GPT-5.2 vs Claude Opus 4.6 / Sonnet 4.6 vs Gemini 3.1 Pro: Which Model Should Junior Devs Choose?

Three Different AI Philosophies — Understand First, Then Choose

If you’re torn between GPT-5.2, Claude Opus 4.6/Sonnet 4.6, and Gemini 3.1 Pro, I get it. I’ve spent time comparing each one, running them across different projects. The clearest takeaway: there’s no single “best model.” The right question is “which model fits this task?”

Each company builds toward a different vision, and the differences run deeper than you might think:

• GPT-5.2 (OpenAI): Built for versatility — deeply integrated with Azure, Microsoft 365, and GitHub Copilot
• Claude Opus 4.6 / Sonnet 4.6 (Anthropic): Prioritizes safety and reasoning above all — in return, it delivers the best text quality and reasoning of the three
• Gemini 3.1 Pro (Google): Born from the Google ecosystem — natively multimodal, real-time Search, and Vertex AI for enterprise

Breaking Down Each Model’s Strengths and Weaknesses

GPT-5.2 — Versatile with a Broad Ecosystem

GPT-5.2 builds on the GPT-5 lineage with notable improvements in reasoning and response speed. It’s especially well-suited when:

• Your project deploys on Azure or integrates with Microsoft 365
• You need reliable function calling and structured JSON output
• Multi-language code generation (TypeScript, Go, Rust — GPT handles these well)
• Your team already uses GitHub Copilot and wants model consistency

The biggest downside is cost. At the highest tier, monthly bills can exceed the budget of a small startup or side project if you’re not monitoring usage carefully.

Claude Opus 4.6 / Sonnet 4.6 — Deep Reasoning, High-Quality Writing

Anthropic built Claude around a fundamentally different philosophy: prioritize safety and honesty above everything else. That might sound minor, but in practice it means Claude will straightforwardly say “I’m not sure” instead of fabricating a plausible-sounding answer — which matters a lot more in production than people realize.

Claude stands out for:

• Natural writing: Blog posts, technical docs, emails — the output rarely needs editing
• Large context window: 200K+ tokens, handles codebases or lengthy documents better than competitors
• Extended thinking: Opus 4.6 can “think silently” before responding — especially useful for complex problems
• Consistency: Output format stays more stable across multiple API calls

Between the two versions: Opus 4.6 is the flagship — slower and more expensive, but reasoning is on a different level. Sonnet 4.6 hits the sweet spot for cost/performance. I’ve been using Sonnet 4.6 in production for content generation. Latency is consistent, costs run about 60% lower than Opus — and more importantly, output quality is good enough to push straight to production without much editing.

Weaknesses: Multimodal capability falls short of Gemini, and real-time web search is more limited.

Gemini 3.1 Pro — Multimodal and the Google Ecosystem

Gemini is the near-obvious choice if your project handles multiple media types simultaneously or runs on GCP:

• Native multimodal: Text, image, audio, and video in a single API call
• Google Search grounding: Answers backed by real-time search results, reducing hallucination on recent information
• Vertex AI: Enterprise deployment on GCP with clear SLAs
• Competitive pricing: Lower tiers (Flash) are very affordable, great for prototyping

Weaknesses: Pure text quality doesn’t match Claude. And the API sometimes returns inconsistent formats with complex prompts — I’ve run into this a few times and had to add extra handling on the client side.

Which Model for Which Situation?

Here’s the table I use when advising junior devs on the team:

Practical Implementation Guide

Enough theory — let’s get into the code. I’ll show you how to call all three APIs in Python, plus a shared-interface wrapper so you can switch models later without touching your business logic.

Install Dependencies

pip install anthropic openai google-generativeai

Calling Claude Sonnet 4.6

import anthropic

client = anthropic.Anthropic(api_key="sk-ant-...")

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=2048,
    messages=[
        {"role": "user", "content": "Giải thích Docker volumes cho người mới bắt đầu"}
    ]
)

print(response.content[0].text)

Calling GPT-5.2

from openai import OpenAI

client = OpenAI(api_key="sk-...")

response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "user", "content": "Giải thích Docker volumes cho người mới bắt đầu"}
    ],
    max_tokens=2048
)

print(response.choices[0].message.content)

Calling Gemini 3.1 Pro

import google.generativeai as genai

genai.configure(api_key="AIza...")

model = genai.GenerativeModel("gemini-3.1-pro")
response = model.generate_content(
    "Giải thích Docker volumes cho người mới bắt đầu"
)

print(response.text)

Multi-Model Wrapper — One Interface for All Three

In production, I always write a wrapper so switching models doesn’t require rewriting everything. Here’s the pattern I use:

from enum import Enum
import anthropic
from openai import OpenAI
import google.generativeai as genai

class AIProvider(Enum):
    CLAUDE = "claude"
    GPT = "gpt"
    GEMINI = "gemini"

class AIClient:
    def __init__(self, provider: AIProvider, api_key: str, model: str = None):
        self.provider = provider

        if provider == AIProvider.CLAUDE:
            self.client = anthropic.Anthropic(api_key=api_key)
            self.model = model or "claude-sonnet-4-6"
        elif provider == AIProvider.GPT:
            self.client = OpenAI(api_key=api_key)
            self.model = model or "gpt-5.2"
        elif provider == AIProvider.GEMINI:
            genai.configure(api_key=api_key)
            self.client = genai.GenerativeModel(model or "gemini-3.1-pro")
            self.model = model or "gemini-3.1-pro"

    def chat(self, message: str, max_tokens: int = 2048) -> str:
        if self.provider == AIProvider.CLAUDE:
            resp = self.client.messages.create(
                model=self.model,
                max_tokens=max_tokens,
                messages=[{"role": "user", "content": message}]
            )
            return resp.content[0].text

        elif self.provider == AIProvider.GPT:
            resp = self.client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": message}],
                max_tokens=max_tokens
            )
            return resp.choices[0].message.content

        elif self.provider == AIProvider.GEMINI:
            resp = self.client.generate_content(message)
            return resp.text

# Sử dụng
claude  = AIClient(AIProvider.CLAUDE, api_key="sk-ant-...")
gpt     = AIClient(AIProvider.GPT,    api_key="sk-...")
gemini  = AIClient(AIProvider.GEMINI, api_key="AIza...")

question = "Viết function Python đọc file CSV và trả về list of dict"

for name, client in [("Claude", claude), ("GPT", gpt), ("Gemini", gemini)]:
    print(f"\n=== {name} ===")
    print(client.chat(question))

This pattern is especially useful when a model hits rate limits or goes down — just change one line from AIProvider.CLAUDE to AIProvider.GEMINI and you’re done, no business logic changes needed.

Notes on Real-World Costs

Pricing changes frequently, but here’s the general pattern I’ve observed:

• Claude Sonnet 4.6: Mid-range cost, best performance-to-price ratio for text tasks
• Claude Opus 4.6: The most expensive of the three — only use it when you genuinely need deep reasoning
• GPT-5.2: Expensive at higher tiers, but has multiple pricing levels and volume discounts
• Gemini 3.1 Pro: Competitive pricing, with a generous free tier for testing and prototyping

Practical advice: test all three with your actual task before committing. I once spent nearly two weeks prompt-engineering with GPT-5.2. The result? Claude Sonnet 4.6 did the same job better and cheaper. Hard-won lesson: benchmarks on the internet and benchmarks in production are two very different things.

Conclusion: A Model Selection Formula for Junior Devs

After nearly a year working with all three, I’ve arrived at a pretty simple formula:

1. Start with Claude Sonnet 4.6 — best balance overall, stable API, easy to work with
2. Upgrade to Opus 4.6 or GPT-5.2 when you need complex reasoning or advanced code generation
3. Add Gemini 3.1 Pro when your project involves multimodal content or needs live Google Search data
4. Always write a wrapper — when one model has issues, fall back to another without touching business logic

For junior devs just starting out: don’t try to master all three at once. Pick one model, learn its API deeply, then expand gradually. The wrapper above will make switching easy once you’re ready.