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.
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.
High-signal model metadata in a structured two-column overview table.
The entity that provides this model.
The routed model identifier exposed by upstream providers.
The number of tokens supported by the input context window.
The number of tokens that can be generated by the model in a single request.
Whether the model's code is available for public use.
When the model was first released.
When the model's knowledge was last updated.
The providers that offer this model. This is not an exhaustive list.
Types of data this model can process.
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.
Supports up to 1,048,576 tokens in a single context, enabling processing of long documents, codebases, or extended conversation histories in one request.
Designed to return responses at real-time speeds, making it suitable for interactive applications and live user-facing workflows.
Generates coherent, contextually relevant text across tasks such as summarization, drafting, question answering, and instruction following.
Supports structured response formats, allowing developers to request JSON or other schema-conforming outputs for downstream processing.
Supports function calling, enabling the model to invoke developer-defined tools and integrate with external APIs or services within a workflow.
Accepts text, images, audio, and video as inputs, allowing mixed-media prompts to be processed within the same large context window.
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Additional usage-cost dimensions synced into the project for this model.
Places where this model is available, based on the synced detail-page metadata.
Endpoint-level provider data currently available for this model.
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
|
|
MATH-500
Undergraduate and competition-level math problems
|
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
Official model cards, release notes, docs, and other references synced from the source page.
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.
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 :(
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)
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
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.
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.
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.
Yes. The model is specifically tagged for real-time latency, making it appropriate for interactive, user-facing applications where response speed is a priority.
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.
Continue browsing adjacent models from the same provider.