalone TypeScript code generation, Claude Code is arguably better. For the full editing experience, Cursor wins.

Rust — Best: Claude Code (8.8/10)

Rust is where the gap between AI coding tools becomes most apparent. The ownership system, borrow checker, and lifetime annotations create a complexity cliff that most AI tools fall off of. Claude Code handles it better than any alternative, though even it is not perfect.

The key differentiator is Claude Codes understanding of ownership semantics. When you ask it to write a function that processes data, it makes intelligent decisions about when to take ownership versus borrowing, when to use &str versus String, and how to structure code to satisfy the borrow checker:

use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;

#[derive(Debug, Clone)]
pub struct CacheEntry<V: Clone> {
    value: V,
    expires_at: std::time::Instant,
}

pub struct AsyncCache<V: Clone + Send + Sync + static> {
    store: Arc<RwLock<HashMap<String, CacheEntry<V>>>>,
    default_ttl: std::time::Duration,
}

impl<V: Clone + Send + Sync + static> AsyncCache<V> {
    pub fn new(default_ttl: std::time::Duration) -> Self {
        Self {
            store: Arc::new(RwLock::new(HashMap::new())),
            default_ttl,
        }
    }

    pub async fn get(&self, key: &str) -> Option<V> {
        let store = self.store.read().await;
        store.get(key).and_then(|entry| {
            if entry.expires_at > std::time::Instant::now() {
                Some(entry.value.clone())
            } else {
                None
            }
        })
    }

    pub async fn set(&self, key: impl Into<String>, value: V) {
        let mut store = self.store.write().await;
        store.insert(
            key.into(),
            CacheEntry {
                value,
                expires_at: std::time::Instant::now() + self.default_ttl,
            },
        );
    }

    pub async fn evict_expired(&self) -> usize {
        let mut store = self.store.write().await;
        let now = std::time::Instant::now();
        let before = store.len();
        store.retain(|_, entry| entry.expires_at > now);
        before - store.len()
    }
}

This code compiles correctly on the first try — which is not something you can take for granted with AI-generated Rust. Claude Code correctly uses Arc<RwLock> for shared mutable state, applies appropriate trait bounds (Clone + Send + Sync), uses impl Into<String> for ergonomic key input, and structures the lock acquisitions to minimize hold times.

Where Claude Code still struggles: deeply nested lifetime annotations, complex trait implementations with associated types, and unsafe code blocks. But for the vast majority of Rust development, it produces code that is idiomatic and correct.

Runner-up: GitHub Copilot (7.5/10) — Copilot generates decent Rust completions for common patterns but frequently produces code that fails the borrow checker. It tends to clone values unnecessarily and struggles with lifetime annotations in function signatures. You will spend more time fixing Copilots Rust output than you would with Claude Code.

Go — Best: GitHub Copilot (8.5/10)

Gos simplicity is an advantage for AI tools — there are fewer ways to do things, which means fewer opportunities to generate non-idiomatic code. Copilot edges out the competition here with consistently clean, idiomatic Go that follows community conventions.

Copilots Go output reads like it was written by someone who has actually read Effective Go. Error handling follows the if err != nil pattern consistently, struct methods use proper receiver conventions, and concurrency patterns use channels and goroutines correctly:

package worker

import (
	"context"
	"fmt"
	"log/slog"
	"sync"
	"time"
)

type Job struct {
	ID      string
	Payload []byte
}

type Result struct {
	JobID string
	Data  []byte
	Err   error
}

type Pool struct {
	workers    int
	jobQueue   chan Job
	resultChan chan Result
	wg         sync.WaitGroup
	logger     *slog.Logger
}

func NewPool(workers, queueSize int, logger *slog.Logger) *Pool {
	return &Pool{
		workers:    workers,
		jobQueue:   make(chan Job, queueSize),
		resultChan: make(chan Result, queueSize),
		logger:     logger,
	}
}

func (p *Pool) Start(ctx context.Context) {
	for i := 0; i < p.workers; i++ {
		p.wg.Add(1)
		go p.worker(ctx, i)
	}
}

func (p *Pool) worker(ctx context.Context, id int) {
	defer p.wg.Done()
	p.logger.Info("worker started", "worker_id", id)

	for {
		select {
		case <-ctx.Done():
			p.logger.Info("worker shutting down", "worker_id", id)
			return
		case job, ok := <-p.jobQueue:
			if !ok {
				return
			}
			start := time.Now()
			data, err := processJob(job)
			duration := time.Since(start)

			if err != nil {
				p.logger.Error("job failed",
					"job_id", job.ID,
					"worker_id", id,
					"duration", duration,
					"error", err,
				)
			} else {
				p.logger.Info("job completed",
					"job_id", job.ID,
					"worker_id", id,
					"duration", duration,
				)
			}

			p.resultChan <- Result{
				JobID: job.ID,
				Data:  data,
				Err:   err,
			}
		}
	}
}

func (p *Pool) Submit(job Job) {
	p.jobQueue <- job
}

func (p *Pool) Results() <-chan Result {
	return p.resultChan
}

func (p *Pool) Shutdown() {
	close(p.jobQueue)
	p.wg.Wait()
	close(p.resultChan)
}

func processJob(job Job) ([]byte, error) {
	// Process the job payload
	if len(job.Payload) == 0 {
		return nil, fmt.Errorf("empty payload for job %s", job.ID)
	}
	// Simulated processing
	return job.Payload, nil
}

This is clean, production-ready Go. Proper use of context.Context for cancellation, log/slog (the modern structured logging package), sync.WaitGroup for goroutine lifecycle management, and buffered channels for backpressure. Copilot consistently generates Go code that passes go vet and golangci-lint without warnings.

Runner-up: Claude Code (8.3/10) — Claude Code produces excellent Go code, especially for complex concurrency patterns and interface design. It edges ahead of Copilot when designing APIs with multiple packages and when the task requires understanding broader architectural patterns. For line-by-line Go coding, Copilot is slightly more ergonomic. For larger-scale Go projects, Claude Codes architectural awareness gives it an edge.

Java/Kotlin — Best: JetBrains AI (8.5/10)

JetBrains AI has a distinct advantage for Java and Kotlin development: it lives inside IntelliJ IDEA, the IDE that most Java and Kotlin developers already use. This deep integration means it understands your project structure, dependency injection configuration, and framework annotations in ways that external tools cannot match.

For Spring Boot development — which covers the majority of enterprise Java — JetBrains AI generates code that is aware of your application context, bean definitions, and configuration properties:

@RestController
@RequestMapping("/api/v1/orders")
@RequiredArgsConstructor
@Validated
public class OrderController {

    private final OrderService orderService;
    private final OrderMapper orderMapper;

    @PostMapping
    @ResponseStatus(HttpStatus.CREATED)
    public OrderResponse createOrder(
            @Valid @RequestBody CreateOrderRequest request,
            @AuthenticationPrincipal UserDetails currentUser) {

        Order order = orderService.createOrder(
            orderMapper.toCommand(request),
            currentUser.getUsername()
        );
        return orderMapper.toResponse(order);
    }

    @GetMapping("/{orderId}")
    public OrderResponse getOrder(
            @PathVariable @Positive Long orderId,
            @AuthenticationPrincipal UserDetails currentUser) {

        Order order = orderService.getOrderForUser(orderId, currentUser.getUsername());
        return orderMapper.toResponse(order);
    }

    @GetMapping
    public Page<OrderResponse> listOrders(
            @RequestParam(defaultValue = "0") @Min(0) int page,
            @RequestParam(defaultValue = "20") @Max(100) int size,
            @RequestParam(defaultValue = "createdAt,desc") String sort,
            @AuthenticationPrincipal UserDetails currentUser) {

        Pageable pageable = PageRequest.of(page, size, Sort.by(
            Sort.Direction.fromString(sort.split(",")[1]),
            sort.split(",")[0]
        ));

        return orderService
            .getOrdersForUser(currentUser.getUsername(), pageable)
            .map(orderMapper::toResponse);
    }
}

JetBrains AI knows to use @RequiredArgsConstructor (Lombok) for constructor injection, applies proper validation annotations, handles pagination with Spring Datas Pageable, and includes security context via @AuthenticationPrincipal. Its Kotlin support is equally strong, with proper coroutine integration and idiomatic Kotlin patterns.

Runner-up: GitHub Copilot (8.2/10) — Copilot handles Java well, especially for common Spring Boot patterns. It occasionally generates slightly outdated patterns (like field injection instead of constructor injection), but overall quality is high. For Kotlin specifically, Copilot sometimes generates Java-ish Kotlin instead of fully idiomatic code.

C/C++ — Best: Claude Code (8.0/10)

Systems programming is where most AI tools reveal their limitations. C and C++ require understanding of memory management, pointer arithmetic, platform-specific behavior, and the subtle distinctions between undefined behavior and implementation-defined behavior. Claude Code handles this better than competitors, though all tools have room for improvement here.

Claude Codes strength in C/C++ is its understanding of modern C++ idioms and its ability to generate memory-safe code using RAII, smart pointers, and standard library containers:

#include <memory>
#include <vector>
#include <string>
#include <mutex>
#include <optional>
#include <functional>
#include <stdexcept>

template <typename T>
class ThreadSafeObjectPool {
public:
    using Factory = std::function<std::unique_ptr<T>()>;

    explicit ThreadSafeObjectPool(Factory factory, size_t initial_size = 8)
        : factory_(std::move(factory)) {
        for (size_t i = 0; i < initial_size; ++i) {
            pool_.push_back(factory_());
        }
    }

    // Non-copyable, movable
    ThreadSafeObjectPool(const ThreadSafeObjectPool&) = delete;
    ThreadSafeObjectPool& operator=(const ThreadSafeObjectPool&) = delete;
    ThreadSafeObjectPool(ThreadSafeObjectPool&&) = default;
    ThreadSafeObjectPool& operator=(ThreadSafeObjectPool&&) = default;

    class Lease {
    public:
        Lease(ThreadSafeObjectPool& pool, std::unique_ptr<T> obj)
            : pool_(&pool), obj_(std::move(obj)) {}

        ~Lease() {
            if (obj_) {
                pool_->return_object(std::move(obj_));
            }
        }

        // Move-only
        Lease(Lease&&) = default;
        Lease& operator=(Lease&&) = default;
        Lease(const Lease&) = delete;
        Lease& operator=(const Lease&) = delete;

        T& operator*() { return *obj_; }
        T* operator->() { return obj_.get(); }

    private:
        ThreadSafeObjectPool* pool_;
        std::unique_ptr<T> obj_;
    };

    Lease acquire() {
        std::lock_guard<std::mutex> lock(mutex_);
        if (pool_.empty()) {
            return Lease(*this, factory_());
        }
        auto obj = std::move(pool_.back());
        pool_.pop_back();
        return Lease(*this, std::move(obj));
    }

    size_t available() const {
        std::lock_guard<std::mutex> lock(mutex_);
        return pool_.size();
    }

private:
    void return_object(std::unique_ptr<T> obj) {
        std::lock_guard<std::mutex> lock(mutex_);
        pool_.push_back(std::move(obj));
    }

    Factory factory_;
    std::vector<std::unique_ptr<T>> pool_;
    mutable std::mutex mutex_;
};

This is modern C++ done right: RAII-based resource management with the Lease pattern (objects automatically return to the pool on destruction), proper move semantics, deleted copy constructors, and thread safety via std::mutex. Claude Code understands that in C++, resource lifetime management is the core challenge and designs accordingly.

For C specifically, Claude Code generates code with proper bounds checking, careful memory allocation and deallocation, and awareness of common pitfalls like buffer overflows and use-after-free. It is not perfect — no AI tool is for systems programming — but it provides the best starting point.

Other tools: Copilot scores around 7.0/10 for C/C++, generating correct but sometimes non-idiomatic code. Cursor scores similarly. Most AI tools fall back on C-style patterns even when modern C++ alternatives are more appropriate. JetBrains AI with CLion integration is decent at 7.5/10, especially for CMake-based projects.

SQL — Best: GitHub Copilot (8.5/10)

SQL is a domain where AI tools provide enormous value because query writing is highly pattern-based but the syntax variations across databases are a constant annoyance. Copilot leads here with reliable query generation across PostgreSQL, MySQL, SQLite, and SQL Server.

Copilot excels at translating natural language descriptions into correct, optimized queries. Ask it for “find users who signed up in the last 30 days, ordered more than 3 times, and have not left a review” and you get:

-- Find active new users who havent reviewed yet
WITH recent_users AS (
    SELECT
        u.id,
        u.email,
        u.display_name,
        u.created_at,
        COUNT(o.id) AS order_count,
        SUM(o.total_amount) AS total_spent
    FROM users u
    INNER JOIN orders o ON o.user_id = u.id
    WHERE u.created_at >= CURRENT_DATE - INTERVAL 30 days
      AND o.status = completed
    GROUP BY u.id, u.email, u.display_name, u.created_at
    HAVING COUNT(o.id) > 3
)
SELECT
    ru.id,
    ru.email,
    ru.display_name,
    ru.order_count,
    ru.total_spent,
    ru.created_at AS signed_up
FROM recent_users ru
LEFT JOIN reviews r ON r.user_id = ru.id
WHERE r.id IS NULL
ORDER BY ru.total_spent DESC;

The query uses CTEs for readability, proper JOIN types, correct aggregate functions with GROUP BY, and the LEFT JOIN ... WHERE NULL anti-join pattern for finding users without reviews. Copilot also generates proper indexes when asked and understands the performance implications of different query structures.

Runner-up: Claude Code (8.3/10) — Claude Code generates excellent SQL and often produces more optimized queries for complex analytical workloads. Its advantage over Copilot in SQL is understanding context: it knows when to suggest window functions over self-joins, when a materialized CTE would help, and how to structure queries for specific database engines. For PostgreSQL-specific features (JSONB queries, lateral joins, recursive CTEs), Claude Code is arguably the best option.

Language-by-Language Comparison Table

How to Choose Based on Your Primary Language

The best AI coding assistant depends heavily on what you actually build. Here is a practical decision framework:

If You Primarily Write Python

Go with Claude Code. Its understanding of Python frameworks, idioms, and the scientific computing ecosystem is the best available. If you prefer an IDE experience over the terminal, Copilot inside VS Code is the runner-up choice.

If You Primarily Write JavaScript/TypeScript

Use Cursor. The deep TypeScript language server integration and React/Next.js awareness makes it the clear winner for frontend and full-stack JavaScript development. Learn Cursors tips and tricks to get the most out of it.

If You Write Rust or C/C++

Claude Code is your best option for systems programming. No other tool comes close to matching its understanding of ownership, lifetimes, and memory management patterns. Consider using it alongside your IDEs built-in analysis tools (rust-analyzer, clangd) for the best experience.

If You Write Go

Copilot inside VS Code or GoLand is the smoothest experience. Gos simplicity means most AI tools perform reasonably well, but Copilots completions are the most consistently idiomatic.

If You Write Java or Kotlin

JetBrains AI inside IntelliJ IDEA is the natural choice, especially for Spring Boot and enterprise Java. If you do not use IntelliJ, Copilot is the best alternative.

If You Work Across Multiple Languages

Claude Code has the highest average score across all languages and does not require switching tools between languages. It is the best generalist. Combine it with a language-specific tool (Cursor for JS/TS, JetBrains AI for Java) if your project has a dominant secondary language. See our complete guide to the best AI coding tools for a broader comparison.

What About Other Languages?

Our testing focused on seven languages, but here are quick notes on others:

• Ruby: Copilot is strongest, with good Rails awareness. Claude Code is close behind. Cursor and Windsurf are decent but less Ruby-fluent.
• PHP: Copilot and Claude Code both handle PHP well, including modern PHP 8.3 features. Laravel and Symfony patterns are well-supported.
• Swift: Copilot in Xcode (via the GitHub Copilot extension) is the primary option. Claude Code handles Swift syntax well but lacks the IDE integration for SwiftUI previews.
• Dart/Flutter: This is a weaker area for all tools. Copilot is the best of the bunch, but you will need to review widget trees carefully.
• Elixir/Haskell/Clojure: Claude Code handles functional languages better than any competitor, including pattern matching, monadic patterns, and immutable data structures.

Related Reading

Last updated: February 2026. All tools were tested by the RunAICode team using real-world codebases across multiple languages. Scores reflect code generation quality, framework awareness, and language-specific pattern understanding. No affiliate relationships with any vendor.