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Gemini 3.1 pro vs Opus 4.6 vs Gpt 5. 3 Codex: The Ultimate Comparison

Compare Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.3 Codex across benchmarks, pricing, and features. Data-driven guide to choose the best AI model for coding in 2026.

Ashley Innocent

Ashley Innocent

24 February 2026

Gemini 3.1 pro vs Opus 4.6 vs Gpt 5. 3 Codex: The Ultimate Comparison

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TL;DR

February 2026 brought three cutting-edge AI models: Gemini 3.1 Pro , Claude Opus 4.6 , and GPT-5.3 Codex . No single model dominates all use cases—each excels in specific areas:

Introduction

February 2026 will be remembered as the month AI labs stopped competing on benchmarks and started competing on developer workflows. In just 15 days, three major labs released four flagship models—Claude Opus 4.6 (Feb 5), GPT-5.3 Codex (Feb 5), and Gemini 3.1 Pro (Feb 19)—each claiming to be the "most capable" model for coding and development.

For developers, this creates a practical problem: Which model should you actually use? The answer isn't simple, because unlike previous generations where one model clearly led, these three models each dominate different slices of the development workflow.

In this guide, we'll cut through the marketing claims with real benchmark data, pricing analysis, and practical use cases. We'll also show you how to test and integrate these AI model APIs using Apidog's unified workspace, so you can evaluate all three models in your actual development environment before committing to one.

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By the end, you'll know exactly which model to choose for your specific coding tasks—or whether you should use multiple models together.

The February 2026 AI Model Rush

The release timeline tells the story of an unprecedented competitive sprint:

This wasn't coincidental. Each lab positioned their model as the answer to agentic coding—AI that doesn't just suggest code but plans, executes, and debugs entire projects autonomously.

The strategic timing mattered because these models target the same high-value users: professional developers, dev tool companies building AI features, and enterprises automating software development. The question shifted from "can AI write code?" to "which AI writes code you can actually ship?"

Benchmark Performance Deep Dive

Let's examine how these models perform across industry-standard coding benchmarks:

ARC-AGI-2: Abstract Reasoning

Winner: Gemini 3.1 Pro (77.1%)

The ARC-AGI-2 benchmark tests abstract reasoning—the ability to solve novel logic patterns without prior training. Gemini 3.1 Pro's score of 77.1% represents a massive jump from Gemini 3 Pro's 31.1%, demonstrating Google's focus on reasoning improvements.

This matters for competitive programming and algorithm design, where you need to solve unfamiliar problems rather than apply known patterns.

Gemini 3.1 Pro Benchmark

SWE-Bench: Real-World Software Engineering

Winner: Claude Opus 4.6 (80.8% on Verified)

SWE-Bench tests whether models can resolve real GitHub issues in popular Python repositories. This is the closest proxy we have for real-world software engineering tasks.

Note: These use different SWE-Bench variants, so direct comparison requires caution. The "Verified" subset is smaller but higher-quality than "Pro Public."

OPus 4.6 benchmark

Terminal-Bench 2.0: Command-Line Workflows

Winner: GPT-5.3 Codex (77.3%)

Terminal-Bench evaluates models on terminal-based development tasks—debugging, system administration, git operations, and build systems.

Codex's dominance here reflects OpenAI's specific optimization for interactive terminal workflows.

Terminal-Bench 2.0 Gpt 5.3 Codex benchmark

LiveCodeBench: Competitive Coding

Winner: Gemini 3.1 Pro (2887 Elo)

LiveCodeBench uses an Elo rating system for competitive programming challenges, updated continuously to prevent training data contamination.

GPQA Diamond: Graduate-Level Science Questions

Winner: Gemini 3.1 Pro (94.3%)

While not coding-specific, GPQA Diamond tests expert-level knowledge across physics, biology, and chemistry—relevant for scientific computing applications.

GDPval-AA: Expert Task Performance (Elo Ratings)

Winner: Claude Sonnet 4.6 (1633 Elo, though we're comparing Opus 4.6)

This human-evaluated benchmark measures quality on expert tasks. Claude Opus 4.6 scores 1606 Elo, while Gemini 3.1 Pro achieves 1317 Elo—suggesting Claude produces more polished, contextually appropriate outputs.

Summary: Different Models, Different Strengths

The benchmark data reveals a clear pattern:

There's no single "best" model—your choice depends on your specific workflow.

Pricing & Cost Analysis

Cost matters when you're making thousands of API calls daily. Here's how the pricing stacks up:

Token Pricing Comparison

ModelInput TokensOutput TokensLong Context Premium
Gemini 3.1 Pro$2 per million$12 per million$4/$18 (200K-1M tokens)
Claude Opus 4.6$5 per million$25 per million$10/$37.50 (>200K tokens)
GPT-5.3 CodexNot yet announcedNot yet announcedTBD

Key Insight: Gemini 3.1 Pro is 7x cheaper than Claude Opus 4.6 on a per-request basis for standard prompts under 200K tokens.

Real-World Cost Examples

Let's calculate costs for common development tasks:

Task 1: Code Review (3,000 input tokens, 800 output tokens)

Task 2: Refactoring Large File (15,000 input tokens, 12,000 output tokens)

Task 3: Long-Context Repository Analysis (500,000 input tokens, 3,000 output tokens)

Value for Money Analysis

While Gemini 3.1 Pro offers the lowest per-token cost, cost per task depends on efficiency:

Recommendation: Start with Gemini 3.1 Pro for cost-sensitive workflows, but track completion rates to calculate true cost-per-successful-task.

Key Features & Capabilities

Beyond benchmarks and pricing, each model offers unique features that change how you work:

Gemini 3.1 Pro Features

1 Million Token Context Window (Standard)

Gemini 3.1 Pro's 1M token context is available without beta access, allowing you to:

The output limit is 65,536 tokens—sufficient for generating complete modules.

Multimodal Reasoning

Unlike text-focused coding models, Gemini 3.1 Pro handles:

This matters for design-driven development workflows.

Google Ecosystem Integration

Native integration with:

Transformer Mixture-of-Experts Architecture

The three-tier thinking system optimizes for deep reasoning—evident in the ARC-AGI-2 score improvement.

Claude Opus 4.6 Features

Agent Teams (Paradigm Shift)

Claude Opus 4.6 introduces Agent Teams—multiple Claude instances collaborating on a task with distinct roles (planner, executor, reviewer). This has no direct equivalent in OpenAI or Google's offerings.

Use cases:

Adaptive Thinking Mode

Opus 4.6 spends variable time "thinking" before responding, similar to o1-style reasoning. You see a thinking indicator while it plans the approach, then receives a more thought-through solution.

This reduces iterations on complex problems.

1 Million Token Context (Beta) + 128K Output

While Gemini offers 1M input tokens standard, Claude's 128K output capacity enables:

The 1M context is currently in beta but available to API users.

Extended Thinking on Demand

You can request "extended thinking" for tasks requiring deep planning, trading latency for solution quality.

GPT-5.3 Codex Features

Interactive Steering

Unlike traditional LLMs that complete your prompt and stop, GPT-5.3 Codex supports mid-execution steering:

This feels more like pair programming than prompt engineering.

Self-Bootstrapping Sandboxes

Codex can spin up isolated environments, test its own code, and debug failures autonomously—reducing the feedback loop from minutes to seconds.

25% Faster Inference

OpenAI optimized GPT-5.3 Codex for speed, making it noticeably snappier than GPT-5.2 while maintaining quality.

Deep Diffs

Codex generates contextual diffs that explain not just what changed but why, making code review and Git workflows more efficient.

First Self-Improving Model

GPT-5.3 Codex is OpenAI's first model where early versions helped debug its own training, manage deployment, and diagnose test results—an interesting milestone in AI development.

Testing AI Model APIs with Apidog

If you're serious about choosing the right AI model, you need to test them with your actual use cases. Apidog's unified workspace makes it easy to compare all three models side-by-side.

Apidog Testing interface

Why Test AI Model APIs?

Setting Up AI Model Endpoints in Apidog

Here's how to configure all three models in a single Apidog workspace:

Step 1: Create a New Workspace

In Apidog, create a workspace named "AI Models Comparison" to organize your test requests.

Create a New Workspace In Apidog

Step 2: Set Up Environment Variables

Navigate to Environments → Create environment variables for each API key:

GEMINI_API_KEY=your_google_api_key_here
CLAUDE_API_KEY=your_anthropic_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

This keeps credentials secure and makes it easy to switch between development and production keys.

Step 3: Add Gemini 3.1 Pro Endpoint

Create a new POST request:

URL: https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-pro:generateContent
Headers:
  x-goog-api-key: {{GEMINI_API_KEY}}
  Content-Type: application/json

Body:
{
  "contents": [{
    "parts": [{
      "text": "Write a Python function to check if a number is prime."
    }]
  }],
  "generationConfig": {
    "temperature": 0.7,
    "maxOutputTokens": 2048
  }
}

Step 4: Add Claude Opus 4.6 Endpoint

Create a new POST request:

URL: https://api.anthropic.com/v1/messages
Headers:
  x-api-key: {{CLAUDE_API_KEY}}
  anthropic-version: 2023-06-01
  Content-Type: application/json

Body:
{
  "model": "claude-opus-4-6-20260205",
  "max_tokens": 2048,
  "messages": [{
    "role": "user",
    "content": "Write a Python function to check if a number is prime."
  }]
}

Step 5: Add GPT-5.3 Codex Endpoint

Create a new POST request:

URL: https://api.openai.com/v1/chat/completions
Headers:
  Authorization: Bearer {{OPENAI_API_KEY}}
  Content-Type: application/json

Body:
{
  "model": "gpt-5.3-codex",
  "messages": [{
    "role": "user",
    "content": "Write a Python function to check if a number is prime."
  }],
  "temperature": 0.7,
  "max_tokens": 2048
}

Comparing Response Quality

With all three endpoints configured, you can:

  1. Send identical prompts to each model
  2. Compare response times in Apidog's response panel
  3. Analyze token usage from response headers
  4. Evaluate code quality side-by-side
  5. Track costs using token counts and pricing data

Pro Tip: Use Apidog's test scenarios to automate this comparison across multiple prompts, giving you statistically meaningful quality data.

Monitoring Token Usage and Costs

Add post-request scripts to calculate costs automatically:

// Example for Gemini 3.1 Pro
const inputTokens = pm.response.json().usageMetadata.promptTokenCount;
const outputTokens = pm.response.json().usageMetadata.candidatesTokenCount;
const cost = (inputTokens * 0.000002) + (outputTokens * 0.000012);

console.log(`Tokens used: ${inputTokens} input, ${outputTokens} output`);
console.log(`Estimated cost: $${cost.toFixed(4)}`);

This gives you real-time cost awareness while testing.

Use Case Recommendations

After analyzing benchmarks, features, and developer feedback, here's when to use each model:

Use Gemini 3.1 Pro For:

Algorithmic Coding & Competitive Programming

Reason: Highest ARC-AGI-2 and LiveCodeBench scores demonstrate superior reasoning for novel problems.

Large Codebase Analysis

Reason: 1M token context window (standard, not beta) + lowest cost for long-context tasks.

Multimodal Development

Reason: Native multimodal support across images, audio, and video.

Cost-Sensitive Projects

Reason: $2/$12 per million tokens is 7x cheaper than Claude Opus 4.6.

Use Claude Opus 4.6 For:

Greenfield Projects & Creative Work

Reason: Developers report Claude produces more "polished and contextually appropriate" code for creative tasks.

Complex Multi-Step Tasks

Reason: Agent Teams and adaptive thinking mode handle complex planning better.

Long-Form Code Generation

Reason: 128K output token limit enables generating complete applications in one response.

Quality Over Speed

Reason: Human evaluators consistently prefer Claude's output quality (GDPval-AA: 1606 Elo).

Use GPT-5.3 Codex For:

Terminal & Command-Line Workflows

Reason: 77.3% Terminal-Bench 2.0 score—highest by significant margin.

Code Review & Analysis

Reason: Deep diff capabilities and code review optimizations.

Interactive Debugging

Reason: Interactive steering allows mid-execution course correction.

Refactoring Existing Code

Reason: Excels at understanding existing patterns and applying consistent changes.

Multi-Model Strategies

Many professional developers use multiple models together:

Strategy 1: Model Routing by Task Type

Strategy 2: Cost Optimization

Strategy 3: Quality Consensus

Real Developer Experiences

Beyond benchmarks, how are developers actually using these models?

Case Study: Shipping 93,000 Lines in 5 Days

One developer documented using Claude Opus 4.6 to ship 93,000 lines of code in 5 days, including 44 pull requests. The workflow relied on Agent Teams—one agent writing code while another wrote tests and a third reviewed for security issues.

Key Insight: The adaptive thinking mode reduced back-and-forth iterations, allowing more features to ship in the first attempt.

Common Pain Points

Across developer forums and case studies, common themes emerge:

Gemini 3.1 Pro:

Claude Opus 4.6:

GPT-5.3 Codex:

Switching Patterns

Developers report starting with one model and switching when:

How to Get Started

Ready to test these models yourself? Here's how to get started with each:

Getting Started with Gemini 3.1 Pro

Access:

Authentication:

  1. Visit Google AI Studio
  2. Create an API key
  3. Use key in x-goog-api-key header

First API Request:

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-pro:generateContent \
  -H "x-goog-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "parts": [{"text": "Write a Python function to reverse a string."}]
    }]
  }'

Pricing: Pay-as-you-go, $2/$12 per million tokens

Getting Started with Claude Opus 4.6

Access:

Opus 4.6 in Claude Code

Authentication:

  1. Visit platform.claude.com
  2. Generate an API key
  3. Use key in x-api-key header
Claude Opus 4.6 on Anthropic API console platform

First API Request:

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: YOUR_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-6-20260205",
    "max_tokens": 1024,
    "messages": [{
      "role": "user",
      "content": "Write a Python function to reverse a string."
    }]
  }'

Pricing: $5/$25 per million tokens ($10/$37.50 for >200K context)

Getting Started with GPT-5.3 Codex

Access:

gpt 5-3 codex in codex CLI tool

Authentication:

  1. Visit platform.openai.com
  2. Generate an API key
  3. Use key in Authorization: Bearer header

First API Request (when API access available):

curl https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.3-codex",
    "messages": [{
      "role": "user",
      "content": "Write a Python function to reverse a string."
    }]
  }'

Pricing: Not yet announced (currently bundled with ChatGPT Plus for web access)

Testing All Three in Apidog

The fastest way to compare all three models:

  1. Import the AI Models collection from Apidog's template library (if available)
  2. Configure environment variables for all three API keys
  3. Run test scenarios with identical prompts across models
  4. Compare response times, token usage, and output quality
  5. Monitor costs using Apidog's cost tracking features

This gives you empirical data to make an informed choice for your specific use case.

Conclusion

The February 2026 AI model releases mark a turning point: we've moved from "which model is best?" to "which model is best for this specific task?"

The verdict:

Rather than picking one model, professional developers increasingly use multiple models together—routing tasks to the optimal model or using consensus approaches for critical code.

The fastest way to determine which model works best for your workflow is to test all three with your actual use cases. Apidog's unified workspace makes this easy—set up all three API endpoints, configure your API keys once, and send identical prompts to compare response quality, speed, and cost in real-time.

Ready to compare these AI models for your specific use case? Import your existing API collections into Apidog's workspace in 60 seconds and test Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.3 Codex side-by-side with no code required.

Try Apidog free—no credit card required.

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