Gemini 2.5 Flash vs Claude 3.5 Haiku

Compare Gemini 2.5 Flash and Claude 3.5 Haiku. Find out which one is better for your use case.

Model Comparison

FeatureGemini 2.5 FlashClaude 3.5 Haiku
ProviderGoogleAnthropic
Model Typetexttext
Context Window1,000,000 tokens200,000 tokens
Input Cost$0.30 / 1M tokens$0.80 / 1M tokens
Output Cost$2.50 / 1M tokens$4.00 / 1M tokens

Strengths & Best Use Cases

Gemini 2.5 Flash

1. Highly cost-efficient for large-scale workloads

  • Extremely low input cost ($0.30/M) and affordable output cost.
  • Built for production environments where throughput and budget matter.
  • Significantly cheaper than competitors like o4-mini, Claude Sonnet, and Grok on text workloads.

2. Fast performance optimized for everyday tasks

  • Ideal for summarization, chat, extraction, classification, captioning, and lightweight reasoning.
  • Designed as a high-speed “workhorse model” for apps that require low latency.

3. Built-in “thinking budget” control

  • Adjustable reasoning depth lets developers trade off latency vs. accuracy.
  • Enables dynamic cost management for large agent systems.

4. Native multimodality across all major formats

  • Inputs: text, images, video, audio, PDFs.
  • Outputs: text + native audio synthesis (24 languages with the same voice).
  • Great for conversational agents, voice interfaces, multimodal analysis, and captioning.

5. Industry-leading long context window

  • 1,000,000 token context window.
  • Supports long documents, multi-file processing, large datasets, and long multimedia sequences.
  • Stronger MRCR long-context performance vs previous Flash models.

6. Native audio generation and multilingual conversation

  • High-quality, expressive audio output with natural prosody.
  • Style control for tones, accents, and emotional delivery.
  • Noise-aware speech understanding for real-world conditions.

7. Strong benchmark performance for its cost

  • 11% on Humanity’s Last Exam (no tools) — competitive with Grok and Claude.
  • 82.8% on GPQA diamond (science reasoning).
  • 72.0% on AIME 2025 single-attempt math.
  • Excellent multimodal reasoning (79.7% on MMMU).
  • Leading long-context performance in its price tier.

8. Capable coding assistance

  • 63.9% on LiveCodeBench (single attempt).
  • 61.9%/56.7% on Aider Polyglot (whole/diff).
  • Agentic coding support + tool use + function calling.

9. Fully supports tool integration

  • Function calling.
  • Structured outputs.
  • Search-as-a-tool.
  • Code execution (via Google Antigravity / Gemini API environments).

10. Production-ready availability

  • Available in: Gemini App, Google AI Studio, Gemini API, Vertex AI, Live API.
  • General availability (GA) with stable endpoints and documentation.

Claude 3.5 Haiku

1. Intelligence & Benchmark Performance

  • Matches Claude 3 Opus (previous largest model) on many intelligence tasks.
  • Surpasses Claude 3 Opus on multiple evaluations despite being a smaller, faster model.
  • Major improvements across every skill category vs previous Haiku.

2. Coding Strength

  • Scores 40.6% on SWE-bench Verified, outperforming:

    • Claude 3.5 Sonnet (original version)
    • GPT-4o
    • Many agent-driven systems
  • Excellent for engineering assistants, agent coding tasks, and bug fixing.

3. Speed & Latency

  • Same speed class as Claude 3 Haiku (ultra-fast).
  • Ideal for real-time interactions, high request volumes, and UI responsiveness.

4. Tool Use & Instruction Following

  • Better at following instructions than previous Haiku.
  • Stronger at tool use accuracy, making it reliable for agents and workflows.

5. Best Use Cases

  • High-volume, low-latency tasks
  • User-facing products
  • Sub-agent tasks in larger workflows
  • Processing large structured datasets (pricing, inventory, purchase history)
  • Rapid content or code generation where speed matters

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