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Google: gemini-4-carbon

Gemini 4 Carbon is a Google Gemini chat model served through TokenLab. It accepts text and image input and returns text, with tool use, reasoning, JSON mode and implicit prompt caching, and it does not enforce a structured-output JSON schema. Context window is 1048576 input tokens and 65536 output tokens.
Compare models
gemini-4-carbon
AvailableGoogleChatPreview
Input / Output
$2.00 / $10.00
Context
1M
Max output
64K
Modalities
Chat

Getting started

  1. Create API key

    Create a key in Console, then use it with every model on the platform.

  2. Send your first request

    Copy the example for your language and run it against the endpoint.

    Text to textGemini API
    POST/v1beta/models/gemini-4-carbon:generateContent
    API format:
    curl "https://api.tokenlab.sh/v1beta/models/gemini-4-carbon:generateContent" \
      -H "Content-Type: application/json" \
      -H "x-goog-api-key: sk-xxx" \
      -d '{
        "contents": [
          {"role": "user", "parts": [{"text": "Hello!"}]}
        ]
      }'

Pricing

The Verified price applies to Verified, which costs less on most models. The Official price is the model maker's published price and applies to the more reliable Official route. Auto bills the route that completes the request.

Default spec

per 1M tokens
Official price
Input $2.00 / Output $10.00 / Cache read $0.10
Verified price
—
Discount
—
Prompt cache pricing

Cache read

Official price
$0.10
Verified price
—
Discount
—

Usage & activity

Success rate is the share of requests that completed. Latency is how long a full response takes; P95 means 95% of requests finished within that time.

Usage & availability

Last 24 hours
Requests
Success rate
P95 latency
Total tokens
Model performance
Metrics appear once privacy and data volume thresholds are met.

Data is based on aggregate user requests, excluding status checks.

Open in Console

Open gemini-4-carbon in Console with a prompt ready to edit or send.

Help me try gemini-4-carbon with a short message at /v1beta/models/gemini-4-carbon:generateContent. Show the reply, latency, and cost.

Use cases

  • Agents and tools

    Handle reasoning, support triage, tool calls, and multi-step tasks.

  • Coding

    Generate, review, or debug code in the tools you already use.

  • Knowledge assistants

    Build chat, search, and retrieval with a clear price and capability profile.

  • Side-by-side test

    Compare response quality, latency, and price side by side.

Prompt examples

Write a concise support reply and list the assumptions behind it.

Review this API design and call out the top three integration risks.

Turn a long changelog into release notes a non-engineer would read.

This model has conditional pricing. Monthly token totals alone cannot produce a reliable estimate; use the detailed pricing for the request specification, cache, and applicable time window.

FAQ

How much does gemini-4-carbon cost?

On TokenLab, gemini-4-carbon costs Input $2.00 / Output $10.00 per 1M tokens. The pricing table above shows the full breakdown. Rates depend on the billing unit, specification, and usage. Compare matching conditions in the model's detailed pricing; a single rate does not determine the total cost.

What are the context window and output limit of gemini-4-carbon?

gemini-4-carbon accepts up to 1,048,576 tokens of context and returns up to 65,536 tokens in one response.

How do I test gemini-4-carbon?

Open gemini-4-carbon in Create. A sample for /v1beta/models/gemini-4-carbon:generateContent will be ready to try.

Which endpoint should gemini-4-carbon use?

Use https://api.tokenlab.sh/v1beta/models/gemini-4-carbon:generateContent for gemini-4-carbon. The request example below shows the matching code shape.

Can I test gemini-4-carbon before integrating it?

Yes. Open Console starts a ready draft for gemini-4-carbon and keeps your prompt after sign-in, so you don’t lose context.

Which operations does gemini-4-carbon support?

gemini-4-carbon supports Text to text. Select an operation above to see its endpoint and request example.

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