Head to head
GPT-5.6 Sol vs Claude Opus 5
GPT-5.6 Sol from OpenAI against Claude Opus 5 from Anthropic — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
Claude Opus 5 leads
Claude Opus 5 wins 2 of the 2 benchmarks both models report, GPT-5.6 Sol wins 0, consistently. The average gap across shared tests is 7.6 points.
Price
GPT-5.6 Sol is cheaper
On a 3:1 input-to-output mix, GPT-5.6 Sol costs $8.00 per million tokens against $10.00 for Claude Opus 5 — about 1.2× 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
- GPT-5.6 Sol takes 1.1M tokens of context against 1M — 1.1× 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.
| Category | GPT-5.6 Sol | Claude Opus 5 | Better at this |
|---|---|---|---|
| ReasoningOnly one model reports this | — | — | Not comparable |
| MathsNeither model reports this | — | — | Not comparable |
| CodingTerminal-Bench 2.1, Terminal-Bench 4.0 | 63.0% | 70.7% | Claude Opus 5+7.6 |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalNeither model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories wonOut of 1 comparable | 0 | 1 | Claude Opus 5 |
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
GPT-5.6 Sol and Claude Opus 5, row by row
| Attribute | GPT-5.6 SolOpenAI | Claude Opus 5Anthropic |
|---|---|---|
| Specification | ||
| MakerWho built it | OpenAI | Anthropic |
| Released | 2026-07 | 2026-07 |
| Context windowHow much can go in at once | 1,050,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens |
| Input | Text, Image | Text, Image |
| ReasoningSpends extra tokens thinking before it answers | Yes | Yes |
| Tool calling | Yes | Yes |
| Knowledge cutoff | Not reported | 2026-05 |
| Licence | Proprietary | Proprietary |
| Open weightsCan you download and run it yourself | No | No |
| Price | ||
| Input priceUSD per million tokens in | $4.00 | $5.00 |
| Output priceUSD per million tokens out | $20.00 | $25.00 |
| Cached input | $0.40 | $0.50 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $8.00Cheapest | $10.00 |
| 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 reported | |
| SWE-bench ProA harder, contamination-resistant successor to SWE-bench Verified, drawn from commercial and copyleft repositories that were never public training data. | Not reported | |
| Terminal-Bench 2.1The 2.1 revision of the terminal agent benchmark. Scores on it are not comparable with Terminal-Bench 2.0 or 4.0 — the task set changed. | ||
| Frontier-Bench v0.1Novel problems built to resist memorisation, scored on whether the model gets anywhere at all. Absolute numbers are low by design. | Not reported | |
| Terminal-Bench 4.0End-to-end tasks in a real terminal — install, build, debug, run — scored on whether the machine ends up in the required state. | ||
| Links | Full page | Full page |
| Row verified | 2026-10 | 2026-10 |
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
GPT-5.6 Sol or Claude Opus 5?
Is GPT-5.6 Sol better than Claude Opus 5?
Claude Opus 5 wins 2 of the 2 benchmarks both models report, GPT-5.6 Sol wins 0, consistently. The average gap across shared tests is 7.6 points.
Which is cheaper, GPT-5.6 Sol or Claude Opus 5?
On a 3:1 input-to-output mix, GPT-5.6 Sol costs $8.00 per million tokens against $10.00 for Claude Opus 5 — about 1.2× 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 GPT-5.6 Sol and Claude Opus 5?
GPT-5.6 Sol takes 1.1M tokens of context against 1M — 1.1× more room for long documents or a large codebase.