Agentic coding: Claude Opus 5 is strongest on difficult coding tasks: multi-file features, larger refactors, and end-to-end feature work. It completes full tasks rather than leaving stubs or placeholders, and it performs best when given the complete task specification up front and left to run. It also performs well on easier tasks like single-turn edits, where the difference from prior models is smaller.
Code review and bug-finding: Claude Opus 5 reviews code with high precision and recall: it finds real bugs at a high rate per pass, and its additional findings are mostly real issues rather than false positives. Accuracy holds at lower effort settings, which supports a fast pass at review time and a more thorough pass later. If your review prompt says "only report high-severity issues" or "be conservative," the model may follow that instruction literally and report less; ask it to report everything and filter in a separate pass instead.
Efficiency at lower effort: low and medium effort produce strong quality at a fraction of the tokens and latency of higher settings, and they perform above the same settings on prior Opus models. Use them liberally as your primary control for token cost and response time wherever your evals show quality holds; for coding and agentic work, xhigh remains the recommended starting point. If you carried effort defaults over from a prior model, re-run an effort sweep on your own evals. See Effort for the full recommendations.
Vision: Claude Opus 5 is strong on chart, document, and diagram understanding, and on UI and frontend visual replication. Re-validate any prompt-side vision workarounds you tuned for prior models; they may no longer be needed. Vision performance is strongest when the model has tools to iteratively analyze, crop, and visually verify its work, and tool use is a more cost-effective lever than thinking alone.
Long-context work: Claude Opus 5 has a 1M token context window as both the default and the maximum, and its instruction following, tool calling, and reasoning stay consistent throughout the window.
Office and document tasks: Claude Opus 5 generates and works with complex, multi-sheet spreadsheets with non-trivial formulas, and it produces well-structured slide decks. Prompt it with any specific styles or templates it needs to follow.
Multi-agent coordination: Claude Opus 5 coordinates teams of subagents well, with effective writer-verifier patterns and few cases of agents overwriting each other's work. For cost-sensitive workloads, cap delegation; see Controlling subagent spawning.
About Claude Opus 5 on Product Hunt
“Near-Fable 5 intelligence at half the price”
Claude Opus 5 launched on Product Hunt on July 27th, 2026 and earned 420 upvotes and 13 comments, earning #3 Product of the Day. Opus 5 is a step change improvement for the Opus tier powering long-running agents while delivering improvements in coding and professional work.
On the analytics side, Claude Opus 5 competes within Messaging and Artificial Intelligence — topics that collectively have 527.1k followers on Product Hunt. The dashboard above tracks how Claude Opus 5 performed against the three products that launched closest to it on the same day.
Who hunted Claude Opus 5?
Claude Opus 5 was hunted by Chris Messina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Chris Messina
To get the most out of this model, here's the new prompting guide:
Agentic coding: Claude Opus 5 is strongest on difficult coding tasks: multi-file features, larger refactors, and end-to-end feature work. It completes full tasks rather than leaving stubs or placeholders, and it performs best when given the complete task specification up front and left to run. It also performs well on easier tasks like single-turn edits, where the difference from prior models is smaller.
Code review and bug-finding: Claude Opus 5 reviews code with high precision and recall: it finds real bugs at a high rate per pass, and its additional findings are mostly real issues rather than false positives. Accuracy holds at lower effort settings, which supports a fast pass at review time and a more thorough pass later. If your review prompt says "only report high-severity issues" or "be conservative," the model may follow that instruction literally and report less; ask it to report everything and filter in a separate pass instead.
Efficiency at lower effort: low and medium effort produce strong quality at a fraction of the tokens and latency of higher settings, and they perform above the same settings on prior Opus models. Use them liberally as your primary control for token cost and response time wherever your evals show quality holds; for coding and agentic work, xhigh remains the recommended starting point. If you carried effort defaults over from a prior model, re-run an effort sweep on your own evals. See Effort for the full recommendations.
Vision: Claude Opus 5 is strong on chart, document, and diagram understanding, and on UI and frontend visual replication. Re-validate any prompt-side vision workarounds you tuned for prior models; they may no longer be needed. Vision performance is strongest when the model has tools to iteratively analyze, crop, and visually verify its work, and tool use is a more cost-effective lever than thinking alone.
Long-context work: Claude Opus 5 has a 1M token context window as both the default and the maximum, and its instruction following, tool calling, and reasoning stay consistent throughout the window.
Office and document tasks: Claude Opus 5 generates and works with complex, multi-sheet spreadsheets with non-trivial formulas, and it produces well-structured slide decks. Prompt it with any specific styles or templates it needs to follow.
Multi-agent coordination: Claude Opus 5 coordinates teams of subagents well, with effective writer-verifier patterns and few cases of agents overwriting each other's work. For cost-sensitive workloads, cap delegation; see Controlling subagent spawning.