Gemini 3.1 Pro preview
Google Vertex AI chatGemini 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.
Estimate Gemini 3.1 Pro preview spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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 Jun 2, 2026.
| Input | $2.00/M | |
| Output | $12.00/M | |
| Blended (3:1 in:out) | $4.50/M Cross-model comparable price | |
| Cached input | $0.200/M | |
| Batch input | $1.00/M | |
| Batch output | $6.00/M | |
| Per image | $0.000120 |
Context & 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
Strengths & caveats — when to pick Gemini 3.1 Pro preview
Data-driven from Gemini 3.1 Pro preview's percentile within chat peers.
Where it's strong
- +pricing — cheaper than 82% 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 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.
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"))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: anthropic
- 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.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Google AI | $2.00/M | $12.00/M | Jun 2, 2026 |
Compare with similar models
Grouped by Chatbot Arena tier (Gemini 3.1 Pro preview sits at 1492 ELO).
- Claude Opus 4.7Google Vertex AI · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.6Google Vertex AI · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Gemini 3 Pro PreviewGoogle Vertex AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
- Claude Sonnet 4.6Google Vertex AI · $3.00/M in · $15.00/M out · 1,000,000 ctx
25–100 ELO lower, ≤50% of price
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 Jun 2, 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 82% 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.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.