Straight answers, pulled from across all 50 breakdowns.
What is Claude Opus 5?
Claude Opus 5 is Anthropic’s new default Opus model, launched July 24, 2026 and included in every paid Claude plan (Pro, Max, Team, Enterprise) with no extra usage cap. It’s positioned as near-Fable-5 capability at roughly half the price, and it’s now the default model on Claude Max and the strongest model available to Claude Pro. Anthropic pitches it as state-of-the-art on its own coding evals while costing the same per token as the older Opus 4.8.
Opus 5 vs Fable 5 — which is better?
It’s close, and cost is the tiebreaker. Anthropic’s own charts put Opus 5 at or near Fable 5 on most benchmarks while costing about half as much per task. Creators split the live tests 1-1 to 2-1 on raw output quality — Naman and Alex Finn preferred Opus 5’s visual builds, while Fable 5 still led on a denser flight-sim city and on multidisciplinary reasoning, legal, and health tasks. The consensus: comparable power, but Opus 5 wins on price.
Opus 5 vs Opus 4.8 — what changed?
Same price ($5/$25 per million input/output tokens), much bigger model. Opus 5 more than doubles Opus 4.8 on Frontier-Bench software-engineering tasks (43.3% vs 21.1% in Nick Saraev’s reading), and jumps from 1.5% to 30.2% on ARC-AGI-3 novel problem-solving — a 20x leap in one generation. Chase AI calls the 4.8-to-5 jump a bigger leap than the gap between Opus 5 and Fable 5. The new behaviors are self-verification and error recovery: it checks its work against a goal and iterates instead of one-shotting.
How much does Opus 5 cost, and is it worth it?
$5 per million input tokens and $25 per million output tokens — unchanged from Opus 4.8 and about half of Fable 5’s rate. But half price on the sheet is not half price in practice: Opus 5 burns more tokens per task (about 37k to Fable’s 33k on Artificial Analysis), so Theo measured the real-world gap at closer to 20-25%, and Nate Herk’s 18 sessions found Opus ran roughly three times longer and sometimes cost MORE in total — $112 versus Fable’s $73 on one structural-simulator build. The clearer win is on subscriptions: Claude plans cap Fable 5 at 50% of your weekly limit but let Opus 5 use 100% of it, so switching roughly doubles how much work your plan covers.
How should I prompt Claude Opus 5?
Anthropic published its own prompting guide because pre-Opus-5 habits produce worse results, and the headline rule is a reversal: hand it the entire job in one prompt — goal, assets, and where the finished work should land — instead of breaking it into small sequential steps the way older Claude models rewarded. Then rein it in, because Opus 5 over-delivers: state explicitly what NOT to build or touch, cap how long the response should be, cap the size of the deliverable itself, and don’t tell it to double-check its work (it already verifies as it goes, so asking again just burns tokens). Anthropic also shipped new context-engineering guidance saying you no longer need to over-specify instructions in system prompts and CLAUDE.md the way earlier models required.
Which Claude model should I use on my plan?
It depends on the tier. On the $20 Pro plan, Anthropic’s guidance is to default to Sonnet and switch to Opus 5 only for complex tasks or when Sonnet falls short. On $100-plus Max plans, Opus 5 can be your default for nearly all knowledge work, with Fable 5 demoted to a second opinion when you’re unhappy with what Opus produced — not an automatic upgrade. On effort: low on Pro, medium on Max, as a default that beats fiddling with the setting per task.
What effort level should I use on Opus 5?
Medium is the sweet spot for most work; drop to low for simple tasks and only reach for high on the genuinely hard 20%. Chase AI found dropping from max to low effort cut cost per task from about $22 to $3.76 — over 80% — while low effort still hit a 60% pass rate on a long-horizon agentic benchmark. Every went further: Opus 5 actually performs better at medium or low than at high or max, so cranking the dial can cost you both money and quality.
Is Opus 5 good at coding?
Yes — it’s state-of-the-art on Anthropic’s agentic coding evals, leading Frontier-Bench at 43.3% and one-shotting playable game clones, a macOS clone, and 3D simulations in creator tests. Its real edge is cost-per-task and self-verification: fixing bugs at the root cause and building its own test harnesses. Caveats: Fable 5 and Kimi K3 still edge it on some front-end polish and cybersecurity work, and Every found it stops early on big, complex skill files. Codex users on GPT-5.6 saw little reason to switch.