Gemini Pro

Google AI chat

Gemini Pro is a Google AI chat model.It supports a 32,760-token context windowwith up to 8,192 output tokens.Input is priced at $0.350/M tokens and output at $1.05/M tokens. Capabilities include function calling. Route Gemini Pro via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 7, 2026 View source ↗
Cost calculator

Estimate Gemini Pro spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
032,760
400
08,192
5,000
01,000,000
Per request
$0.001470
in $0.001050 · out $0.000420
Per day
$7.35
5,000 requests
Per month
$224
152,188 requests

Estimate uses $0.3500/M input · $1.05/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 May 7, 2026.

Input $0.350/M
Output $1.05/M
Blended (3:1 in:out) $0.525/M
Cross-model comparable price

Context & limits

Context window
32,760 tokens
Max input
32,760 tokens
Max output
8,192 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick Gemini Pro

Data-driven from Gemini Pro's percentile within chat peers.

Where it's strong

  • +agentic workflows that depend on reliable tool calls

Watch out for

  • !limited context — 32,760-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmark scores

Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.

Captured Aug 8, 2026
Try it

Call Gemini 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.

SDK
Native Future AGI client (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 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-pro",
    messages=[{"role": "user", "content": "Hello, Gemini 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-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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Same model on other providers

gemini-pro is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google Vertex AI$0.500/M$1.50/MMay 7, 2026

Compare with similar models

Gemini Pro doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Gemini Pro cost?

Input is priced at $0.350 per 1M tokens and output at $1.05 per 1M tokens (Google AI, last verified May 7, 2026).

What is the context window of Gemini Pro?

Gemini Pro supports a 32,760-token context window with up to 8,192 output tokens.

Does Gemini Pro support function calling?

Yes — Gemini Pro supports function (tool) calling.

Is Gemini Pro good for production?

Gemini Pro is well-suited for agentic workflows that depend on reliable tool calls. Consider alternatives if you need limited context — 32,760-token window is in the bottom quartile; not ideal for long documents or large RAG.

How can I route to Gemini 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 Pro

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.