CorX Labs

Head to head

Claude Opus 5 vs GPT-5

Claude Opus 5 from Anthropic against GPT-5 from OpenAI — specification, price and every benchmark both makers have published, in one table.

Benchmarks

No shared benchmarks

These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.

Price

GPT-5 is cheaper

On a 3:1 input-to-output mix, GPT-5 costs $3.44 per million tokens against $10.00 for Claude Opus 5 — about 2.9× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.

What actually differs

  • Claude Opus 5 takes 1M tokens of context against 400K — 2.5× more room for long documents or a large codebase.

Scorecard

Which is better at what

Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.

Which model scores higher in each capability category, averaged over the benchmarks all of them report.
CategoryClaude Opus 5GPT-5Better at this
ReasoningOnly one model reports thisNot comparable
MathsOnly one model reports thisNot comparable
CodingOnly one model reports thisNot comparable
KnowledgeNeither model reports thisNot comparable
MultimodalOnly one model reports thisNot comparable
Instruction followingNeither model reports thisNot comparable
Human preferenceNeither model reports thisNot comparable
Categories wonNo category has a test both models report

Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.

Side by side

Claude Opus 5 and GPT-5, row by row

AttributeClaude Opus 5AnthropicGPT-5OpenAI
Specification
MakerWho built itAnthropicOpenAI
Released2026-072025-08
ArchitectureNot reportedMoE
Context windowHow much can go in at once1,000,000 tokens400,000 tokens
Max output128,000 tokens128,000 tokens
InputText, ImageText, Image
ReasoningSpends extra tokens thinking before it answersYesYes
Tool callingYesYes
Knowledge cutoff2026-052024-09
LicenceProprietaryProprietary
Open weightsCan you download and run it yourselfNoNo
Price
Input priceUSD per million tokens in$5.00$1.25
Output priceUSD per million tokens out$25.00$10.00
Cached input$0.50$0.125
Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone$10.00$3.44Cheapest
Published benchmarks
GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%.Not reported85.7%
AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning.Not reported94.6%
SWE-bench Verified500 human-validated GitHub issues from real Python repositories. The model must produce a patch that makes the project's own tests pass.Not reported74.9%
Terminal-Bench 2.1The 2.1 revision of the terminal agent benchmark. Scores on it are not comparable with Terminal-Bench 4.0 — the task set changed.89.1%IndependentNot reported
Frontier-Bench v0.1Novel problems built to resist memorisation, scored on whether the model gets anywhere at all. Absolute numbers are low by design.43.3%Not reported
MMMUCollege-level questions that require reading charts, diagrams, tables and photographs alongside the text.Not reported84.2%
LinksFull pageFull page
Row verified2026-092026-08

Where these numbers come from

Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.

A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.

Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.

Questions

Claude Opus 5 or GPT-5?

Is Claude Opus 5 better than GPT-5?

These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.

Which is cheaper, Claude Opus 5 or GPT-5?

On a 3:1 input-to-output mix, GPT-5 costs $3.44 per million tokens against $10.00 for Claude Opus 5 — about 2.9× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.

What is the difference between Claude Opus 5 and GPT-5?

Claude Opus 5 takes 1M tokens of context against 400K — 2.5× more room for long documents or a large codebase.