Best GPT for Coding: Comparing AI Code Assistants

Everyone wants to know: GPT-4, Claude, or Gemini for coding?

Wrong question.

The right question is: What kind of coding task? Because these models have very different strengths, and "best" depends entirely on what you're doing.

The Real Comparison

After building an AI-powered code intelligence platform and testing extensively, here's what I've found:

Code Generation (Writing New Code)

GPT-4 / GPT-4o:

• Excellent at common patterns
• Good framework knowledge (React, Django, etc.)
• Sometimes adds unnecessary complexity
• Tends to over-engineer simple solutions

Claude (Sonnet/Opus):

• Better at following existing code style
• More conservative, less likely to over-engineer
• Excellent at TypeScript specifically
• Better at explaining its reasoning

Gemini 1.5:

• Good at very long context (1M tokens)
• Inconsistent quality
• Better for research than implementation

Winner for generation: GPT-4 for boilerplate, Claude for fitting existing patterns

Code Understanding (Explaining Existing Code)

This is where the differences get interesting.

// Given a complex function like this:
export async function syncFeaturesFromDescription(workspaceId: number) {
  const workspace = await getWorkspace(workspaceId);
  const description = JSON.parse(workspace.description || '{"features":[]}');
  
  await sql`BEGIN`;
  try {
    await sql`DELETE FROM feature_catalog WHERE workspace_id = ${workspaceId}`;
    
    for (const [index, feature] of description.features.entries()) {
      await sql`
        INSERT INTO feature_catalog (workspace_id, name, how_it_works, routes, files, display_order)
        VALUES (${workspaceId}, ${feature.name}, ${feature.how_it_works}, 
                ${feature.routes}, ${feature.files}, ${index})
      `;
    }
    await sql`COMMIT`;
  } catch (error) {
    await sql`ROLLBACK`;
    throw error;
  }
}

GPT-4: Explains what each line does (surface level)

Claude: Explains the why — "This uses a transaction to atomically replace all features, preventing partial updates that could leave the database in an inconsistent state"

Gemini: Variable quality, sometimes misses transaction semantics

Winner for understanding: Claude, especially for architectural reasoning

Debugging (Finding Issues)

// Bug: This function sometimes returns undefined
async function getUserProfile(userId: string) {
  const user = await db.user.findFirst({ where: { id: userId }});
  return {
    name: user.name,
    email: user.email
  };
}

GPT-4: "Add null check for user"

Claude: "The function assumes findFirst always returns a user, but it returns null when no match is found. Three options: (1) throw an error for invalid userId, (2) return null and update callers, (3) use findFirstOrThrow. Option 1 is usually best because it fails fast and makes the error obvious."

Gemini: Usually catches it, sometimes misses edge cases

Winner for debugging: Claude for nuanced recommendations

Long Context Tasks

Processing large codebases or many files:

GPT-4: 128K context, but quality degrades with length Claude: 200K context, maintains quality well Gemini 1.5: 1M+ context, good for search but quality varies

For our MCP tools, we use Claude because understanding code relationships requires maintaining context across many files:

// Our 60+ MCP tools work with Claude (MCPToolsReference.tsx)
const tools = {
  // Discovery - needs to understand multiple files
  search_symbols: 'ILIKE search in code_definitions',
  get_file_structure: 'Lists files with symbols',
  
  // Call graphs - needs to trace across files
  get_symbol_call_graph: 'Recursive CTE, 10-level max',
  find_callers: 'Reverse lookup in code_call_paths',
  
  // Architecture - needs full codebase context
  get_code_dependencies: 'Inheritance hierarchies',
  get_dependency_graph: 'Package dependencies'
};

Winner for long context: Claude for quality, Gemini for raw length

The Real-World Test

We tested all three on actual development tasks from our platform:

Task 1: "Explain how feature discovery works"

Setup: Pointed to our discoveredFeatureService.ts

GPT-4: Described the function signatures accurately but missed the transaction pattern significance.

Claude: Explained the full flow including why features are stored in JSON in code_snapshots.description, parsed and synced to feature_catalog, and why atomic transactions matter for consistency.

Gemini: Got the basics but added some incorrect assumptions about the schema.

Task 2: "Find why call graph depth is limited to 10"

GPT-4: Found the WHERE ct.depth < 10 clause but said "arbitrary limit"

Claude: Found the clause AND explained: "Recursive CTEs can be expensive. 10 levels covers virtually all real call chains while preventing runaway queries. The 1000 row LIMIT is a secondary safeguard."

Task 3: "Add a new metric to health insights"

GPT-4: Generated working code but didn't follow our existing pattern

Claude: Generated code matching our existing insights.push({ type, severity, message }) pattern exactly

The Honest Assessment

| Task | Best Choice | Why | |------|-------------|-----| | Quick code snippets | GPT-4 | Fast, common patterns | | Understanding architecture | Claude | Better reasoning | | Fitting existing patterns | Claude | Style matching | | Very long documents | Gemini 1.5 | Context length | | API/library usage | GPT-4 | Training data breadth | | TypeScript specifically | Claude | Consistently better types | | Explaining decisions | Claude | Shows reasoning |

What Actually Matters

Here's the thing: the model matters less than the context you give it.

A mediocre prompt with GPT-4 performs worse than a great prompt with Claude Haiku.

What we found building Glue:

Context > Model Giving the AI actual code structure (call graphs, dependencies, file relationships) matters more than which model you use.

Tools > Chat AI with tools (like our 60+ MCP tools) dramatically outperforms raw chat because it can actually query the codebase instead of guessing.

// This is why we built MCP tool integration
// Instead of: "Look at this code and tell me about auth"
// We use: AI calls search_symbols('auth') → get_symbol_call_graph('AuthService')
// Real data, no hallucination

My Recommendation

For most developers:

• Use Claude Sonnet for daily coding tasks
• Use GPT-4 for quick snippets and library questions
• Don't obsess over the choice — prompt engineering matters more

For building AI tools:

• Use Claude for reasoning-heavy tasks
• Build in structured tools (MCP or function calling)
• Never rely on the AI's "memory" of your codebase — always provide fresh context

For enterprises:

• Look at data residency and privacy policies first
• Claude and GPT-4 both have enterprise tiers
• Consider self-hosted options for sensitive code

The best GPT for coding is the one that has access to your actual codebase context. That's why we built what we built — not another chat interface, but AI with deep code understanding.