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

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

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.

Captured May 12, 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.