Claude API vs Google Gemini API

Side-by-side comparison of Anthropic Claude API vs Google Gemini API: pricing, context window, Python SDK, tool use, and migration code. With working examples.

Evaluating Claude vs Gemini for your next project? Here's a practical comparison based on what actually matters when building — not benchmark scores.

Quick comparison

Gemini → Claude migration (Python)

# BEFORE (Gemini)
from google import genai
client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Summarize this text in 3 bullet points.",
    config={"system_instruction": "You are a helpful assistant."}
)
print(response.text)

# AFTER (Claude)
import anthropic
client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system="You are a helpful assistant.",   # ← top-level param
    messages=[
        {"role": "user", "content": "Summarize this text in 3 bullet points."}
    ]
)
print(response.content[0].text)

Tool use migration

# BEFORE (Gemini tool use)
from google.genai import types

weather_tool = types.Tool(function_declarations=[
    types.FunctionDeclaration(
        name="get_weather",
        description="Get current weather for a city",
        parameters=types.Schema(
            type="OBJECT",
            properties={"city": types.Schema(type="STRING")},
            required=["city"]
        )
    )
])

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="What is the weather in Paris?",
    config={"tools": [weather_tool]}
)
fn_call = response.candidates[0].content.parts[0].function_call
print(fn_call.name, fn_call.args)

# AFTER (Claude tool use)
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "What is the weather in Paris?"}],
    tools=[{
        "name": "get_weather",
        "description": "Get current weather for a city",
        "input_schema": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"]
        }
    }]
)
for block in response.content:
    if block.type == "tool_use":
        print(block.name, block.input)  # get_weather {"city": "Paris"}

When to choose Claude over Gemini

• Prompt caching: Claude's native caching (available in the standard API, not just Vertex) saves up to 90% on repeated long context — critical for production chatbots and RAG. Gemini's context caching requires Vertex AI.
• Cost predictability: Claude has straightforward per-token pricing with no tier multipliers. Gemini 1.5 Pro has a 2× price jump above 128K tokens per request.
• Document processing: Claude's PDF API and 200K context handle most real-world documents natively. Claude's safety training tends to be more permissive on complex technical document analysis.
• Independence from Google Cloud: Claude runs on Anthropic's API directly, plus AWS Bedrock. No lock-in to Google infrastructure.

When to choose Gemini over Claude

• Audio analysis: Gemini is the only major LLM API with native audio input. Essential for transcription + analysis pipelines.
• 1M token context: If you genuinely need to fit an entire large codebase or multi-hour transcript in one call, Gemini 1.5 Pro is the only option at this scale.
• Google Cloud ecosystem: Deep Vertex AI integration, BigQuery ML, Cloud Run — if your infra is already Google, Gemini is simpler to operate.

Use the Claude API Cost Calculator to estimate costs for your specific token usage. For Claude Python quickstart, see the Python quickstart guide. For OpenAI comparison, see Claude API vs OpenAI.

Frequently asked questions

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