Claude 5 Sonnet vs Claude 4.7 Opus

1. Anthropic's most agentic Sonnet yet

• A substantial upgrade over Sonnet 4.6 on reasoning, tool use, coding, and knowledge work - narrows the gap to Opus 4.8 while staying at Sonnet pricing.
• Early partners describe it finishing multi-step jobs end to end where prior Sonnet models used to stall halfway.

2. Clear benchmark gains over Sonnet 4.6

• 63.2% on SWE-bench Pro (vs 58.1%), 80.4% on Terminal-Bench 2.1 (vs 67.0%), 81.2% on OSWorld-Verified (vs 78.5%), and 57.4% on Humanity's Last Exam with tools (vs 46.8%).
• On the GDPval-AA v2 knowledge-work benchmark it edges past Opus 4.8 (1,618 vs 1,615).

3. Near-Opus performance at a fraction of the price

• Priced at $3/M input and $15/M output - 40% cheaper than Opus 4.8's $5/$25 - with introductory pricing of $2/$10 through Aug 31, 2026 during the rollout.
• Supports low, medium, high, and xhigh effort levels, covering a wider cost-performance range than Sonnet 4.6 and matching Opus 4.8 on some tasks at higher effort.

4. Long-context, long-horizon reliability

• Full 1M token context window with up to 128K output tokens.
• Adaptive thinking automatically decides how long to reason; better self-correction, fewer tool errors, and steadier follow-through across long agent sessions in Claude Code and Cowork.

5. Safer and more honest than its predecessor

• Lower rates of hallucination, sycophancy, and misaligned behavior than Sonnet 4.6 on Anthropic's automated safety audits.
• Deliberately limited cyber capability compared with Opus-class models, shipped with cyber safeguards enabled by default.

6. Updated tokenizer

• Uses a new tokenizer (shared with the Opus 4.7/4.8 line) that can map the same text to up to 1.35x more tokens than Sonnet 4.6 - introductory pricing is set so the switch is roughly cost-neutral.

1. State-of-the-art software engineering

• A notable upgrade over Opus 4.6 on the hardest coding tasks, with users reporting they can hand off work that previously required close supervision.
• Early partners reported double-digit gains on real-world benchmarks - e.g., Cursor saw CursorBench jump from 58% to 70%, and Rakuten-SWE-Bench resolution tripled versus Opus 4.6.
• Handles complex, long-running tasks with rigor: plans carefully, catches its own logical faults, and verifies its outputs before reporting back.

2. Long-horizon agent reliability

• Full 1M token context window at standard pricing, with state-of-the-art long-context consistency.
• Far fewer tool errors, stronger recovery from tool failures, and better follow-through on multi-step workflows - designed for async work like CI/CD, automations, and managing multiple agents in parallel.
• Stronger file-system-based memory, retaining useful notes across long, multi-session runs.

3. Sharper instruction following and honesty

• Takes instructions literally and precisely - existing prompts may need re-tuning since earlier models were more lenient.
• More honest about its own limits: reports missing data instead of fabricating plausible-but-wrong answers, and resists dissonant-data traps that tripped up Opus 4.6.

4. Substantially improved vision and multimodal reasoning

• Accepts images up to 2,576 px on the long edge (~3.75 MP) - over 3x more than prior Claude models.
• Unlocks dense-screenshot computer use, complex diagram extraction, and pixel-perfect reference tasks.
• Stronger document reasoning for enterprise analysis (e.g., 21% fewer errors than Opus 4.6 on Databricks' OfficeQA Pro).

5. Top-tier professional knowledge work

• State-of-the-art on the Finance Agent evaluation and GDPval-AA, with tighter, more professional finance analyses, models, and presentations.
• Strong on legal work - e.g., 90.9% on BigLaw Bench at high effort, with better-calibrated reasoning on review tables and ambiguous edits.
• Noted by design-focused partners as the best model for building dashboards and data-rich interfaces.

6. Modern effort and budget controls

• Introduces a new xhigh effort level between high and max for finer control over reasoning vs. latency.
• Task budgets (public beta) let developers guide token spend across long runs.
• Recommended to start with high or xhigh effort for coding and agentic use cases.