Claude Opus 4.7 vs GPT-5.3 Codex
Wins 1 of 7 areas
Coding
Wins 3 of 7 areas
Reasoning · Long documents · Following instructions
GPT-5.3 Codex wins more areas, narrowly.Claude Opus 4.7 is better at coding.
Scores updated · 22 tests both models report · How we compare
Where each one wins
Tests won in each of the seven areas where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
- ReasoningHard problems that need careful thinking01GPT-5.3 Codex1 of 3 tests · 2 ties
- Long documentsFinding answers in very long texts01GPT-5.3 Codex1 of 1 test
- Following instructionsDoing exactly what it is asked01GPT-5.3 Codex1 of 1 test
- CodingWriting and fixing software31Claude Opus 4.73 of 4 tests
- AgentsCarrying out multi-step tasks on its own11Even1 each
- FactsGetting facts right instead of making them up11Even1 each
- Images and chartsUnderstanding pictures, charts and video00Even0 each · 1 tie
Images and charts, long documents and following instructions rest on a single test each.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
GPT-5.3 Codex costs 48% less for the same work.
The biggest differences
The three tests each model wins by the widest margin. Scores are out of 100.
Where GPT-5.3 Codex pulls ahead
Where Claude Opus 4.7 pulls ahead
- Avoids making up answers it doesn't knowAA-Omniscience · Non-hallucination+46.9points ahead
- Completes tasks by operating a computer desktopOSWorld-Verified+13.3points ahead
- Long, multi-file coding tasks in real codebasesSWE-bench Pro+7.5points ahead
Every test, side by side
All 22 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingClaude Opus 4.7
- LMArena · WebDevClaude Opus 4.7 by 149 rating points15581409+149 rating
- SWE-bench ProClaude Opus 4.7 by 7.564.356.8+7.5
- Terminal-Bench HardGPT-5.3 Codex by 1.551.553+1.5
- SciCodeClaude Opus 4.7 by 1.354.553.2+1.3
AgentsEven
- OSWorld-VerifiedClaude Opus 4.7 by 13.37864.7+13.3
- GDPValGPT-5.3 Codex by 6.242.849+6.2
ReasoningGPT-5.3 Codex
- CritPtGPT-5.3 Codex by 4.91216.9+4.9
- Humanity's Last Examtie42.342.5tie
- GPQA Diamondtie91.491.5tie
FactsEven
- AA-Omniscience · Non-hallucinationClaude Opus 4.7 by 46.957.710.8+46.9
- AA-Omniscience · AccuracyGPT-5.3 Codex by 448.952.9+4
Following instructionsGPT-5.3 Codex
- IFBenchGPT-5.3 Codex by 16.858.675.4+16.8
Other results8 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- HiL-Bench (Tools-allowed)Claude Opus 4.7 by 37.441.74.3+37.4
- AA Agentic IndexGPT-5.3 Codex by 2139.560.5+21
- Artificial Analysis Coding IndexClaude Opus 4.7 by 20.573.653.1+20.5
- AA-OmniscienceClaude Opus 4.7 by 16.427.310.9+16.4
- AA IntelligenceClaude Opus 4.7 by 8.240.732.5+8.2
- Terminal-Bench 2.0GPT-5.3 Codex by 7.969.477.3+7.9
- LiveBenchClaude Opus 4.7 by 4.176.972.8+4.1
- τ²-Bench Telecom (AA run)Claude Opus 4.7 by 2.688.686+2.6
Questions people ask
Which is better, Claude Opus 4.7 or GPT-5.3 Codex?
GPT-5.3 Codex wins three of the seven areas where both have results: reasoning, long documents and following instructions. Claude Opus 4.7 wins coding. They are level on agents, facts and images and charts.
Which is better for coding?
Claude Opus 4.7. It wins 3 of the 4 coding tests both models report; GPT-5.3 Codex wins 1.
Which is cheaper?
Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GPT-5.3 Codex costs $1.75 and $14.00. That makes GPT-5.3 Codex about 48% cheaper for the same work.
How do you compare the two?
We use the 22 benchmark tests both models have published scores on. The verdict counts the 14 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 8 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.