Anthropic's Claude Opus 5.5 Ships as Five Models in One, Tuning Intelligence Against Cost

Anthropic dropped Claude Opus 5.5 this September, and the interesting part isn't just the flagship number, it's the fact that "Claude Opus 5.5" isn't really one model. It's five, each running the same underlying architecture at a different reasoning-effort setting: low, medium, high, xhigh, and max. Artificial Analysis benchmarked all five, and the spread between them tells you a lot about how Anthropic wants developers to think about cost versus capability going forward.

This is adaptive reasoning as a first-class product knob, not just a system prompt trick. You're not choosing between different model sizes. You're choosing how hard the same model thinks before it answers, and that choice moves the needle on intelligence score, latency, and price simultaneously.

At the top end, Claude Opus 5.5 running max effort hits 58 on the Artificial Analysis Intelligence Index, currently the highest score on the board out of 673 models tracked. That's a meaningful jump for Anthropic's lineup, edging past the previous Claude Opus 5 (max) and putting daylight between it and competitors like GPT-6 Astra (max) and Grok 4.7 (xhigh) in this comparison set. The Intelligence Index itself is a composite: Artificial Analysis's v4.3.2 methodology rolls together ten evaluations, including AA-Briefcase, GDPval-AA, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, and AA-LCR. It's a broad enough basket that a single point movement usually reflects real, distributed capability gains rather than one benchmark being gamed.

The Five-Tier Tradeoff

Here's where it gets practically useful for anyone actually shipping with this model. The five variants aren't just labeled differently, they perform differently across every axis that matters for production use:

• Intelligence: max scores 58, xhigh scores 56, high scores 54, medium scores 51, and low scores 42. That's a 16-point gap between the cheapest and priciest tier, larger than the gap between some entirely different model families.
• Output speed: counterintuitively, high effort is the fastest at 90 tokens/second, followed by xhigh at 84 t/s, low at 81 t/s, and medium at 76 t/s. Max effort doesn't report a speed figure in this data, likely because its reasoning overhead makes throughput measurement less meaningful for that mode.
• Latency: low effort delivers the quickest time-to-first-answer-token at 7.03 seconds, which matters if you're building anything interactive rather than batch-oriented.
• Pricing: low effort costs $0.55 per Intelligence Index task, scaling up through medium at $1.34, high at $1.82, xhigh at $3.46, and max at $5.98. That's roughly an 11x spread from cheapest to most expensive tier, all within the same model release.

What this tells you is that Anthropic has effectively built in a dial for the classic intelligence-versus-cost tradeoff, and it's a pretty steep one. Going from low to max effort roughly triples your Intelligence Index score improvement per dollar spent in absolute terms, but you're paying nearly 11 times more per task to get there. Whether that's worth it depends entirely on your use case. A customer support bot doesn't need max effort. A one-shot codebase migration or a research synthesis task might.

Plotted against cost, Claude Opus 5.5's variants sit right on or near what Artificial Analysis calls the Pareto frontier, the line representing the best available intelligence for a given price point. That's the real headline here: Anthropic isn't just topping the leaderboard at the high end, it's also competitive at the low end, with the $0.55 low-effort tier holding its own against budget options from Alibaba, Z AI, and others in the same price band.

Under the hood, Opus 5.5 supports a 1 million token context window, which Artificial Analysis notes is roughly equivalent to 1,500 pages of standard A4 text at 12pt Arial. That's the same generous context ceiling Anthropic has been pushing across its recent releases, useful for long document analysis, large codebase review, or extended agentic workflows where you don't want to worry about hitting a wall mid-task. Input modality covers both text and image; output remains text-only, so no native image or audio generation here. This is confirmed as a reasoning model, meaning it does internal chain-of-thought style processing before producing a final answer, though Artificial Analysis notes a non-reasoning variant may also exist separately.

Worth noting: all five variants ship with what Anthropic calls "default fallback" behavior, suggesting some kind of automatic degradation path if a given effort tier hits capacity or fails, though the exact mechanics aren't detailed in the release data.

Key Takeaways

Claude Opus 5.5's real innovation isn't a single benchmark number, it's operationalizing the intelligence-cost tradeoff into five distinct, individually priced tiers within one release. The top tier's 58 score puts it at the front of the pack among tracked models. But the more useful signal for engineering teams is the shape of the curve: an 11x price difference between low and max effort, a 16-point intelligence swing, and speed characteristics that don't move in a straight line with cost, since high effort actually outpaces xhigh on raw tokens per second. If you're integrating this model, the smart move is benchmarking your specific workload against at least three of the five tiers before committing, because the "best" version of Opus 5.5 depends entirely on what you're optimizing for.