Google

Gemini 2.0 Flash

Gemini 2.0 Flash is a text generation model developed by Google, released as part of the Gemini 2.0 model family. It features a context window of 1,048,576 tokens and is designed to handle a broad range of everyday tasks with real-time response latency. The model's training data has a cutoff of June 2024. Gemini 2.0 Flash is positioned as an upgrade for users of the 1.5 Flash model who want meaningfully improved output quality, and for users of the 1.5 Pro model who want comparable or slightly improved quality at lower latency and cost. It is well-suited for applications that require processing long documents, maintaining extended conversations, or running high-throughput workloads where response speed matters.

Feb 05, 2025 1,048,576 context 8,192 tokens output
Large Context Window Real-Time Latency Text Generation Structured Output Function Calling Multimodal Input

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-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.

No

Release Date

When the model was first released.

Feb 05, 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

What is Gemini 2.0 Flash

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

Gemini 2.0 Flash is a text generation model developed by Google, released as part of the Gemini 2.0 model family. It features a context window of 1,048,576 tokens and is designed to handle a broad range of everyday tasks with real-time response latency. The model's training data has a cutoff of June 2024.

Gemini 2.0 Flash is positioned as an upgrade for users of the 1.5 Flash model who want meaningfully improved output quality, and for users of the 1.5 Pro model who want comparable or slightly improved quality at lower latency and cost. It is well-suited for applications that require processing long documents, maintaining extended conversations, or running high-throughput workloads where response speed matters.

Capabilities

What Gemini 2.0 Flash supports

CTX

Large Context Window

Supports up to 1,048,576 tokens in a single context, enabling processing of long documents, codebases, or extended conversation histories in one request.

AI

Real-Time Latency

Designed to return responses at real-time speeds, making it suitable for interactive applications and live user-facing workflows.

AI

Text Generation

Generates coherent, contextually relevant text across tasks such as summarization, drafting, question answering, and instruction following.

JSON

Structured Output

Supports structured response formats, allowing developers to request JSON or other schema-conforming outputs for downstream processing.

AI

Function Calling

Supports function calling, enabling the model to invoke developer-defined tools and integrate with external APIs or services within a workflow.

MM

Multimodal Input

Accepts text, images, audio, and video as inputs, allowing mixed-media prompts to be processed within the same large context window.

Pricing for Gemini 2.0 Flash

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

Price Comparison

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

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: 96.0% Supported params: 9 Implicit caching: No

Google AI Studio

Max output: 8,192 1d uptime: 98.7% 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
33.0%
GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
62.3%
HLE
Questions that challenge frontier models across many domains
5.3%
LiveCodeBench
Real-world coding tasks from recent competitions
33.4%
MATH-500
Undergraduate and competition-level math problems
93.0%
MMLU-Pro
Expert knowledge across 14 academic disciplines
77.9%
SciCode
Scientific research coding and numerical methods
33.3%

Resources & Documentation

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

Community discussion

What people think about Gemini 2.0 Flash

Gemini 2.0 Flash discussions are most active in r/Bard, r/feedsfun, r/googlecloud. Top Reddit threads cluster around coding workflow discussions. The strongest match in this snapshot has 157 upvotes and 25 comments.

# 80,000 NOK ($7,500) drained from my Google Cloud account in 5 minutes — full forensic breakdown of how the attack worked

I want to write this up while it's fresh, because the *mechanism* of the attack is more interesting than the "I leaked a key, oops" headline — and the platform design that allowed it is something every Google Cloud user should know about.

# What happened

* May 8, 2026, evening (CET): I get a billing alert email saying I owe NOK 82,305.36 (\~$7,500 USD) on my Google Cloud account.
* My typical monthly spend: \~100 NOK ($10).
* The spike happened in roughly 5 minutes.
* All charges were on the Gemini API in a single project I'd barely touched (an old "no-code maps" project from 2017).
* An API key from that project was leaked somewhere — I'm still hunting where. Most likely an old GitHub repo or a public webpage from 2018-ish that had Gemini API enabled on its project years later (I think this is what made it exploitable — the key sat dormant, but the moment Gemini got enabled on its project, the dormant key became a Gemini-capable wallet).

# What the attacker actually did (the part nobody talks about)

I pulled the SKU-level breakdown from Billing → Reports. The attacker didn't just hit one model. They ran an automated framework that fanned out across every Gemini variant simultaneously:

* Gemini 3 Pro (text + image generation)
* Gemini 3 Flash
* Gemini 3.1 Flash Image
* Gemini 3.1 Flash Lite Preview
* Gemini 2.5 Pro (text + TTS)
* Gemini 2.5 Flash (short + long context, multimodal)
* Gemini 2.5 Flash Lite
* Gemini 2.0 Flash TTS
* Gemini Embedding-2 + Embedding-001

15+ distinct models in 5 minutes. No human application uses 15 models in parallel. This is the signature of an automated abuse framework, almost certainly a credential-resale operation.

Token volumes:

* 1.09 BILLION input tokens on Gemini 2.5 Flash Lite alone
* 402M image input tokens on Gemini 3 Pro
* 226M text input tokens on Gemini 3 Pro
* 19.4M image output tokens on Gemini 3 Pro Image — kr 21,674 ($2,000) on this single SKU, the most expensive line item

The attacker prioritized image generation because that's where the real money is — image output tokens are 50–100x more expensive than text.

# How they bypassed rate limits (this is the architectural problem)

You'd think rate limits would protect you. They don't — at least not on Google Cloud:

* Gemini 3 Pro: 1,000 RPM
* Gemini 3 Flash: 2,000 RPM
* Gemini 2.5 Flash Lite: 4,000 RPM
* (etc., for every model — *each with its own independent quota*)

There is no per-key aggregate cap across models. If you fan out across 15 models concurrently, you cap at the *sum* — easily 30,000+ RPM combined.

OpenAI, Anthropic, and Mistral all have per-key aggregate caps. Google does not. This is not a policy oversight — it's the core mechanism that makes a single compromised key a 5-minute, 5-figure liability.

Also: Google Cloud does not offer a hard spending cap. No "stop all spend at $X" option. The closest is a budget alert that *emails you* (after the fact), or — and this is the documented "solution" — you can write your own Cloud Function that listens to budget Pub/Sub events and programmatically disables your billing account. Yes, Google's official answer to "how do I stop runaway spending" is "deploy code on the same platform that's billing you." This has been a known gripe for years.

# What logging gave me — almost nothing

I tried every audit log query:

* `protoPayload.serviceName="generativelanguage.googleapis.com"` → empty
* `resource.type="consumed_api"` for the project → empty
* Vertex AI logs → empty

Google does not log per-request data for Gemini API key calls. No caller IP, no user-agent, no request size. The only forensic record that exists is the SKU-level billing report — and that only goes down to "model + token type", not session/request/key.

So I can't tell you who did it, where they were, or what they generated. I just know it was 15 models in parallel and 19M image output tokens.

# What I did in the first 90 minutes

* Deleted all 13 API keys on the affected project (after seeing the alert at \~01:25)
* Disabled [`generativelanguage.googleapis.com`](http://generativelanguage.googleapis.com) and [`aiplatform.googleapis.com`](http://aiplatform.googleapis.com) on every one of my 25+ projects (script via `gcloud services disable`)
* Closed all 3 billing accounts
* Called my bank, blocked the Visa
* Got into Google's billing chat queue, escalated to specialist team within 5 messages
* Case 71021804 opened, 24-48h response window
* Pulled SKU-level forensic evidence

The chat agent confirmed end-of-month billing cycle, so the actual charge attempt won't fire until \~May 28-31. By then either the specialist team has waived it, or the card-block + chargeback dispute kicks in.

# What I'm pretty sure happens next

* \~85% chance: specialist team waives the charge under the compromised-credentials policy. Google has standardized this for exactly this scenario because they know the rate-limit architecture allows it.
* \~10% chance: partial waiver / settlement.
* \~5% chance: they refuse, my bank chargeback wins it under Norwegian Finansavtaleloven (450 NOK max liability for unauthorized card use).

I'm not actually going to pay 80k. The realistic worst case is several months of paperwork.

# Lessons / PSA for everyone running Google Cloud

1. Restrict every API key at creation time. Application restriction (HTTP referrer or IP allowlist) + API restriction (only the APIs you use). An unrestricted key on a project where Gemini happens to be enabled is a wallet.
2. Audit every project for keys you've forgotten about. I had keys from 2017, 2020, 2021 — most predating Gemini's existence. The moment Gemini got enabled on those old projects, the old keys could call it.
3. Disable APIs you don't actively use. Per-project. An enabled API + an unrestricted key = exposure.
4. Set up a budget-disables-billing Cloud Function. The auto-shutdown one. Yes it's stupid that Google makes you write code for this, but it's the only real circuit breaker.
5. Don't trust rate limits. They protect Google's infrastructure, not your wallet. Per-model RPM × N models = no real cap.
6. Don't store API keys in client-side code, ever. Even if you think a project is dead.

# Where the leak came from

Honestly, I don't know yet. The project was created in 2017 (back when Google appended a numeric suffix like `-364317` to project IDs). It had 13 keys accumulated over years. One of them is somewhere out in the wild. I'll be searching GitHub history, old Vercel deployments, Wayback Machine, and screenshots over the coming days. If I find it I'll edit this post.

If anyone has run into the same multi-model abuse pattern recently, I'd love to hear about it — particularly if you have any signals on which credential-resale operations are currently active.

Edit: Will update with specialist team's response when it arrives in 24-48h.

Open Reddit thread
r/lmarena 12 upvotes 16 comments April 25, 2026
List of all models.

There are currently 481 models listed on the [arena.ai](http://arena.ai) website.

Here's the full list:

amazon.nova-pro-v1:0

anonymous-0410

anonymous-1111

anonymous-1218

anonymous-1221

anonymous-1800

anonymous-1815

anonymous-1825

anonymous-1835

apex-atlas

april26-chatbot1

april26-chatbot2

arastradero

atlas

autobear

badger

basalt-0303-1

basalt-0422-1

baseliner

beluga-0311-1

beluga-0413-1

blackhawk

blue-forge

botbot2

chatgpt-image-latest-high-fidelity (20251216)

chipmunk

chives

citrus

claude-3-5-sonnet-20241022

claude-3-7-sonnet-20250219

claude-3-7-sonnet-20250219-thinking-32k

claude-haiku-4-5-20251001

claude-opus-4-1-20250805

claude-opus-4-1-20250805-thinking-16k

claude-opus-4-1-search

claude-opus-4-20250514

claude-opus-4-20250514-thinking-16k

claude-opus-4-5-20251101

claude-opus-4-5-20251101-thinking-32k

claude-opus-4-5-search

claude-opus-4-6

claude-opus-4-6-search

claude-opus-4-6-thinking

claude-opus-4-7

claude-opus-4-7-search

claude-opus-4-7-thinking

claude-opus-4-search

claude-sonnet-4-20250514

claude-sonnet-4-20250514-thinking-32k

claude-sonnet-4-5-20250929

claude-sonnet-4-5-20250929-thinking-32k

claude-sonnet-4-5-search

claude-sonnet-4-6

claude-sonnet-4-6-search

clawl

clinkz

cloud-buddy

dall-e-3

dart-frog-0206

deep-octo

deepseek-v4-flash

deepseek-v4-flash-thinking

deepseek-v4-pro

deepseek-v4-pro-thinking

devstral-2

devstral-medium-2507

dialogue

dola-seed-2.0-preview-text

dola-seed-2.0-preview-vision

dola-seed-2.0-pro-text

dola-seed-2.0-pro-vision

dove

duomo-1-hero

EB45-turbo

EB45-vision

ember

emu

epilogue

ernie-5.0-0110

ernie-5.0-preview-1220

ernie-exp-251023

ernie-exp-251024

ernie-exp-251025

ernie-exp-251026

ernie-exp-251027

ernie-exp-vl-251016

ernie-image

eureka

february26-chatbot2

february26-chatbot3

february26-chatbot4

flashbrown-a

flashbrown-b

flow-state

flow-state-2

flow-state-3

flux-1-kontext-dev

flux-1-kontext-max

flux-1-kontext-pro

flux-2-dev

flux-2-flex

flux-2-klein-4b

flux-2-klein-9b

flux-2-max

flux-2-pro

flying-octopus

frenchfry

frieza

gallant

gallery

gcps-fast

gemini-2.0-flash-001

gemini-2.5-flash

gemini-2.5-flash-image-preview (nano-banana)

gemini-2.5-pro

gemini-2.5-pro-grounding

gemini-2.5-pro-grounding-exp

gemini-3-flash

gemini-3-flash (thinking-minimal)

gemini-3-flash-grounding

gemini-3-pro

gemini-3-pro-image-preview-2k (nano-banana-pro)

gemini-3.1-flash-image-preview (nano-banana-2) \[web-search\]

gemini-3.1-flash-lite-preview

gemini-3.1-pro

gemini-3.1-pro-grounding

gemini-3.1-pro-preview

gemma-3-27b-it

gemma-3n-e4b-it

glm-4.7

glm-4.7-flash

glm-5

glm-5.1

glm-5v-turbo

globe\_2

gpt-4.1-2025-04-14

gpt-4.1-mini-2025-04-14

gpt-5-chat

gpt-5-high

gpt-5-high-new-system-prompt

gpt-5-high-no-system-prompt

gpt-5-medium

gpt-5-mini-high

gpt-5-nano-high

gpt-5-search

gpt-5.1

gpt-5.1-codex

gpt-5.1-codex-max

gpt-5.1-codex-mini

gpt-5.1-high

gpt-5.1-medium

gpt-5.1-search

gpt-5.1-search-sp

gpt-5.2

gpt-5.2-chat-latest

gpt-5.2-codex

gpt-5.2-high

gpt-5.2-search

gpt-5.2-search-non-reasoning

gpt-5.3-chat-latest

gpt-5.3-codex

gpt-5.4

gpt-5.4-high

gpt-5.4-high-no-system-prompt

gpt-5.4-medium

gpt-5.4-mini-high

gpt-5.4-nano-high

gpt-5.4-no-system-prompt

gpt-5.4-search

gpt-5.5

gpt-5.5-high

gpt-5.5-search

gpt-image-1

gpt-image-1-high-fidelity

gpt-image-1-mini

gpt-image-1.5-high-fidelity

gpt-image-2 (medium)

gpt-oss-120b

gpt-oss-20b

grok-3-mini-beta

grok-3-mini-high

grok-4-0709

grok-4-1-fast-non-reasoning

grok-4-1-fast-reasoning

grok-4-1-fast-search

grok-4-fast-chat

grok-4-fast-reasoning

grok-4-fast-search

grok-4-search

grok-4.1

grok-4.1-thinking

grok-4.20-beta-0309-reasoning

grok-4.20-beta1

grok-4.20-multi-agent-beta-0309

grok-code-fast-1

grok-imagine-image

grok-imagine-image-pro

grok-imagine-video

hailuo-02-fast

hailuo-02-pro

hailuo-02-standard

hailuo-2.3

hailuo-2.3-fast

happy-friday-testing-1

happy-friday-testing-2

hearth

hidream-e1.1

hofburg\_2

hofburg\_2\_alt

hofburg\_3

hofburg\_4

hofburg\_5

hofburg\_5\_alt

hofburg-1

hunyuan-hy3-preview

hunyuan-image-2.1

hunyuan-image-3.0

hunyuan-image-3.0-fal

hunyuan-t1-20250711

hunyuan-video-1.5

hunyuan-vision-1.5-thinking

ibm-granite-h-small

ideogram-v3-quality

imagen-3.0-generate-002

imagen-4.0-fast-generate-001

imagen-4.0-generate-001

imagen-4.0-ultra-generate-001

intellect-3

jester

jumbo-dungeness

juniper

k2

kandinsky-5.0-i2v-pro

kandinsky-5.0-t2v-lite

kandinsky-5.0-t2v-pro

karyu

KAT-Coder-Pro-V1

ketchup-v2

kimi-k2-0711-preview

kimi-k2-0905-preview

kimi-k2-thinking-turbo

kimi-k2.5

kimi-k2.5-instant

kimi-k2.6

kiteki

kiwi-do

kiwire

kizen-alpha

kizen-beta

kling-2.5-turbo-1080p

kling-2.6-pro

kling-2.6-standard

kling-image-o1

kling-o1-pro

kling-o3-pro

kling-v2.1-master

kling-v2.1-standard

kling-v3

leepwal

left-bank

ling-1t

ling-1t-1031

ling-2.5-1t

ling-flash-2.0

llama-3.3-70b-instruct

longcat-flash-chat

ltx-2-19b

lucid-origin

mammoth-newt-0206

mammoth-newt-0226

march26-chatbot1

march26-chatbot1-public

march26-chatbot2

march26-chatbot3

markhor

Max

mercury

mercury-2

micro-mango

mimo-v2-flash

mimo-v2-flash (thinking)

mimo-v2-omni

mimo-v2-pro

mimo-v2.5

mimo-v2.5-pro

minicpm-sala

minimax-m1

minimax-m2

minimax-m2-preview

minimax-m2.1-preview

minimax-m2.5

mistral-large-3

mistral-medium-2505

mistral-medium-2508

mistral-small-2506

mistral-small-2603

mistral-small-3.1-24b-instruct-2503

mochi-v1

model-x

model-x-2

molmo-2-8b

monologue

monster

monterey

neon

nightride-on

nightride-on-v2

nova-2-lite

nvidia-nemotron-3-nano-30b-a3b-bf16

o3-2025-04-16

o3-mini

o3-search

o4-mini-2025-04-16

olmo-3-32b-think

olmo-3.1-32b-instruct

olmo-3.1-32b-think

orion

p-image

p-image-edit

paper-lantern

pebble-1

pebble-2

pepper

photon

pika-v2.2

pine

pisces-0226d

pisces-0309

pisces-0309-vision

pisces-0309b

pisces-0309c

pisces-0309d

pisces-0318-text

pisces-0318-vision

pisces-0320

pisces-llm-0130

pixel-parrot

pixverse-v5.6

ppl-sonar-reasoning-pro-high

prologue

pteronura

pulse

queen-bee

quiet\_sand

qwen-image-2.0

qwen-image-2.0-pro

qwen-image-2512

qwen-image-edit

qwen-image-edit-2511

qwen-image-prompt-extend

qwen-vl-max-2025-08-13

qwen3-235b-a22b

qwen3-235b-a22b-instruct-2507

qwen3-235b-a22b-no-thinking

qwen3-235b-a22b-thinking-2507

qwen3-30b-a3b

qwen3-30b-a3b-instruct-2507

qwen3-coder-480b-a35b-instruct

qwen3-max-2025-09-23

qwen3-max-2025-09-26

qwen3-max-2025-10-30

qwen3-max-preview

qwen3-max-thinking

qwen3-next-80b-a3b-instruct

qwen3-next-80b-a3b-thinking

qwen3-omni-flash

qwen3-vl-235b-a22b-instruct

qwen3-vl-235b-a22b-thinking

qwen3-vl-8b-instruct

qwen3-vl-8b-thinking

qwen3.5-122b-a10b

qwen3.5-122b-a10b-code

qwen3.5-27b

qwen3.5-27b-code

qwen3.5-35b-a3b

qwen3.5-35b-a3b-code

qwen3.5-397b-a17b

qwen3.5-flash

qwen3.6-plus

qwen3.6-plus-preview

qwq-32b

raptor-1.8-0120

raptor-1123

raptor-1124

ray-3

ray2

recraft-v3

recraft-v4

redwood

reve-v1.1

reve-v1.1-fast

ring-1t

ring-2.5-1t

ring-flash-2.0

rising-sun

robin

robin-high

rotten-apple

runway-gen-4.5

runway-gen4

runway-gen4-aleph

runway-gen4-turbo

scorch

seed-1.8

seedance-v1-lite

seedance-v1-pro

seedance-v1.5-pro

seededit-3.0

seedream-3

seedream-4-high-res-fal

seedream-4.5

seedream-5.0-lite

shakshouka

significant-otter

snowflake

soft-shell

solar-eclipse

sora

sora-2

sora-2-pro

spark

sphinx

spire

star-drift

steed-0217

step-3

step-3-mini-2511

step-3.5-flash

stephen-v2

stephen-vision-csfix

sungod

sunshine-ai

super-cara

super-gcp

tatertot

trinity-large

trinity-large-thinking

velo

veo-2

veo-3

veo-3-audio

veo-3-fast

veo-3-fast-audio

veo-3.1-audio

veo-3.1-audio-1080p

veo-3.1-audio-4k

veo-3.1-fast-audio

veo-3.1-fast-audio-1080p

veo-3.1-fast-audio-4k

vidu-q2-image

vierra

viper

vortex

vulcan

waffle

wan-v2.2-a14b

wan-vace

wan2.5-i2i-preview

wan2.5-i2v-preview

wan2.5-preview

wan2.5-t2i-preview

wan2.5-t2v-preview

wan2.6-i2v

wan2.6-image

wan2.6-t2i

wan2.6-t2v

wan2.7-i2v

wan2.7-image

wan2.7-image-pro

wan2.7-t2v

whisperfall

wild-bits

yivon-beta

yotta-nexus

z-image

zephyr

zero-prism

zeylu-alpha

zeylu-beta

zorik

Unfortunately, the list of models available for selection in direct and side-by-side mode is much smaller :(

Open Reddit thread
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)

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FAQ

Common questions about Gemini 2.0 Flash

What is the context window size for Gemini 2.0 Flash?

Gemini 2.0 Flash has a context window of 1,048,576 tokens, which allows it to process very long documents or extended 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 does Gemini 2.0 Flash differ from Gemini 1.5 Flash?

According to Google's documentation, Gemini 2.0 Flash offers significantly better output quality than 1.5 Flash, with a modest trade-off in response speed.

Is Gemini 2.0 Flash suitable for real-time applications?

Yes. The model is specifically tagged for real-time latency, making it appropriate for interactive, user-facing applications where response speed is a priority.

Where can I access Gemini 2.0 Flash via API?

Gemini 2.0 Flash is available through Google's Gemini API and on Google Cloud Vertex AI. On MindStudio, you can use it directly without managing API keys yourself.

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