Gemini 1.5 Pro vs Claude 4.7 Opus

1. Breakthrough long-context window up to 1,000,000 tokens

• Can process 1 hour of video, 11 hours of audio, 700k+ words, or 100k+ lines of code in a single prompt.
• Supports advanced retrieval, reasoning, summarization, and cross-document tasks.
• Achieves 99% retrieval accuracy on 1M-token Needle-In-A-Haystack tests.

2. Strong multimodal reasoning across video, audio, images, and text

• Can analyze long videos (e.g., full silent films), track events, infer causality, and identify small details.
• Handles large complex documents like manuals, transcripts, and books.

3. High-performance reasoning and problem solving

• Comparable to Gemini 1.0 Ultra across many benchmarks.
• Excels at code reasoning, multi-step explanations, and large-scale codebase analysis.

4. Advanced code understanding and generation

• Performs problem-solving on codebases exceeding 100,000 lines.
• Capable of cross-file reasoning, debugging guidance, API comprehension, and generating structured code improvements.

5. Efficient Mixture-of-Experts (MoE) architecture

• Activates only relevant expert pathways per input.
• Enables faster training, lower latency, and more efficient serving.
• Dramatically improves scalability and inference speed.

6. Exceptional in-context learning capabilities

• Learns new tasks directly from long prompts without fine-tuning.
• Demonstrated by learning to translate a low-resource language (Kalamang) from a grammar manual.

7. High-fidelity multimodal understanding

• Reads, analyzes, and reasons about long PDFs, code repositories, images, and videos together.
• Enables new classes of applications: legal analysis, scientific review, codebase audits, long-form content generation, etc.

8. Safety and reliability first

• Undergoes extensive ethics, safety testing, and red-teaming.
• Improved representational safety and reduced hallucinations compared to previous generations.

9. Available for developers and enterprises

• Accessible via AI Studio and Vertex AI.
• Supports future pricing tiers for expanded context windows.
• Designed for real enterprise-scale workloads.

10. Widely capable mid-size model

• Positioned between Gemini Pro and Gemini Ultra generations.
• Well-balanced: reasoning, multimodality, long-context, and speed.

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.