AI / Comparison
Opus 5 against GPT-5.6 on the work developers actually do
The verdict
For the agent you leave running - a refactor across forty files, a two-hour session, a migration you review rather than write - take Claude Opus 5. Anthropic positions it explicitly for complex agentic coding, it is cheaper per output token than the GPT-5.6 flagship, and its reliable knowledge cutoff of May 2026 is the most recent of any model here, which matters more than a benchmark point when you are working with a library that shipped six months ago. For breadth across a platform, and for anything cost-sensitive at volume, take GPT-5.6. Its Sol tier is competitive on every agentic coding evaluation published this year, and the Terra and Luna tiers undercut Anthropic's mid and small models badly enough to decide any high-throughput workload on price alone.
Every figure on this page was checked against Anthropic's and OpenAI's own documentation in August 2026. Both families have shipped a flagship inside the last eight weeks - GPT-5.6 reached general availability on July 9, 2026, and Claude Opus 5 landed on July 24 - so assume anything undated elsewhere is stale.
Claude vs GPT: the published specifications
Prices are per million tokens on the standard synchronous API, as listed by each vendor in August 2026. Batch and caching discounts apply on both sides and are not shown.
| Dimension | Claude Opus 5 | GPT-5.6 Sol |
|---|---|---|
| Released | July 24, 2026 | July 9, 2026, alongside Terra and Luna |
| Positioned for | Complex agentic coding and enterprise work | Complex professional work; OpenAI's stated best coding model |
| Price per MTok | 5 dollars in, 25 dollars out | 5 dollars in, 30 dollars out |
| Context window | 1M tokens | 1.05M tokens |
| Max output | 128k tokens, or up to 300k on the Batch API with a beta header | 128k tokens |
| Reliable knowledge cutoff | May 2026 | February 16, 2026 |
| Reasoning control | Adaptive thinking plus an effort parameter that defaults to high on the API and in Claude Code | Reasoning effort selectable per request and from the Codex CLI |
| Cheaper siblings | Sonnet 5 at 3 dollars in and 15 out, discounted to 2 and 10 through August 31, 2026; Haiku 4.5 at 1 and 5 | Terra at 2 dollars in and 12 out; Luna at 20 cents in and 1.20 out, all on the same 1.05M context |
| Halo model above it | Claude Fable 5, generally available June 9, 2026, at 10 dollars in and 50 out | Sol is the top public tier of the 5.6 family |
Gotcha: the context windows look identical and are not comparable token for token. Anthropic notes that models from Opus 4.7 onward use a new tokenizer that produces roughly 30 percent more tokens for the same text. Measure your own corpus rather than assuming a 1M window holds the same repository on both sides.
What the benchmarks claim, and how much to believe
Treat every number below as a claim with a date, not a property. Agentic coding scores depend on the harness as much as the model, and the published leaderboards moved several times during 2026.
Anthropic's own Opus 5 claims, July 24, 2026. The launch post is notable for what it does not lead with. There is no SWE-bench headline. Instead Anthropic quotes agentic and computer-use evaluations: Frontier-Bench v0.1, where Opus 5 more than doubles Opus 4.8's performance at a lower cost per task; CursorBench 3.2, where it lands within 0.5 percent of Fable 5's peak score at half the cost per task; ARC-AGI 3, where it scores three times the next-best model; OSWorld 2.0, where it surpasses Fable 5's best result at just over a third of the cost; and Zapier's AutomationBench, where the pass rate is around 1.5 times the next-best model. Cost per task appears in almost every one of those sentences, which is the story.
OpenAI's GPT-5.6 claims, July 9, 2026. Sol is described as the strongest coding model in the family and as OpenAI's strongest cybersecurity model, with independent aggregators reporting Terminal-Bench 2.1 results in the high eighties for Sol and higher still for its maximum-effort configuration. Third-party coding-agent indices published this summer put GPT-5.6 Sol running inside Codex at or near the top of their composite rankings.
The figures circulating that neither vendor published. You will see "96 percent on SWE-bench Verified" attributed to Opus 5 across a lot of summary sites. That number comes from third-party aggregation, not from Anthropic's announcement. It may well be accurate; it is not a vendor claim, and it should not be quoted as one. The same caution applies to any single Terminal-Bench figure - which variant, which effort level, and which harness all move it by several points.
The honest summary: on frontier agentic coding these two are close enough that the harness, the prompt, and your repository's test suite matter more than the model choice. Where they are genuinely not close is the price ladder below the flagship, and that is where most real spending happens.
When each family is the right call
Choose Claude when
- The workload is a long-running coding agent. This is the use case Anthropic optimizes for and markets against, and Claude Code's adoption among senior developers reflects it.
- You are working with libraries and APIs that changed in the first half of 2026. Opus 5's May 2026 knowledge cutoff is three months fresher than GPT-5.6's.
- Output tokens dominate your bill. At 25 dollars against 30 per million out, sustained code generation is measurably cheaper on Opus 5 at identical input pricing.
- You need very large single responses. The Batch API supports up to 300k output tokens behind a beta header, which no comparable tier here matches.
- You want a step above the workhorse without leaving the family. Fable 5 exists at 10 and 50 per million for the tasks where cost genuinely does not matter.
Choose GPT when
- Volume is the constraint. Luna at 20 cents in and 1.20 out on a 1.05M context has no equivalent on the Anthropic price list, and it decides any high-throughput classification, extraction, or summarization workload outright.
- You want one price ladder with one context window. Sol, Terra, and Luna all take 1.05M tokens, so you can downgrade a call to a cheaper tier without redesigning the prompt.
- Your team already pays for ChatGPT. Codex is bundled with the subscription rather than sold separately, which removes a procurement conversation entirely.
- The task needs OpenAI's wider tool surface - hosted web search, file search, and computer use available through one API alongside the model.
- You need a mid-tier permanently rather than promotionally. Terra at 2 and 12 is list price; Sonnet 5's matching 2 and 10 is introductory pricing that ends August 31, 2026.
In practice: agent behavior beats benchmark scores
The thing that decides whether a two-hour agent session succeeds is not raw capability. It is whether the model asks before doing something irreversible, whether it notices its own failed command instead of confidently proceeding, whether it stays coherent after compaction, and whether it stops when it is stuck rather than trying the same fix a fourth time. None of those are on a leaderboard, and all of them differ between families and between harnesses.
The practical consequence is that you should test on your own repository before committing. Give both the same task with the same rules file - a real one, from your backlog, that touches at least four files - and score them on the review, not the demo. How much did you have to correct? What did it delete? Did it invent a dependency? That afternoon is worth more than every benchmark on this page.
Also worth saying plainly: you do not have to pick one forever. Cursor exposes models from multiple vendors, Copilot gates premium models by tier, and open-source agents like Aider and Cline point at any endpoint you give them. Claude Code is the exception - it runs Anthropic models only - which is a genuine consideration if multi-vendor flexibility is a requirement rather than a preference.
Where this fits
Models are the layer underneath the tools. For the tool decision, read Claude Code vs Codex, which pairs each model family with its native terminal agent, or the full assistant field guide for all ten options. The workflow guide covers matching model tier to task, which is the single biggest lever on cost.
Building AI features into a product rather than using AI to write it? The AI APIs and SDKs directory covers the client libraries, gateways, and vector stores, and the AI app stack guide assembles a full recommendation. Official model documentation lives at platform.claude.com and developers.openai.com.