Gemini 2.0 Flash preview Image Generation

Google AI chat Deprecated 180d ago
Heads-up: Google AI has scheduled Gemini 2.0 Flash preview Image Generation for deprecation on Nov 14, 2025. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Gemini 2.0 Flash preview Image Generation is a Google AI chat model.It supports a 1,048,576-token context windowwith up to 8,192 output tokens.Input is priced at $0.1000/M tokens and output at $0.400/M tokens. Capabilities include function calling, vision, audio input, prompt caching. Route Gemini 2.0 Flash preview Image Generation 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 2.0 Flash preview Image Generation 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
08,192
5,000
01,000,000
cached @ $0.0250/M
Per request
$0.000460
in $0.000300 · out $0.000160
Per day
$2.30
5,000 requests
Per month
$70.01
152,188 requests

Estimate uses $0.1000/M input · $0.4000/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 $0.1000/M
Output $0.400/M
Cached input $0.0250/M

Limits

Context window
1,048,576 tokens
Max input
1,048,576 tokens
Max output
8,192 tokens
Modalities
vision, audio_in, audio_out, text

Capabilities

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

Where it's strong

  • +long-context tasks — context window in the top 2% of peers
  • +audio output — only 3% of chat models on Future AGI advertise this
  • +audio input — only 4% of chat models on Future AGI advertise this
  • +prompt caching — only 23% of chat models on Future AGI advertise this

Watch out for

  • !already deprecated — provider stopped accepting new traffic 180 days ago

Benchmarks pending

We haven't logged public benchmark scores for Gemini 2.0 Flash preview Image Generation yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Gemini 2.0 Flash preview Image Generation 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 2.0 Flash preview Image Generation 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-2-0-flash-preview-image-generation",
    messages=[{"role": "user", "content": "Hello, Gemini 2.0 Flash preview Image Generation!"}],
)

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-2-0-flash-preview-image-generation",
    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-2-0-flash-preview-image-generation is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google Vertex AI$0.1000/M$0.400/MMay 7, 2026

Compare with similar models

Gemini 2.0 Flash preview Image Generation 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 2.0 Flash preview Image Generation cost?

Input is priced at $0.1000 per 1M tokens and output at $0.400 per 1M tokens (Google AI, last verified May 7, 2026).

What is the context window of Gemini 2.0 Flash preview Image Generation?

Gemini 2.0 Flash preview Image Generation supports a 1,048,576-token context window with up to 8,192 output tokens.

Does Gemini 2.0 Flash preview Image Generation support function calling?

Yes — Gemini 2.0 Flash preview Image Generation supports function (tool) calling.

Is Gemini 2.0 Flash preview Image Generation good for production?

Gemini 2.0 Flash preview Image Generation is well-suited for long-context tasks — context window in the top 2% of peers and audio output — only 3% of chat models on Future AGI advertise this. Consider alternatives if you need already deprecated — provider stopped accepting new traffic 180 days ago.

How can I route to Gemini 2.0 Flash preview Image Generation 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 2.0 Flash preview Image Generation

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