Gemini nano Banana 2.1

Google AI image generation

Gemini nano Banana 2.1 is a Google AI image generation model.It supports a 131,072-token context windowwith up to 32,768 output tokens.Input is priced at $1.50/M tokens and output at $7.50/M tokens. Capabilities include vision, reasoning. Route Gemini nano Banana 2.1 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Gemini nano Banana 2.1 spend

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

~3K in / ~400 out · 5K req/day
3,000
0131,072
400
032,768
5,000
01,000,000
Per request
$0.007500
in $0.004500 · out $0.003000
Per day
$37.50
5,000 requests
Per month
$1,141
152,188 requests

Estimate uses $1.50/M input · $7.50/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 Oct 22, 2026.

Input $1.50/M
Output $7.50/M
Blended (3:1 in:out) $3.00/M
Cross-model comparable price
Batch input $0.750/M
Batch output $3.75/M
Per image $0.0336

Context & limits

Context window
131,072 tokens
Max input
131,072 tokens
Max output
32,768 tokens
Modalities
vision, pdf, image, text

Capabilities

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

Strengths & caveats — when to pick Gemini nano Banana 2.1

Data-driven from Gemini nano Banana 2.1's percentile within image generation peers.

Where it's strong

  • +long-context work — top-3 largest context window across 51 image generation models
  • +long-form generation — 32,768-token max output, top-11% of peers
  • +native reasoning — only 1% of image generation models on Future AGI advertise this
  • +PDF input — only 7% of image generation models on Future AGI advertise this

Watch out for

  • No major caveats flagged from public spec.

Benchmarks pending

We haven't logged public benchmark scores for Gemini nano Banana 2.1 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Gemini nano Banana 2.1 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 nano Banana 2.1 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-nano-banana-2-1",
    messages=[{"role": "user", "content": "Hello, Gemini nano Banana 2.1!"}],
)

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-nano-banana-2-1",
    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-nano-banana-2-1 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google Vertex AI$1.50/M$7.50/MOct 22, 2026

Explore similar models

Gemini nano Banana 2.1 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Gemini nano Banana 2.1 cost?

Input is priced at $1.50 per 1M tokens and output at $7.50 per 1M tokens (Google AI, last verified Oct 22, 2026).

What is the context window of Gemini nano Banana 2.1?

Gemini nano Banana 2.1 supports a 131,072-token context window with up to 32,768 output tokens.

Does Gemini nano Banana 2.1 support function calling?

Gemini nano Banana 2.1 does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.

Is Gemini nano Banana 2.1 good for production?

Gemini nano Banana 2.1 is well-suited for long-context work — top-3 largest context window across 51 image generation models and long-form generation — 32,768-token max output, top-11% of peers.

How can I route to Gemini nano Banana 2.1 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 nano Banana 2.1

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