Claude Opus 4.7 vs GPT-5.3 Codex
anthropic/claude-opus-4.7 · openai/gpt-5.3-codex
As of November 2, 2026, Claude Opus 4.7 is the cheaper API on UberLLM: $0.0350 per million input tokens and $0.1750 per million output, versus $0.1794 / $1.44 for GPT-5.3 Codex, about 88% less when output is 3 tokens for every input token. These are the best prices on the market right now. Both work with one OpenAI-compatible key and UberLLM adds no fee. Choose Claude Opus 4.7 to cut cost; choose GPT-5.3 Codex when its capabilities fit your task better.
Claude Opus 4.7 vs GPT-5.3 Codex, side by side
| Spec | Claude Opus 4.7 | GPT-5.3 Codex |
|---|---|---|
| Price · input / 1M | $0.0350 | $0.1794 |
| Price · output / 1M | $0.1750 | $1.44 |
| Context length | 1,000,000 | 400,000 |
| Max output tokens | 128,000 | 128,000 |
| Input modalities | text, image | text, image |
| Output modalities | text | text |
| Tools / functions | Yes | Yes |
| Vision (image input) | Yes | Yes |
| Reasoning | — | Yes |
| % off list | 99.3% off | 89.8% off |
Best prices on UberLLM right now. No UberLLM fee on requests; adding credit by card costs 5.5% + $0.50. Updated November 2, 2026. A price more than 70% below list is what buyers were billed, not an error. Why so cheap?
Claude Opus 4.7 vs GPT-5.3 Codex — FAQ
Is Claude Opus 4.7 or GPT-5.3 Codex cheaper?
Claude Opus 4.7 is cheaper on a blended token mix, by about 88%. A request costs what its provider charged, UberLLM adds no fee, and every request is listed with its cost in Activity.
What's the price difference between Claude Opus 4.7 and GPT-5.3 Codex?
Claude Opus 4.7: $0.0350 in / $0.1750 out per 1M tokens. GPT-5.3 Codex: $0.1794 in / $1.44 out per 1M tokens. That's roughly a 88% blended difference. Prices are live; UberLLM adds no fee.
Can I use both Claude Opus 4.7 and GPT-5.3 Codex with one API?
Yes. Both are served through a single OpenAI-compatible endpoint — set the base URL to https://api.uberllm.dev/v1 and your UberLLM key, then switch between them by changing the model id. Anthropic SDKs work via https://api.uberllm.dev/v1/messages.