2M Token Context Recall
Analyze 50+ contracts or massive codebases with near-perfect retrieval accuracy across 2 million tokens.
curl --request POST "https://gptproto.com/v1/chat/completions" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "gemini-2.5-pro",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'Chat, agen coding & pekerjaan dokumen. Harga per 1 juta token — input, input yang di-cache, dan output ditagih secara terpisah. GPTProto 40% lebih murah daripada tarif resmi.
| Skenario | Daftar Google | OpenRouter | GPTProto | Penghematan Anda / bln |
|---|---|---|---|---|
| Pribadi10M token / bln (4.8M dalam cache) | $24.60 | $25.95 | $14.76 | −$9.84≈ $118.08 / thn |
| Tim100M token / bln (48M dalam cache) | $246.00 | $259.53 | $147.60 | −$98.40≈ $1180.80 / thn |
| Bisnis500M token / bln (240M dalam cache) | $1230.00 | $1297.65 | $738.00 | −$492.00≈ $5904.00 / thn |
Unlock advanced intelligence with the Gemini 2.5 Pro API. Explore the standout capabilities that differentiate this model from standard LLMs.
2M Token Context Recall
Analyze 50+ contracts or massive codebases with near-perfect retrieval accuracy across 2 million tokens.
Native Multimodal Reasoning
Interleave video, audio, and text in one prompt for deeper cross-modal understanding and analysis.
Agentic Tool Use
Optimized for complex tool chaining and multi-step workflows with 15% better performance than Gemini 1.5.
Efficient Context Caching
Drastically reduce costs by caching frequently used data, perfect for large technical documentation sets.
Find answers to common questions about implementing the Gemini 2.5 Pro API into your tech stack, including pricing, context window capabilities, and multimodal data privacy standards.
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