Both labs shipped a major model in the last six months: OpenAI's GPT-5 in March 2026, Anthropic's Claude Opus 4.7 in April. Benchmarks are now within a few points of each other on most leaderboards, which makes the leaderboard the wrong place to choose. The right way to choose is by job.
This guide maps five concrete workloads to the model that actually does them better in production today.
What changed in 2026
- GPT-5 ships with native multimodality. Voice, vision, and code execution are first-class — no separate endpoints — and tool latency dropped roughly 40% vs GPT-4o.
- Claude Opus 4.7 ships with a 1M-token context window and a much sharper "thinking" mode that pays off on multi-file code review and long legal/contract work.
- Pricing converged. GPT-5 is $4/M input · $20/M output. Opus 4.7 is $5/M · $25/M. Both labs offer prompt caching (90% input discount on cache hits).
Reasoning + math
GPT-5 leads on competition math (AIME, GPQA Diamond) by roughly 3-4 points. Opus 4.7 leads on multi-step business reasoning (financial models, contract review) where the chain-of-thought has to track many constraints over a long input. If you're building an agent that does step-by-step math problems, lean GPT-5. If you're doing structured analysis over a long document, lean Opus 4.7.
Coding
Opus 4.7 wins on real codebase work — refactoring across files, reviewing PRs, writing tests for legacy code. The 1M context lets you load a small repo whole. GPT-5 wins on greenfield script writing and tool-augmented coding (it spawns a Python sandbox more naturally). On HumanEval and SWE-bench Verified, the gap is small (<3 points) — but the integration patterns differ noticeably.
Long-context tasks
Opus 4.7's 1M context with stronger needle-in-haystack recall is the practical winner here. GPT-5 caps at 256K with a longer-context preview at 1M but recall above 200K is noticeably worse on adversarial benchmarks. For "read 60 contracts and find the indemnity clause" or "summarize this 400-page S-1," Opus is the safer pick.
Multimodal + voice
GPT-5 dominates. Voice latency is ~280ms end-to-end, vision works on PDFs and screenshots without a separate vision API, and the same model handles tool calls. Opus 4.7 handles vision well on stills but does not yet have native voice.
Tool use and agents
Both have parallel tool calling and structured outputs. GPT-5's Agents SDK is more mature in 2026, with built-in tracing. Opus 4.7 is the better thinker between tool calls — fewer wasted steps, better error recovery on flaky tools.
When to use which (pricing-aware)
| Workload |
Pick |
Why |
| Multi-file refactor / PR review |
Opus 4.7 |
1M context, stronger code reasoning |
| Voice agent / customer support |
GPT-5 |
Native voice, lowest latency |
| Long document Q&A |
Opus 4.7 |
Better recall past 200K |
| Greenfield script / one-shot code |
GPT-5 |
Slightly faster, cheaper |
| Cost-sensitive bulk classification |
Either Mini |
Pick by latency; both at ~$0.15/M |
| Multimodal data extraction |
GPT-5 |
Vision + structured output is more polished |
FAQ
Which is "smarter"?
On aggregate benchmarks, they trade leadership by 1-2 points across releases. There is no single "smarter" — there are workloads where each wins.
Should I switch all my apps to one or the other?
No. Most teams that ship at scale route by task: GPT-5 for voice and multimodal, Opus 4.7 for long-context reasoning and code review, a Mini/Haiku tier for the boring 80%.
What about price?
Output is where it bites. At 1k requests/day with 1k output tokens, GPT-5 runs ~$600/mo, Opus 4.7 runs ~$750/mo. Cache discounts close most of the gap if your prompts have stable system messages.
Where to go next
For related deep dives see Claude Opus 4.7 — everything new, Claude Sonnet vs Opus in 2026, and ChatGPT Pro vs Claude Pro vs Gemini Advanced.