Claude 3.5 Sonnet vs GPT-5.3 Codex
Claude 3.5 Sonnet vs GPT-5.3 Codex: GPT-5.3 Codex is cheaper for input-heavy usage ($1.75/M vs $3.00/M input tokens), while GPT-5.3 Codex is better for long-context tasks (256,000 tokens).
Direct answer: choose GPT-5.3 Codex for lower token spend and choose GPT-5.3 Codex when your workload needs longer context.
Compare input and output token pricing, context windows, and monthly cost estimates on one page so you can pick the cheaper model fast.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Claude 3.5 Sonnet
GPT-5.3 Codex
Cost Differences
GPT-5.3 Codex costs less than Claude 3.5 Sonnet
Quick Recommendation
Winner for direct API pricing: GPT-5.3 Codex. At the default workload, GPT-5.3 Codex saves about $5.25/month ($63.875/year) versus Claude 3.5 Sonnet.
Feature Comparison
| Feature | Claude 3.5 Sonnet | GPT-5.3 Codex |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Input Price | $3.00/1M tokens | $1.75/1M tokens |
| Output Price | $15.00/1M tokens | $14.00/1M tokens |
| Context Window | 200,000 tokens | 256,000 tokens |
| Max Output | 8,192 tokens | 32,768 tokens |
| Category | balanced | coding |
| Capabilities | textvisioncode | textcode |
| Release Date | 10/22/2024 | 3/1/2026 |
Claude 3.5 Sonnet vs GPT-5.3 Codex: Which Should You Choose?
Choosing between Claude 3.5 Sonnet and GPT-5.3 Codex depends on your priorities: cost efficiency, context length, or raw capability. GPT-5.3 Codex is the more affordable option at $1.75/1M input tokens — 42% cheaper than Claude 3.5 Sonnet. Meanwhile, GPT-5.3 Codex offers a significantly larger context window at 256,000 tokens vs 200,000 for Claude 3.5 Sonnet.
These models come from different providers — Anthropic and OpenAI — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with Anthropic, switching to OpenAIinvolves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: Claude 3.5 Sonnet is a balanced model while GPT-5.3 Codex is coding. This means they're optimized for different workloads. GPT-5.3 Codex targets more demanding workloads, while Claude 3.5 Sonnet provides a cost-effective option for everyday tasks.
Output costs matter too. Claude 3.5 Sonnet charges $15.00/1M output tokens vs $14.00 for GPT-5.3 Codex. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. GPT-5.3 Codex has the edge here at $14.00/1M output tokens.
Multimodal capabilities: Claude 3.5 Sonnet supports vision (image inputs) while GPT-5.3 Codex is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose Claude 3.5 Sonnet when:
- • You need more capabilities (vision)
- • You're already using Anthropic's API ecosystem
Choose GPT-5.3 Codex when:
- • Budget is a primary concern
- • You need a larger context window (256,000 tokens)
- • You need longer outputs (up to 32,768 tokens)
- • You're already using OpenAI's API ecosystem
Pros and Caveats at a Glance
Claude 3.5 Sonnet
- • Input pricing: $3.00/M tokens
- • Output pricing: $15.00/M tokens
- • Context window: 200,000 tokens
- • Max output: 8,192 tokens
Watch out for
- • Higher input cost than GPT-5.3 Codex
- • Higher output cost than GPT-5.3 Codex
- • Smaller context window than GPT-5.3 Codex
GPT-5.3 Codex
- • Input pricing: $1.75/M tokens
- • Output pricing: $14.00/M tokens
- • Context window: 256,000 tokens
- • Max output: 32,768 tokens
Watch out for
- • Trade-offs are minor in this matchup.
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Claude 3.5 Sonnet (Anthropic)
GPT-5.3 Codex (OpenAI)
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Which is cheaper, Claude 3.5 Sonnet or GPT-5.3 Codex?▼
What is the context window difference between Claude 3.5 Sonnet and GPT-5.3 Codex?▼
Which model is better for AI Chatbot?▼
Which model has better overall pricing for heavy usage?▼
Where can I compare Anthropic and OpenAI API pricing beyond this model matchup?▼
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