Gemini 2.0 Flash Lite
Google Vertex AI chat Deprecated 156d agomodel fallback routing to swap models without code changes.
Gemini 2.0 Flash Lite is a Google Vertex AI chat model.Input is priced at $0.0750/M tokens and output at $0.300/M tokens. Route Gemini 2.0 Flash Lite via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Gemini 2.0 Flash Lite spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.0750/M input · $0.3000/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 | $0.0750/M | |
| Output | $0.300/M | |
| Blended (3:1 in:out) | $0.131/M Cross-model comparable price | |
| Batch input | $0.0375/M | |
| Batch output | $0.150/M |
Context & limits
- Context window
- —
- Max input
- —
- Max output
- —
- Modalities
- text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick Gemini 2.0 Flash Lite
Data-driven from Gemini 2.0 Flash Lite's percentile within chat peers.
Where it's strong
Watch out for
- !high cost — input + output rates are in the top 89% of priced chat peers; consider a cheaper sibling for high-volume workloads
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !strict structured output — no JSON-schema enforcement, expect retry loops
- !already deprecated — provider stopped accepting new traffic 156 days ago
Benchmark scores
Reported public benchmark numbers. Each row links to the source.
Call Gemini 2.0 Flash Lite 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 2.0 Flash Lite 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-2-0-flash-lite",
messages=[{"role": "user", "content": "Hello, Gemini 2.0 Flash Lite!"}],
)
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-2-0-flash-lite",
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-2-0-flash-lite
provider: vertex-ai
weight: 80
fallbacks:
- model: grok-4
provider: azure-ai-foundry
- model: grok-3
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Same model on other providers
gemini-2-0-flash-lite 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 | $0.0750/M | $0.300/M | Aug 6, 2026 |
Explore similar models
Grouped by Chatbot Arena tier (Gemini 2.0 Flash Lite sits at 1354 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
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≥30 ELO higher
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FAQ
How much does Gemini 2.0 Flash Lite cost?
Input is priced at $0.0750 per 1M tokens and output at $0.300 per 1M tokens (Google Vertex AI, last verified Oct 22, 2026).
What is the context window of Gemini 2.0 Flash Lite?
Context window for Gemini 2.0 Flash Lite is not currently public.
Does Gemini 2.0 Flash Lite support function calling?
Gemini 2.0 Flash Lite 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 2.0 Flash Lite good for production?
Gemini 2.0 Flash Lite is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Gemini 2.0 Flash Lite 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 Lite
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.