Google

Gemini 2.0 Flash Lite

Gemini 2.0 Flash Lite is a multimodal text generation model developed by Google, released in early 2025 as part of the Gemini 2.0 model family. It is designed specifically for high-volume, cost-sensitive applications, offering a balance between response speed and output quality. The model supports a context window of over one million tokens (1,048,576), making it suitable for processing long documents or extended conversations in a single request. Gemini 2.0 Flash Lite is best suited for developers and organizations that need to run large numbers of inference requests without incurring high costs. Its architecture prioritizes throughput and efficiency, making it a practical choice for tasks like summarization, classification, translation, and content generation at scale. The model's training data has a cutoff of June 2024, and it is accessible through Google's Vertex AI platform.

Feb 25, 2025 1,048,576 context 8,192 tokens output

Large Context Window Fast Inference Cost-Effective Scaling Multimodal Input Text Generation Structured Output

Overview ↓ About ↓ Capabilities ↓ Pricing ↓ Price Comparison ↓ Providers ↓ Benchmarks ↓ Resources ↓ Community ↓ FAQ ↓

Model Overview

High-signal model metadata in a structured two-column overview table.

Provider

The entity that provides this model.

Google

Model ID

The routed model identifier exposed by upstream providers.

google/gemini-2.0-flash-lite-001

Input Context Window

The number of tokens supported by the input context window.

1,048,576 tokens

Maximum Output Tokens

The number of tokens that can be generated by the model in a single request.

8,192 tokens tokens

Open Source

Whether the model's code is available for public use.

Release Date

When the model was first released.

Feb 25, 2025 1 year ago

Knowledge Cut-off Date

When the model's knowledge was last updated.

June 2024

API Providers

The providers that offer this model. This is not an exhaustive list.

Google, Vertex AI, Google AI Studio

Modalities

Types of data this model can process.

Text Image Audio Video File

What is Gemini 2.0 Flash Lite

A fuller summary of positioning, capabilities, and source-specific details for Gemini 2.0 Flash Lite.

Gemini 2.0 Flash Lite is a multimodal text generation model developed by Google, released in early 2025 as part of the Gemini 2.0 model family. It is designed specifically for high-volume, cost-sensitive applications, offering a balance between response speed and output quality. The model supports a context window of over one million tokens (1,048,576), making it suitable for processing long documents or extended conversations in a single request.

Gemini 2.0 Flash Lite is best suited for developers and organizations that need to run large numbers of inference requests without incurring high costs. Its architecture prioritizes throughput and efficiency, making it a practical choice for tasks like summarization, classification, translation, and content generation at scale. The model's training data has a cutoff of June 2024, and it is accessible through Google's Vertex AI platform.

Capabilities

What Gemini 2.0 Flash Lite supports

CTX

Large Context Window

Processes up to 1,048,576 tokens in a single request, enabling analysis of long documents, codebases, or extended conversation histories without truncation.

Fast Inference

Optimized for low-latency responses, making it suitable for real-time applications and pipelines that require quick turnaround on text generation tasks.

Cost-Effective Scaling

Priced for high-volume usage, allowing developers to run large numbers of requests while keeping per-token costs low compared to larger model tiers.

Multimodal Input

Accepts text and image inputs within the same request, supporting tasks that combine visual and textual understanding such as image captioning or document analysis.

Text Generation

Generates coherent, contextually relevant text for use cases including summarization, translation, classification, and content drafting.

JSON

Structured Output

Supports JSON-mode responses, allowing developers to request structured data outputs suitable for downstream processing in applications and APIs.

Pricing for Gemini 2.0 Flash Lite

Primary API pricing shown in the same “quick compare” spirit as the reference page.

Input tokens $0.08 Per million tokens

Output tokens $0.30 Per million tokens

Price Comparison

Additional usage-cost dimensions synced into the project for this model.

Image input $0.07

Audio input $0.07

Web search $14000.00

Reasoning $0.30

maxTemperature 2

maxResponseSize 8,192 tokens

API Access & Providers

Places where this model is available, based on the synced detail-page metadata.

Google Vertex AI Google AI Studio

Provider Endpoints

Endpoint-level provider data currently available for this model.

Google

Max output: 8,192 1d uptime: 99.7% Supported params: 9 Implicit caching: No

Google AI Studio

Max prompt: 1,048,576 Max output: 8,192 1d uptime: 99.9% Supported params: 9 Implicit caching: No

Model Performance

Benchmark scores synced from the current model source and normalized into the local catalog.

Benchmark	Score
AIME 2024 American math olympiad problems	27.7%
GPQA Diamond PhD-level science questions (biology, physics, chemistry)	53.5%
HLE Questions that challenge frontier models across many domains	3.6%
LiveCodeBench Real-world coding tasks from recent competitions	18.5%
MATH-500 Undergraduate and competition-level math problems	87.3%
MMLU-Pro Expert knowledge across 14 academic disciplines	72.4%
SciCode Scientific research coding and numerical methods	25.0%

Resources & Documentation

Official model cards, release notes, docs, and other references synced from the source page.

Official Website Announcements

→

Documentation Documentation

→

Gemini API Docs Documentation

→

Google AI Studio Playground Playground

→

Gemini 2.0 Launch Announcement Announcements

→

OpenRouter Model Page OpenRouter

→

Community discussion

What people think about Gemini 2.0 Flash Lite

Gemini 2.0 Flash Lite discussions are most active in r/Bard, r/OpenClawInstall, r/GeminiAI. Top Reddit threads cluster around benchmark and model-comparison threads, coding workflow discussions.

The strongest match in this snapshot has 32 upvotes and 6 comments.

r/OpenClawInstall 1 upvotes 9 comments April 14, 2026

Openclaw Version 4.14 Debugging

Hi all. After 15 hours of trying to get openclaw to work.... I have officially given up. Will someone please help me make the fix?

Here is the error I am getting: In telegram: " Agent couldn't generate a response. Please try again."

Config file:

{
"agents": {
"defaults": {
"workspace": "/home/tyler/.openclaw/workspace",
"models": {
"openrouter/auto": {
"alias": "OpenRouter"
},
"openrouter/google/gemini-2.0-flash-lite-001": {}
},
"model": {
"primary": "openrouter/google/gemini-2.0-flash-lite-001"
}
},
"list": [
{
"id": "main",
"model": "openrouter/google/gemini-2.0-flash-lite-001",
"tools": {
"profile": "coding",
"alsoAllow": [
"browser",
"canvas",
"gateway",
"nodes",
"agents_list",
"tts",
"message"
]
}
},
{
"id": "jarvis",
"name": "jarvis",
"workspace": "/home/tyler/.openclaw/workspace-jarvis",
"agentDir": "/home/tyler/.openclaw/agents/jarvis/agent",
"model": "openrouter/google/gemini-3-flash-preview"
}
]
},
"gateway": {
"mode": "local",
"auth": {
"mode": "token",
"token": "REDACTED"
},
"port": 18789,
"bind": "lan",
"tailscale": {
"mode": "off",
"resetOnExit": false
},
"controlUi": {
"allowedOrigins": [
"http://localhost:18789",
"http://127.0.0.1:18789"
]
},
"nodes": {
"denyCommands": [
"camera.snap",
"camera.clip",
"screen.record",
"contacts.add",
"calendar.add",
"reminders.add",
"sms.send",
"sms.search"
]
}
},
"session": {
"dmScope": "per-channel-peer"
},
"tools": {
"profile": "coding"
},
"auth": {
"profiles": {
"openrouter:default": {
"provider": "openrouter",
"mode": "api_key"
}
}
},
"skills": {
"entries": {
"openai-whisper-api": {
"apiKey": "REDACTED"
},
"sag": {
"apiKey": "REDACTED"
}
}
},
"plugins": {
"entries": {
"device-pair": {
"config": {
"publicUrl": "http://127.0.0.1:18789"
},
"enabled": true
},
"openrouter": {
"enabled": true
},
"telegram": {
"enabled": true
},
"browser": {
"enabled": true
}
}
},
"hooks": {
"internal": {
"enabled": true,
"entries": {
"boot-md": {
"enabled": true
},
"bootstrap-extra-files": {
"enabled": true
},
"command-logger": {
"enabled": true
},
"session-memory": {
"enabled": true
}
}
}
},
"wizard": {
"lastRunAt": "2026-04-14T22:20:23.412Z",
"lastRunVersion": "2026.4.14",
"lastRunCommand": "doctor",
"lastRunMode": "local"
},
"meta": {
"lastTouchedVersion": "2026.4.14",
"lastTouchedAt": "2026-04-14T22:20:23.479Z"
},
"channels": {
"telegram": {
"enabled": true,
"botToken": "REDACTED",
"dmPolicy": "allowlist",
"allowFrom": [
"REDACTED"
]
}
},
"bindings": [
{
"type": "route",
"agentId": "jarvis",
"match": {
"channel": "telegram",
"accountId": "REDACTED"
}
}
]
}

{
"agents": {
"defaults": {
"workspace": "/home/tyler/.openclaw/workspace",
"models": {
"openrouter/auto": {
"alias": "OpenRouter"
},
"openrouter/google/gemini-2.0-flash-lite-001": {}
},
"model": {
"primary": "openrouter/auto",
"fallbacks": [
"openrouter/google/gemini-2.0-flash-lite-001"
]
}
},
"list": [
{
"id": "main",
"model": "openrouter/google/gemini-2.0-flash-lite-001",
"tools": {
"profile": "coding",
"alsoAllow": [
"browser",
"canvas",
"gateway",
"nodes",
"agents_list",
"tts",
"message"
]
}
},
{
"id": "jarvis",
"name": "jarvis",
"workspace": "/home/tyler/.openclaw/workspace-jarvis",
"agentDir": "/home/tyler/.openclaw/agents/jarvis/agent",
"model": "openrouter/google/gemini-3-flash-preview"
}
]
},
"gateway": {
"mode": "local",
"auth": {
"mode": "token",
"token": "REDACTED"
},
"port": 18789,
"bind": "lan",
"tailscale": {
"mode": "off",
"resetOnExit": false
},
"controlUi": {
"allowedOrigins": [
"http://localhost:18789",
"http://127.0.0.1:18789"
]
},
"nodes": {
"denyCommands": [
"camera.snap",
"camera.clip",
"screen.record",
"contacts.add",
"calendar.add",
"reminders.add",
"sms.send",
"sms.search"
]
}
},
"session": {
"dmScope": "per-channel-peer"
},
"tools": {
"profile": "coding"
},
"auth": {
"profiles": {
"openrouter": {
"provider": "openrouter",
"mode": "api_key"
},
"openrouter:default": {
"provider": "openrouter",
"mode": "api_key"
}
}
},
"skills": {
"entries": {
"openai-whisper-api": {
"apiKey": "REDACTED"
},
"sag": {
"apiKey": "REDACTED"
}
}
},
"plugins": {
"entries": {
"device-pair": {
"config": {
"publicUrl": "http://127.0.0.1:18789"
},
"enabled": true
},
"openrouter": {
"enabled": true
},
"telegram": {
"enabled": true
},
"browser": {
"enabled": true
}
}
},
"hooks": {
"internal": {
"enabled": true,
"entries": {
"boot-md": {
"enabled": true
},
"bootstrap-extra-files": {
"enabled": true
},
"command-logger": {
"enabled": true
},
"session-memory": {
"enabled": true
}
}
}
},
"wizard": {
"lastRunAt": "2026-04-14T22:32:39.837Z",
"lastRunVersion": "2026.4.14",
"lastRunCommand": "configure",
"lastRunMode": "local"
},
"meta": {
"lastTouchedVersion": "2026.4.14",
"lastTouchedAt": "2026-04-14T22:32:39.903Z"
},
"channels": {
"telegram": {
"enabled": true,
"botToken": "REDACTED",
"dmPolicy": "allowlist",
"allowFrom": [
"REDACTED"
]
}
},
"bindings": [
{
"type": "route",
"agentId": "jarvis",
"match": {
"channel": "telegram",
"accountId": "REDACTED"
}
}
]
}

Potentially Useful Logs: 22:34:41+00:00 warn gateway {"subsystem":"gateway"} ⚠️ Gateway is binding to a non-loopback address. Ensure authentication is configured before exposing to public networks.
22:34:42+00:00 info gateway {"subsystem":"gateway"} agent model: openrouter/google/gemini-2.0-flash
22:34:42+00:00 warn gateway/ws {"subsystem":"gateway/ws"} {"cause":"origin-mismatch","reason":"origin not allowed","client":"openclaw-control-ui"} code=1008
22:34:50+00:00 warn gateway {"subsystem":"gateway"} startup model warmup failed for openrouter/google/gemini-2.0-flash: Error: Unknown model: openrouter/google/gemini-2.0-flash

22:35:08+00:00 warn agent/embedded {"event":"embedded_run_agent_end","error":"400 google/gemini-2.0-flash is not a valid model ID","failoverReason":"model_not_found"}

22:37:47+00:00 warn Config observe anomaly: missing-meta-vs-last-good, gateway-mode-missing-vs-last-good
22:37:47+00:00 warn gateway/reload config reload skipped (invalid config): JSON5 parse failed
22:37:49+00:00 info gateway/reload config hot reload applied (agents.defaults.model.primary)

22:38:08+00:00 warn agent/embedded incomplete turn detected

22:45:19+00:00 warn gateway/reload config change requires gateway restart (auth.profiles.openrouter)

Open Reddit thread

r/Bard 5 upvotes 4 comments April 11, 2026

Found this list from an api call. Is there any unreleased models/suprises?

Updated

models/gemini-2.5-flash

models/gemini-2.5-pro

models/gemini-2.0-flash

models/gemini-2.0-flash-001

models/gemini-2.0-flash-lite-001

models/gemini-2.0-flash-lite

models/gemini-2.5-flash-preview-tts

models/gemini-2.5-pro-preview-tts

models/gemma-3-1b-it

models/gemma-3-4b-it

models/gemma-3-12b-it

models/gemma-3-27b-it

models/gemma-3n-e4b-it

models/gemma-3n-e2b-it

models/gemma-4-26b-a4b-it

models/gemma-4-31b-it

models/gemini-flash-latest

models/gemini-flash-lite-latest

models/gemini-pro-latest

models/gemini-2.5-flash-lite

models/gemini-2.5-flash-image

models/gemini-3-pro-preview

models/gemini-3-flash-preview

models/gemini-3.1-pro-preview

models/gemini-3.1-pro-preview-customtools

models/gemini-3.1-flash-lite-preview

models/gemini-3-pro-image-preview

models/nano-banana-pro-preview

models/gemini-3.1-flash-image-preview

models/lyria-3-clip-preview

models/lyria-3-pro-preview

models/gemini-robotics-er-1.5-preview

models/gemini-2.5-computer-use-preview-10-2025

models/deep-research-pro-preview-12-2025

models/gemini-embedding-001

models/gemini-embedding-2-preview

models/aqa

models/imagen-4.0-generate-001

models/imagen-4.0-ultra-generate-001

models/imagen-4.0-fast-generate-001

models/veo-2.0-generate-001

models/veo-3.0-generate-001

models/veo-3.0-fast-generate-001

models/veo-3.1-generate-preview

models/veo-3.1-fast-generate-preview

models/veo-3.1-lite-generate-preview

models/gemini-2.5-flash-native-audio-latest

models/gemini-2.5-flash-native-audio-preview-09-2025

models/gemini-2.5-flash-native-audio-preview-12-2025

models/gemini-3.1-flash-live-preview

models/lyria-realtime-exp

Open Reddit thread

r/GeminiAI 1 upvotes 1 comments April 11, 2026

Found this list over all google ai models. Is anything new/suprising?

Open Reddit thread

r/programacionESP 2 upvotes 3 comments February 7, 2026

Ayuda con api gemini

Hola comunidad estoy creando una app que crea una rutina con gemini
al principio estuve usando gemini 2.5 flash y todo genial
pero me di cuenta de que tenia un limitante de 20 solicitudes al día
asi que decidi cambiar de modelo, este es el fragmento de código que cambio:

final model = GenerativeModel(
model: 'gemini-2.0-flash-lite-001',
apiKey: apiKey,
);

y con cada una que utilizo me dice que Error de conexión con la IA: Excediste tu cuota actual. Revisa tu plan y los detalles de facturación.

que modelo puedo usar? que me de mas solicitudes al día

Open Reddit thread

r/GoogleGeminiAI 2 upvotes 4 comments November 20, 2025

Which model uses the free tier? These are the models I have access to with my API key.

models/gemini-2.5-pro-preview-03-25
models/gemini-2.5-flash
models/gemini-2.5-pro-preview-05-06
models/gemini-2.5-pro-preview-06-05
models/gemini-2.5-pro
models/gemini-2.0-flash-exp
models/gemini-2.0-flash
models/gemini-2.0-flash-001
models/gemini-2.0-flash-exp-image-generation
models/gemini-2.0-flash-lite-001
models/gemini-2.0-flash-lite
models/gemini-2.0-flash-lite-preview-02-05
models/gemini-2.0-flash-lite-preview
models/gemini-2.0-pro-exp
models/gemini-2.0-pro-exp-02-05
models/gemini-exp-1206
models/gemini-2.0-flash-thinking-exp-01-21
models/gemini-2.0-flash-thinking-exp
models/gemini-2.0-flash-thinking-exp-1219
models/gemini-2.5-flash-preview-tts
models/gemini-2.5-pro-preview-tts
models/learnlm-2.0-flash-experimental
models/gemma-3-1b-it
models/gemma-3-4b-it
models/gemma-3-12b-it
models/gemma-3-27b-it
models/gemma-3n-e4b-it
models/gemma-3n-e2b-it
models/gemini-flash-latest
models/gemini-flash-lite-latest
models/gemini-pro-latest
models/gemini-2.5-flash-lite
models/gemini-2.5-flash-image-preview
models/gemini-2.5-flash-image
models/gemini-2.5-flash-preview-09-2025
models/gemini-2.5-flash-lite-preview-09-2025
models/gemini-3-pro-preview
models/gemini-3-pro-image-preview
models/nano-banana-pro-preview
models/gemini-robotics-er-1.5-preview
models/gemini-2.5-computer-use-preview-10-2025

Open Reddit thread

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FAQ

Common questions about Gemini 2.0 Flash Lite

What is the context window size for Gemini 2.0 Flash Lite?

Gemini 2.0 Flash Lite supports a context window of 1,048,576 tokens, which allows it to process very long documents or extended multi-turn conversations in a single request.

What is the training data cutoff for this model?

The model's training data has a cutoff of June 2024, meaning it does not have knowledge of events or information published after that date.

How is Gemini 2.0 Flash Lite priced?

Gemini 2.0 Flash Lite is positioned as a cost-effective option within the Gemini 2.0 family, designed for high-volume workloads. Specific pricing details are available on the Google Cloud Vertex AI pricing page.

What types of inputs does Gemini 2.0 Flash Lite support?

The model supports text and image inputs, making it a multimodal model capable of handling tasks that involve both written content and visual data.

Where can I access and deploy Gemini 2.0 Flash Lite?

Gemini 2.0 Flash Lite is available through Google's Vertex AI platform and via the Google AI Studio and Gemini API. Documentation is provided on the Google Cloud Vertex AI documentation site.

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