Gemini 2.5 Pro
Google AI chatGemini 2.5 Pro is a Google AI chat model.It supports a 1,048,576-token context windowwith up to 65,535 output tokens.Input is priced at $1.25/M tokens and output at $10.00/M tokens. Capabilities include function calling, vision, reasoning, audio input. Route Gemini 2.5 Pro via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Gemini 2.5 Pro spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $1.25/M input · $10.00/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Jun 2, 2026.
| Input | $1.25/M | |
| Output | $10.00/M | |
| Blended (3:1 in:out) | $3.44/M Cross-model comparable price | |
| Cached input | $0.125/M |
Context & limits
- Context window
- 1,048,576 tokens
- Max input
- 1,048,576 tokens
- Max output
- 65,535 tokens
- Modalities
- vision, audio_in, pdf, video, text
Capabilities
- Function calling ✓ supported
- Parallel tool calls — not advertised
- Vision input ✓ supported
- Audio input ✓ supported
- Audio output — not advertised
- PDF input ✓ supported
- Streaming ✓ supported
- Structured output ✓ supported
- Prompt caching ✓ supported
- Reasoning ✓ supported
Strengths & caveats — when to pick Gemini 2.5 Pro
Data-driven from Gemini 2.5 Pro's percentile within chat peers.
Where it's strong
- +pricing — cheaper than 79% of priced chat models on Future AGI
- +long-context tasks — context window in the top 2% of peers
- +audio input — only 4% of chat models on Future AGI advertise this
- +PDF input — only 16% of chat models on Future AGI advertise this
- +prompt caching — only 24% of chat models on Future AGI advertise this
Watch out for
- No major caveats flagged from public spec.
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call Gemini 2.5 Pro via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Gemini 2.5 Pro via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="google/gemini-2-5-pro",
messages=[{"role": "user", "content": "Hello, Gemini 2.5 Pro!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="google/gemini-2-5-pro",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: gemini-2-5-pro
provider: google
weight: 80
fallbacks:
- model: grok-3
provider: azure-ai-foundry
- model: gpt-5-mini
provider: openai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }Same model on other providers
gemini-2-5-pro is also available via 2 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Google Vertex AI | $1.25/M | $10.00/M | Jun 2, 2026 |
| GitHub Copilot | — | — | Jun 2, 2026 |
Compare with similar models
Grouped by Chatbot Arena tier (Gemini 2.5 Pro sits at 1380 ELO).
≥30 ELO higher
- Claude Opus 4.6Anthropic · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.6 (2026-02-05)Anthropic · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Gemini 3.1 Pro previewGoogle Vertex AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
- Claude Opus 4.7Anthropic · $5.00/M in · $25.00/M out · 1,000,000 ctx
25–100 ELO lower, ≤50% of price
FAQ
How much does Gemini 2.5 Pro cost?
Input is priced at $1.25 per 1M tokens and output at $10.00 per 1M tokens (Google AI, last verified Jun 2, 2026).
What is the context window of Gemini 2.5 Pro?
Gemini 2.5 Pro supports a 1,048,576-token context window with up to 65,535 output tokens.
Does Gemini 2.5 Pro support function calling?
Yes — Gemini 2.5 Pro supports function (tool) calling.
Is Gemini 2.5 Pro good for production?
Gemini 2.5 Pro is well-suited for pricing — cheaper than 79% of priced chat models on Future AGI and long-context tasks — context window in the top 2% of peers.
How can I route to Gemini 2.5 Pro with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Gemini 2.5 Pro
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Start here — vendor docs are the only authoritative spec.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.