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"gpt4-turbo": "0",
"gpt4o": "0",
"gpt41": "0",
"gpt41-mini": "0",
"gpt41-nano": "0",
"gpt5": "0",
"gpt51": "0",
"gpt52": "0",
"gpt53-codex": "0",
"gpt53-chat-latest": "0",
"gpt54": "0",
"gpt54-thinking": "0",
"gpt54-pro": "0",
"gpt54-mini": "0",
"gpt54-nano": "0",
"gpt55": "0",
"gpt55-pro": "0",
"gpt56": "0",
"gpt56-terra": "0",
"gpt56-luna": "0",
"gpt5-mini": "0",
"gpt5-nano": "0",
"gpt5-chat-latest": "0",
"gpt5-pro": "0",
"o1": "0",
"o1-pro": "0",
"o3-pro": "0",
"o3-mini": "0",
"o3": "0",
"o4-mini": "0",
"dall-e": "0",
"gpt-image": "0",
"claude": "1000000",
"claude-sonnet-5": "1000000",
"claude-opus": "1000000",
"claude-fable": "1000000",
"claude-haiku": "1000000",
"gemini-flash": "0",
"gemini-35-flash": "0",
"gemini-35-flash-lite": "0",
"gemini-31-flash-lite": "0",
"gemini-36-flash": "0",
"gemini-37-flash": "0",
"gemini-pro": "0",
"gemini-3-pro": "0",
"gemini-ultra": "0",
"veo": "0",
"mistral-tiny": "0",
"mistral-small": "0",
"mistral-medium": "0",
"mistral-large": "0",
"aws-claude": "1000000",
"aws-claude-sonnet-5": "1000000",
"aws-claude-opus": "1000000",
"aws-claude-fable": "1000000",
"aws-claude-mythos": "0",
"aws-claude-haiku": "1000000",
"aws-mistral-tiny": "0",
"aws-mistral-small": "0",
"aws-mistral-medium": "0",
"aws-mistral-large": "0",
"gcp-claude": "0",
"gcp-claude-opus": "7000000",
"gcp-gemini-pro": "0",
"gcp-gemini-flash": "0",
"gcp-gemini-3-pro": "0",
"azure-turbo": "0",
"azure-gpt4": "0",
"azure-gpt4-turbo": "0",
"azure-gpt4o": "0",
"azure-gpt41": "0",
"azure-gpt41-mini": "0",
"azure-gpt41-nano": "0",
"azure-gpt5": "0",
"azure-gpt51": "0",
"azure-gpt52": "0",
"azure-gpt53-codex": "0",
"azure-gpt53-chat-latest": "0",
"azure-gpt54": "0",
"azure-gpt54-thinking": "0",
"azure-gpt54-pro": "0",
"azure-gpt54-mini": "0",
"azure-gpt54-nano": "0",
"azure-gpt55": "0",
"azure-gpt55-pro": "0",
"azure-gpt56": "40000000",
"azure-gpt56-terra": "0",
"azure-gpt56-luna": "0",
"azure-gpt5-mini": "0",
"azure-gpt5-nano": "0",
"azure-gpt5-chat-latest": "0",
"azure-gpt5-pro": "0",
"azure-dall-e": "0",
"azure-o1": "0",
"azure-o1-pro": "0",
"azure-o3-pro": "0",
"azure-o3-mini": "0",
"azure-o3": "0",
"azure-o4-mini": "0",
"azure-gpt-image": "0",
"openai": "15000000",
"google-ai": "0"
},
"openAICachedInputQuotaFactor": "0.5",
"anthropicCacheReadQuotaFactor": "0.1",
"anthropicCacheWriteQuotaFactor": "1.25",
"anthropicCacheWrite1hQuotaFactor": "2",
"allowAnthropicFastMode": "false",
"gcpClaudeOpenAiThinkingDefault": "true",
"gcpClaudeMultiRegionFallback": "true",
"gcpClaudeRegionDeepProbe": "true",
"quotaRefreshPeriod": "daily",
"openAIImageModeration": "low",
"imageAssetRetentionDays": "30",
"allowOpenAIToolUsage": "false",
"allowedVisionServices": "google-ai,openai,groq,xai,qwen,meta,anthropic",
"tokensPunishmentFactor": "0",
"serviceInfoAuthMode": "token",
"serverTitleUrl": "https://buy.jewproxy.tech"
}
}.claude, .codex, or opencode configs. back up what's there first if you already use these CLIs. .claude if it doesn't exist. dot-folders may be hidden in finder. create the folder: mkdir $HOME\.claude in powershell — explorer won't make dotfolders. .codex if it doesn't exist. dot-folders may be hidden in finder. create the folder: mkdir $HOME\.codex in powershell — explorer won't make dotfolders. ~/.config/opencode if it doesn't exist. the config uses this proxy as an OpenAI-compatible upstream. create the folder: mkdir $HOME\.config\opencode in powershell. if you run OpenCode in WSL, use the WSL path instead. model or the provider model list to use any model this proxy exposes.jewproxy = api access to many ai models, billed via one token. every app needs the same three values: a base url, an api key, and a model name.
claude opus 4.8, claude fable 5, or claude sonnet 5 in sillytavern, use the openrouter base url (next tab). that's the route most people miss.gemini 3.1, use the google-ai endpoint. same setup, different base url.three lines of config. follow them in order.
https://vip.jewproxy.tech/proxy/openrouter/v1 claude opus 4.8, claude fable 5, or claude sonnet 5 (or another paid openrouter model)show paid openrouter models). it loads the live list, so you can copy the exact name into your app to avoid typos. https://vip.jewproxy.tech/proxy/openrouter/v1claude opus 4.8 · claude fable 5 · claude sonnet 5 · or another from the dropdowna visual ui for both image and video generation. no api setup required.
https://img.jewproxy.tech the image api, taught as a process. run export TOKEN="..." once with your jewproxy token — every command below sends $TOKEN, never a vendor key. then pick the tab whose model id you have: the id format has already chosen the route. four steps per provider — pick, send, read, fail.
vendor/slug id — openai/gpt-image-2, black-forest-labs/flux.2-pro — routes to the normalized /images api. the prefix is the cue. this one route speaks for many vendors, so its fields are normalized too (step 02). pick a model below; the command rewrites itself.GET https://vip.jewproxy.tech/proxy/images/v1/models — the selector shows the common ones; the full list is live/images/v1/generations with normalized fields — prompt, negative_prompt, count, quality, image_size (0.5K–4K), aspect_ratio. size does not exist here; that's the native route's word. the answer is 202 accepted with an imgjob_ id — 202 is not success, it only means queued. phase two polls GET /images/v1/generations/$JOB_ID until the status stops being accepted. the script below does both.JOB_ID=$(curl --request POST \
--url https://vip.jewproxy.tech/proxy/images/v1/generations \
--header "Authorization: Bearer $TOKEN" \
--header "Content-Type: application/json" \
--data '{"model": "openai/gpt-image-2", "prompt": "a lighthouse at dawn, oil painting", "count": 1, "aspect_ratio": "1:1", "image_size": "1K", "quality": "auto"}' | jq -r .id)
while :; do
RESPONSE=$(curl --silent https://vip.jewproxy.tech/proxy/images/v1/generations/$JOB_ID \
--header "Authorization: Bearer $TOKEN")
STATUS=$(echo "$RESPONSE" | jq -r .status)
[ "$STATUS" = "accepted" ] || break
sleep 2
done
URL=$(echo "$RESPONSE" | jq -r '.images[0].url // empty')
if [ -z "$URL" ]; then echo "$RESPONSE" | jq -r .error.message >&2; exit 1; fi
echo "$URL"accepted, the poll is the verdict. on success the image is at images[0].url, hosted by the proxy, and the script prints it. on failure the script pushes .error.message to stderr and exits 1 — that message tells you which side failed.images[0].url — proxy-hosted, after the pollYou've exceeded your token quota for this model type. means wait for the reset or top up.error.code and error.type are always present on image routes — read them first, they name the failure before you guess./proxy/openrouter/v1/chat/completions cannot make images — only this /images route can.-image — gemini-3.1-flash-image, gemini-3-pro-image — takes the native google-ai route. same rule as the other tabs: the id format chooses the route, and here it chose this one. pick a model below; the command rewrites itself.GET https://vip.jewproxy.tech/proxy/google-ai/v1/models — the selector shows the common ones; the full list is live/v1beta/models/<id>:generateContent. the body is google-shaped: contents[] carries the prompt text, generationConfig.responseModalities asks for IMAGE, and imageConfig sets aspectRatio and imageSize. the bearer header is $TOKEN as always.curl --request POST \
--url https://vip.jewproxy.tech/proxy/google-ai/v1beta/models/gemini-3.1-flash-image:generateContent \
--header "Authorization: Bearer $TOKEN" \
--header "Content-Type: application/json" \
--data '{"contents": [{"role": "user", "parts": [{"text": "a lighthouse at dawn, oil painting"}]}], "generationConfig": {"responseModalities": ["IMAGE"], "imageConfig": {"aspectRatio": "1:1", "imageSize": "1K"}}}' \
| jq -r '.candidates[0].content.parts[] | select(.inlineData) | .inlineData.data' | head -n1 | base64 -d > image.pngcandidates[0].content.parts[].inlineData.data. the jq pipe in the command extracts that field and decodes it into image.png — you materialize the file yourself.image.png — no hosted url on this routeYou've exceeded your token quota for this model type. means wait for the reset or top up.error.code and error.type are always present on image routes — read them first, they name the failure before you guess.for local cli agents (claude code, codex, or opencode).
how to use)