Claude 4.7 Opus vs Grok 4

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.

1. Flagship-level reasoning and math performance

• Designed for world-class reasoning depth, precision, and multi-step logical chains.
• Excels at STEM, mathematics, symbolic operations, proofs, and analytical workloads.

2. Powerful multimodal understanding

• Supports text, images, and other modalities.
• Handles cross-modal reasoning tasks requiring context synthesis.

3. Extreme capability across diverse tasks

• Positioned as a top-tier 'jack of all trades' model.
• Strong in natural language, coding, knowledge retrieval, and structured generation.

4. Large 256K context window

• Enables analysis of long documents, entire codebases, multi-document packs, and extensive agent sessions.
• Supports workloads that require persistent reasoning across large inputs.

5. Advanced developer tooling support

• Function calling for tool-augmented workflows.
• Structured outputs for predictable, schema-controlled generation.
• Integrates smoothly with agents and complex automation pipelines.

6. Efficient caching for cost reduction

• Cached input tokens discounted to $0.75 / 1M tokens.
• Encourages RAG, retrieval pipelines, and multi-step conversational workflows.

7. Production-ready performance

• Stable rate limits: 480 requests per minute.
• High token throughput: 2,000,000 tokens per minute.
• Available across multiple xAI regional clusters.

8. Optional Live Search augmentation

• Add-on: $25 per 1K sources.
• Enhances factual accuracy and real-time information retrieval.