Gemini 3.1 Pro preview

Google Vertex AI chat

Gemini 3.1 Pro preview is a Google Vertex AI chat model.It supports a 1,048,576-token context windowwith up to 65,536 output tokens.Input is priced at $2.00/M tokens and output at $12.00/M tokens. Capabilities include function calling, vision, reasoning, audio input. Route Gemini 3.1 Pro preview 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 12, 2026 View source ↗
Cost calculator

Estimate Gemini 3.1 Pro preview spend

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

~3K in / ~400 out · 5K req/day
3,000
01,048,576
400
065,536
5,000
01,000,000
cached @ $0.2000/M
Per request
$0.0108
in $0.006000 · out $0.004800
Per day
$54.00
5,000 requests
Per month
$1,644
152,188 requests

Estimate uses $2.00/M input · $12.00/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 May 12, 2026.

Input $2.00/M
Output $12.00/M
Cached input $0.200/M
Batch input $1.00/M
Batch output $6.00/M
Per image $0.000120

Limits

Context window
1,048,576 tokens
Max input
1,048,576 tokens
Max output
65,536 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

Where it's strong

  • +pricing — cheaper than 83% 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 23% 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.

Chatbot Arena ELOgeneral· overall↑18% vs peers
Captured May 12, 2026
GPQA Diamondreasoning· Thinking (High); No tools
Captured May 12, 2026
SWE-bench Verifiedagent· Thinking (High); Single attempt
Captured May 12, 2026
MMMU-Promultimodal· Thinking (High); No tools
Captured May 12, 2026
ARC-AGI-2reasoning· verified
Captured May 12, 2026
SciCodecode· Thinking (High)
Captured May 12, 2026
Humanity's Last Examreasoning· Thinking (High); No tools
Captured May 12, 2026
Try it

Call Gemini 3.1 Pro preview 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 3.1 Pro preview 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="vertex-ai/gemini-3-1-pro-preview",
    messages=[{"role": "user", "content": "Hello, Gemini 3.1 Pro preview!"}],
)

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="vertex-ai/gemini-3-1-pro-preview",
    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 ↗
Advanced: fallback + cache config (YAML)
strategy: cost-optimized
targets:
  - model: gemini-3-1-pro-preview
    provider: vertex-ai
    weight: 80
fallbacks:
  - model: claude-opus-4-6
    provider: azure-ai-foundry
  - model: claude-opus-4-6-20260205
    provider: anthropic
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Same model on other providers

gemini-3-1-pro-preview is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google AI$2.00/M$12.00/MMay 12, 2026

Compare with similar models

Grouped by Chatbot Arena tier (Gemini 3.1 Pro preview sits at 1492 ELO).

FAQ

How much does Gemini 3.1 Pro preview cost?

Input is priced at $2.00 per 1M tokens and output at $12.00 per 1M tokens (Google Vertex AI, last verified May 12, 2026).

What is the context window of Gemini 3.1 Pro preview?

Gemini 3.1 Pro preview supports a 1,048,576-token context window with up to 65,536 output tokens.

Does Gemini 3.1 Pro preview support function calling?

Yes — Gemini 3.1 Pro preview supports function (tool) calling.

Is Gemini 3.1 Pro preview good for production?

Gemini 3.1 Pro preview is well-suited for pricing — cheaper than 83% 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 3.1 Pro preview 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 3.1 Pro preview

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