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LLM ComparisonGPT-5Gemini 1.5 Pro

GPT-5 vs Gemini 1.5 Pro

Compare GPT-5 and Gemini 1.5 Pro. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5Gemini 1.5 Pro
ProviderOpenAIGoogle
Model Typetexttext
Context Window400,000 tokens1,000,000 tokens
Input Cost
$1.25/ 1M tokens
$3.50/ 1M tokens
Output Cost
$10.00/ 1M tokens
$7.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5

OpenAI

1. High reasoning capability

  • Designed for intelligent reasoning across complex domains.
  • Supports reasoning tokens and adjustable reasoning effort.

2. Strong coding and agentic performance

  • Optimized for multi-step coding tasks, tool-use chains, and agent workflows.
  • Handles complex logic, planning, and structured problem solving reliably.

3. Multimodal input

  • Accepts text + image as input.
  • Produces text outputs with strong instruction following.

4. Extensive tool support

  • Works with Web Search, File Search, Image Generation (as a tool), Code Interpreter, MCP, and more.
  • Integrated across Chat Completions, Responses API, Realtime, Assistants, Batch, Embeddings, etc.

Gemini 1.5 Pro

Google

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.

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