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GPT-5.3 Codex Pricing: $2/M Input — OpenAI's Agentic Coding Model (Feb 2026)

February 2026 — OpenAI GPT-5.3 Codex costs $2 input / $10 output per 1M tokens. Purpose-built for agentic coding, 25% faster than GPT-5.2, 200K context, 32K max output. Full pricing vs Claude, Gemini.

DevTk.AI 2026-02-26 Updated 2026-02-26

OpenAI released GPT-5.3 Codex in February 2026 — their first model explicitly optimized for agentic coding workflows. At $2/$10 per million tokens with a 200K context window and 32K max output, it sits in the sweet spot between GPT-5’s general capability and the focused performance developers need for code generation, debugging, and autonomous task execution.

GPT-5.3 Codex Pricing

MetricValue
Input Price$2.00 / 1M tokens
Output Price$10.00 / 1M tokens
Context Window200,000 tokens
Max Output32,768 tokens
Encodingo200k_base
Release DateFebruary 2026

How GPT-5.3 Codex Compares

ModelInputOutputContextMax OutputBest For
GPT-5.3 Codex$2.00$10.00200K32KAgentic coding
GPT-5$1.25$10.00400K—General flagship
GPT-4.1$2.00$8.001M—Long-context code
Claude Opus 4.6$5.00$25.00200K128KMaximum code quality
Claude Sonnet 4.6$3.00$15.00200K64KBalanced code quality
Gemini 3.1 Pro$1.25$10.001M16KMultimodal + reasoning
DeepSeek V3.2$0.27$1.10128K—Budget coding

Key takeaway: GPT-5.3 Codex is 60% cheaper than Claude Opus 4.6 for input and 60% cheaper for output. Its 32K max output is purpose-built for generating complete files, refactoring entire modules, or producing multi-file changes in a single response. The tradeoff: Claude Opus 4.6 (80.8% SWE-bench) still leads on raw code quality benchmarks.

Monthly Cost Estimates

Solo Developer (100K input + 50K output tokens/day)

ModelMonthly Cost
DeepSeek V3.2$2.46
GPT-5$18.75
GPT-5.3 Codex$21.00
Claude Sonnet 4.6$31.50
Claude Opus 4.6$52.50

Startup Team (1M input + 500K output tokens/day)

ModelMonthly Cost
DeepSeek V3.2$24.60
GPT-5$187.50
GPT-5.3 Codex$210.00
Claude Sonnet 4.6$315.00
Claude Opus 4.6$525.00

Production Scale (10M input + 5M output tokens/day)

ModelMonthly Cost
DeepSeek V3.2$246
GPT-5$1,875
GPT-5.3 Codex$2,100
Claude Sonnet 4.6$3,150
Claude Opus 4.6$5,250

Run your own numbers: AI Model Pricing Calculator.

What Makes GPT-5.3 Codex Different

Built for Agentic Workflows

GPT-5.3 Codex isn’t just another code model. It’s specifically optimized for agent loops — the pattern where an AI model:

  1. Reads context (codebase, error messages, requirements)
  2. Plans a multi-step solution
  3. Generates code changes across multiple files
  4. Validates its own output
  5. Iterates until the task is complete

This makes it ideal for tools like GitHub Copilot Workspace, Cursor, Claude Code, and custom coding agents.

25% Faster Than GPT-5.2

Speed matters enormously in agentic workflows where the model may make 10-50 sequential calls to complete a task. A 25% latency improvement compounds significantly: a 20-step coding task that took 60 seconds with GPT-5.2 now takes ~45 seconds.

32K Max Output

The 32,768-token max output is designed for code generation. For context:

  • 32K tokens ≈ 800-1,000 lines of code
  • Generate complete React components, API endpoints, or test suites in one shot
  • Produce multi-file diffs and refactoring suggestions without truncation

This is smaller than Claude Opus 4.6’s 128K output but substantially larger than most models’ defaults.

200K Context Window

200K tokens of context is sufficient for:

  • An entire medium-sized codebase (50-100 files worth of relevant code)
  • Full project documentation + source files
  • Complete error traces + stack frames + surrounding code

Getting Started

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(api_key="your-api-key")

response = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[
        {
            "role": "system",
            "content": "You are an expert software engineer. Write clean, well-tested code."
        },
        {
            "role": "user",
            "content": "Refactor this Express.js API to use async/await and add error handling middleware."
        }
    ],
    temperature=0.2,
    max_tokens=16000
)

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

TypeScript

import OpenAI from 'openai';

const client = new OpenAI({ apiKey: 'your-api-key' });

const response = await client.chat.completions.create({
  model: 'gpt-5.3-codex',
  messages: [
    {
      role: 'system',
      content: 'You are an expert software engineer. Write clean, well-tested code.',
    },
    {
      role: 'user',
      content: 'Build a complete REST API for a todo app with CRUD operations, validation, and tests.',
    },
  ],
  temperature: 0.2,
  max_tokens: 16000,
});

console.log(response.choices[0].message.content);

With Streaming (for real-time code generation UX)

stream = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[{"role": "user", "content": "Write a comprehensive test suite for this module..."}],
    stream=True,
    max_tokens=16000
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

When to Choose GPT-5.3 Codex

Use CaseBest ChoiceWhy
Agentic coding (multi-step)GPT-5.3 CodexPurpose-built, fastest iteration speed
Code generation + large outputGPT-5.3 Codex32K output for complete files
Maximum code qualityClaude Opus 4.680.8% SWE-bench, 128K output
Budget codingDeepSeek V3.2$0.27/M, good enough for simple tasks
Long-context code analysisGPT-4.11M context at same $2/M price
General-purpose flagshipGPT-5Cheaper input ($1.25 vs $2), more context (400K)
Multimodal + codeGemini 3.1 ProVideo understanding + 1M context

GPT-5.3 Codex vs GPT-5 vs GPT-4.1

FeatureGPT-5.3 CodexGPT-5GPT-4.1
Input Price$2.00/M$1.25/M$2.00/M
Output Price$10.00/M$10.00/M$8.00/M
Context200K400K1M
Max Output32K——
SpeedFastestStandardStandard
Best ForAgentic codingGeneral tasksLong-context

Choose GPT-5.3 Codex when you need speed and coding focus. Choose GPT-5 for general-purpose tasks at lower input cost. Choose GPT-4.1 when you need maximum context for analyzing entire repositories.

Cost Optimization Tips

1. Use Cached Input Pricing

OpenAI offers cached input pricing for repeated system prompts. If your coding agent sends the same system prompt with every request, caching can cut input costs significantly.

2. Route by Task Complexity

Not every coding task needs GPT-5.3 Codex. Use a tiered approach:

  • Simple completions (autocomplete, simple functions): GPT-4.1 Mini or GPT-4.1 Nano
  • Standard coding (feature implementation, bug fixes): GPT-5.3 Codex
  • Complex architecture (system design, multi-file refactoring): Claude Opus 4.6

3. Batch Non-Urgent Tasks

Use OpenAI’s Batch API for non-real-time coding tasks (test generation, documentation, code reviews) at 50% off.

4. Monitor Token Usage

Use our Token Counter to measure how many tokens your prompts actually consume. Coding prompts with large context (repo files, error logs) can be surprisingly expensive.

Bottom Line

GPT-5.3 Codex fills a clear gap in OpenAI’s lineup: a fast, focused coding model optimized for the agentic workflows that are becoming the standard way developers interact with AI. At $2/$10 per million tokens, it’s priced competitively — cheaper than Claude Opus 4.6 while being 25% faster for iterative coding tasks.

If you’re building or using AI coding tools (Copilot, Cursor, custom agents), GPT-5.3 Codex should be on your shortlist.

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