Gemini 1.0 Pro vs Claude 4.1 Opus

Compare Gemini 1.0 Pro and Claude 4.1 Opus. Find out which one is better for your use case.

Model Comparison

FeatureGemini 1.0 ProClaude 4.1 Opus
ProviderGoogleAnthropic
Model Typetexttext
Context Window128,000 tokens1,000,000 tokens
Input Cost$0.50 / 1M tokens$15.00 / 1M tokens
Output Cost$1.50 / 1M tokens$75.00 / 1M tokens

Strengths & Best Use Cases

Gemini 1.0 Pro

1. Strong all-purpose performance

  • Designed as Google's balanced middle-tier model.
  • Handles a wide range of tasks: reasoning, writing, coding, and problem-solving.

2. Natively multimodal understanding

  • Trained from the ground up on text, images, audio, and video.
  • More consistent multimodal reasoning than stitched-together architectures.

3. Great cost-to-capability ratio

  • Offers much of Gemini Ultra's reasoning quality at a fraction of the cost.
  • Strong default choice for large-scale production workloads.

4. Reliable reasoning and factual performance

  • Performs well on benchmarks like MMLU, MMMU, and code reasoning.
  • Handles long-form analysis, multi-step reasoning, and structured problem solving.

5. Advanced coding capabilities

  • Supports major languages such as Python, Java, C++, Go.
  • Generates, edits, debugs, and explains code with high accuracy.
  • Powers advanced coding systems like AlphaCode 2.

6. Efficient and scalable

  • Optimized for Google TPUs for lower latency and faster inference.
  • Suitable for batch workloads, agents, and complex multi-step pipelines.

7. Strong multimodal reasoning

  • Understands math, physics, and scientific diagrams.
  • Handles mixed data inputs (charts + text, screenshots + instructions, etc.).

8. Enterprise-ready reliability

  • Available through Google AI Studio and Vertex AI.
  • Benefits from enterprise-grade governance, safety, privacy, and compliance.

Claude 4.1 Opus

1. Advanced Coding Performance

  • Achieves 74.5% on SWE-bench Verified, improving the Claude family's state-of-the-art coding abilities.

  • Stronger at:

    • Multi-file code refactoring
    • Large codebase debugging
    • Pinpointing exact corrections without unnecessary edits
  • Outperforms Opus 4 and shows gains comparable to jumps seen in past major releases.

2. Improved Agentic & Research Capabilities

  • Better at maintaining detail accuracy in long research tasks.
  • Enhanced agentic search and step-by-step problem solving.
  • Performs reliably across complex multi-turn reasoning tasks.

3. Validated by Real-World Users

  • GitHub: Better multi-file refactoring and code adjustments.
  • Rakuten Group: High precision debugging with minimal collateral changes.
  • Windsurf: One standard deviation improvement on their junior dev benchmark—similar magnitude to Sonnet 3.7 → Sonnet 4.

4. Hybrid-Reasoning Benchmark Improvements

  • Improvements across TAU-bench, GPQA Diamond, MMMLU, MMMU, AIME (with extended thinking).
  • Stronger robustness in long-context reasoning tasks.

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