Anthropic
Anthropic's latest generally available flagship. Improves on Opus 4.6 for advanced software engineering, long-running task reliability, self-verification, and high-resolution vision while keeping the same pricing.
Improves on Opus 4.6 for advanced software engineering, long-running task reliability, self-verification, and high-resolution vision while keeping the same pricing.
66.9
Quality Score
1298
Arena ELO
Undisclosed
Parameters
200K
Context
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69.3K
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0
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Apr 2026
Released
Launches
7
Benchmarks
5
Research
2
General
5
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
LiveCodeBench pass@1 62.4 across 1055 tasks
Interpreting law is one of the oldest jobs in the world. @MaxJunestrand, co-founder and CEO of @WeAreLegora, is bringing it into its next era with Claude. His bet: every new model release raises the tide, and Legora is building the boats for everyone else. https://t.co/EOM8PqALT4
View sourceWe’re expanding Project Glasswing. We’ve extended access to Claude Mythos Preview to approximately 150 additional organizations, based in more than fifteen countries. Read more about this expansion and our future plans for Project Glasswing: https://t.co/QrtHSBdRbh
View sourceBefore we ship a new model, these teams try to break it. They build with it, push it to its limits, and tell us where it falls short. What they find makes the final model better. https://t.co/Q4LD01zIIc
View sourceIntroducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude. Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day. https://t.co/2BgBGtgYGX
View sourceNew Anthropic Science Blog: Making Claude a chemist. To manipulate a molecule, chemists first need to understand its structure. Their main tool is NMR spectroscopy. We found Opus 4.7 matches—and on some tasks beats—dedicated NMR software. Read more: https://t.co/1jUvz7wdhV
Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. https://t.co/OVVPJO7VQx
Interpreting law is one of the oldest jobs in the world. @MaxJunestrand, co-founder and CEO of @WeAreLegora, is bringing it into its next era with Claude. His bet: every new model release raises the tide, and Legora is building the boats for everyone else. https://t.co/EOM8PqALT4
We’re expanding Project Glasswing. We’ve extended access to Claude Mythos Preview to approximately 150 additional organizations, based in more than fifteen countries. Read more about this expansion and our future plans for Project Glasswing: https://t.co/QrtHSBdRbh
Before we ship a new model, these teams try to break it. They build with it, push it to its limits, and tell us where it falls short. What they find makes the final model better. https://t.co/Q4LD01zIIc
We've raised $65 billion in Series H funding at a $965 billion post-money valuation, led by @AltimeterCap, Dragoneer, @Greenoaks, and @sequoia. This investment will help us advance our research and expand our capacity to meet growing demand for Claude.
Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude. Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day. https://t.co/2BgBGtgYGX
Introducing Claude Opus 4.7, our most capable Opus model yet. It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. You can hand off your hardest work with less supervision. https://t.co/PtlRdpQcG5
Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capability of LLMs. Derived from 49 high-quality, open-licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source datasets curation, intent label contextualization, and task format unification. IntentGrasp contains a large-scale training set of 262,759 instances and two evaluation sets: an All Set of 12,909 test cases and a more balanced and challenging Gem Set of 470 cases. Extensive evaluations on 20 LLMs across 7 families (including frontier models such as GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7) demonstrate unsatisfactory performance, with scores below 60% on All Set and below 25% on Gem set. Notably, 17 out of 20 tested models perform worse than a random-guess baseline (15.2%) on Gem Set, while the estimated human performance is ~81.1%, showing substantial room for improvement. To enhance such ability, this paper proposes Intentional Fine-Tuning (IFT), which fine-tunes the models on the training set in IntentGrasp, yielding significant gains of 30+ F1 points on All Set and 20+ points on Gem Set. Tellingly, the leave-one-domain-out (Lodo) experiments further demonstrate the strong cross-domain generalizability of IFT, verifying that it is a promising approach to substantially enhancing the intent understanding of LLMs. Overall, by benchmarking and boosting intent understanding ability, this study sheds light on a promising path towards more intentional, capable, and safe AI assistants for human benefits and social good.
LiveCodeBench pass@1 62.4 across 1055 tasks
Users report being able to hand off their hardest coding work—the kind that previously needed close supervision—to Opus 4.7 with confidence. And—although it is less broadly capable than our most powerful model, Claude Mythos Preview—it shows better results than Opus 4.6 across a range of benchmarks: Last week we announced Project Glasswing , highlighting the risks—and benefits—of AI models for cybersecurity. Clarence Huang VP of Technology Anthropic has already set the standa
GAIA score 58.1 from Jaram 4.0
GAIA score 58.1 from Jaram 4.0