Gemini 3.1 Flash Lite preview

Google Vertex AI chat

Gemini 3.1 Flash Lite 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 $0.250/M tokens and output at $1.50/M tokens. Capabilities include function calling, vision, reasoning, audio input. Route Gemini 3.1 Flash Lite 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: Aug 6, 2026 View source ↗
Cost calculator

Estimate Gemini 3.1 Flash Lite 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 @ $0.0250/M
Per request
$0.001350
in $0.000750 · out $0.000600
Per day
$6.75
5,000 requests
Per month
$205
152,188 requests

Estimate uses $0.2500/M input · $1.50/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 Aug 6, 2026.

Input $0.250/M
Output $1.50/M
Blended (3:1 in:out) $0.563/M
Cross-model comparable price
Cached input $0.0250/M

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 ✓ supported
  • 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 Flash Lite preview

Data-driven from Gemini 3.1 Flash Lite preview's percentile within chat peers.

Where it's strong

  • +long-context tasks — context window in the top 3% of peers
  • +audio input — only 3% of chat models on Future AGI advertise this
  • +PDF input — only 19% of chat models on Future AGI advertise this
  • +parallel tool calls — only 21% 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.

Chatbot Arena ELOgeneral· overall
Captured Aug 8, 2026
Try it

Call Gemini 3.1 Flash Lite 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 Flash Lite 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-flash-lite-preview",
    messages=[{"role": "user", "content": "Hello, Gemini 3.1 Flash Lite 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-flash-lite-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-flash-lite-preview
    provider: vertex-ai
    weight: 80
fallbacks:
  - model: gemini-3-1-pro-preview
    provider: google
  - model: claude-opus-5
    provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Same model on other providers

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

ProviderInput / 1MOutput / 1MVerified
Google AI$0.250/M$1.50/MAug 6, 2026

Compare with similar models

Grouped by Chatbot Arena tier (Gemini 3.1 Flash Lite preview sits at 1432 ELO).

FAQ

How much does Gemini 3.1 Flash Lite preview cost?

Input is priced at $0.250 per 1M tokens and output at $1.50 per 1M tokens (Google Vertex AI, last verified Aug 6, 2026).

What is the context window of Gemini 3.1 Flash Lite preview?

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

Does Gemini 3.1 Flash Lite preview support function calling?

Yes — Gemini 3.1 Flash Lite preview supports function (tool) calling, including parallel tool calls.

Is Gemini 3.1 Flash Lite preview good for production?

Gemini 3.1 Flash Lite preview is well-suited for long-context tasks — context window in the top 3% of peers and audio input — only 3% of chat models on Future AGI advertise this.

How can I route to Gemini 3.1 Flash Lite 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 Flash Lite preview

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