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feat(gemini): add gemini-3.1-flash-lite-image (Nano Banana 2 Lite) pricing
Adds cost-map entries for gemini-3.1-flash-lite-image, gemini/... and vertex_ai/... variants using Google's published Nano Banana 2 Lite pricing ($0.25/1M input, $1.50/1M text output, $30/1M image output -> ~$0.0336 per 1K image). Mirrors the existing gemini-3.1-flash-image entry. Without this, completion_cost() raises "model isn't mapped yet" and Vertex generateContent pass-through cost tracking silently logs $0.
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chiruno-9
Devin AI
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commit
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3 changed files with 296 additions and 0 deletions
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@ -17742,6 +17742,101 @@
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},
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"web_search_billing_unit": "per_query"
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},
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"gemini-3.1-flash-lite-image": {
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"input_cost_per_image": 0.00028,
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"input_cost_per_token": 2.5e-07,
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"litellm_provider": "vertex_ai-language-models",
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"max_input_tokens": 65536,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "image_generation",
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"output_cost_per_image": 0.0336,
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"output_cost_per_image_token": 3e-05,
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"output_cost_per_token": 1.5e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
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"supported_endpoints": [
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"/v1/chat/completions",
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"/v1/completions",
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"/v1/batch"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text",
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"image"
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],
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"supports_function_calling": false,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_vision": true
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},
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"gemini/gemini-3.1-flash-lite-image": {
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"rpm": 1000,
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"tpm": 4000000,
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"input_cost_per_image": 0.00028,
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"input_cost_per_token": 2.5e-07,
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"litellm_provider": "gemini",
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"max_input_tokens": 65536,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "image_generation",
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"output_cost_per_image": 0.0336,
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"output_cost_per_image_token": 3e-05,
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"output_cost_per_token": 1.5e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
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"supported_endpoints": [
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"/v1/chat/completions",
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"/v1/completions",
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"/v1/batch"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text",
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"image"
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],
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"supports_function_calling": false,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_vision": true
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},
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"vertex_ai/gemini-3.1-flash-lite-image": {
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"input_cost_per_image": 0.00028,
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"input_cost_per_token": 2.5e-07,
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"litellm_provider": "vertex_ai-language-models",
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"max_input_tokens": 65536,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "image_generation",
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"output_cost_per_image": 0.0336,
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"output_cost_per_image_token": 3e-05,
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"output_cost_per_token": 1.5e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
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"supported_endpoints": [
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"/v1/chat/completions",
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"/v1/completions",
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"/v1/batch"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text",
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"image"
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],
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"supports_function_calling": false,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_vision": true
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},
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"gemini-3.1-flash-image-preview": {
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"input_cost_per_image": 0.00056,
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"input_cost_per_token": 5e-07,
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@ -17782,6 +17782,101 @@
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},
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"web_search_billing_unit": "per_query"
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},
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"gemini-3.1-flash-lite-image": {
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"input_cost_per_image": 0.00028,
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"input_cost_per_token": 2.5e-07,
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"litellm_provider": "vertex_ai-language-models",
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"max_input_tokens": 65536,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "image_generation",
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"output_cost_per_image": 0.0336,
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"output_cost_per_image_token": 3e-05,
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"output_cost_per_token": 1.5e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
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"supported_endpoints": [
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"/v1/chat/completions",
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"/v1/completions",
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"/v1/batch"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text",
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"image"
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],
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"supports_function_calling": false,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_vision": true
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},
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"gemini/gemini-3.1-flash-lite-image": {
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"rpm": 1000,
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"tpm": 4000000,
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"input_cost_per_image": 0.00028,
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"input_cost_per_token": 2.5e-07,
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"litellm_provider": "gemini",
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"max_input_tokens": 65536,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "image_generation",
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"output_cost_per_image": 0.0336,
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"output_cost_per_image_token": 3e-05,
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"output_cost_per_token": 1.5e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
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"supported_endpoints": [
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"/v1/chat/completions",
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"/v1/completions",
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"/v1/batch"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text",
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"image"
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],
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"supports_function_calling": false,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_vision": true
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},
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"vertex_ai/gemini-3.1-flash-lite-image": {
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"input_cost_per_image": 0.00028,
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"input_cost_per_token": 2.5e-07,
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"litellm_provider": "vertex_ai-language-models",
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"max_input_tokens": 65536,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "image_generation",
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"output_cost_per_image": 0.0336,
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"output_cost_per_image_token": 3e-05,
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"output_cost_per_token": 1.5e-06,
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"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
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"supported_endpoints": [
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"/v1/chat/completions",
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"/v1/completions",
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"/v1/batch"
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],
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"supported_modalities": [
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"text",
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"image"
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],
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"supported_output_modalities": [
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"text",
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"image"
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],
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"supports_function_calling": false,
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"supports_prompt_caching": true,
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"supports_response_schema": true,
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"supports_system_messages": true,
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"supports_vision": true
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},
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"gemini-3.1-flash-image": {
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"input_cost_per_image": 0.00056,
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"input_cost_per_token": 5e-07,
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106
tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py
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106
tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py
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@ -0,0 +1,106 @@
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"""Pricing entry for ``gemini-3.1-flash-lite-image`` (Google's Nano Banana 2 Lite).
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Google publishes: $0.25/1M input, $1.50/1M text output, and $30/1M image-output
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tokens for the Lite image model (https://cloud.google.com/vertex-ai/generative-ai/pricing).
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A 1K image is ~1120 output image tokens => ~$0.0336 / image.
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Without this entry, ``completion_cost`` raises "model isn't mapped yet" and Vertex
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generateContent pass-through cost tracking silently logs $0. These tests pin the
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values in both the primary price map and the ``litellm/`` backup, and verify
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``get_model_info`` / ``completion_cost`` surface them.
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"""
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import json
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import os
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import sys
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sys.path.insert(0, os.path.abspath("../..")) # Adds the parent directory to the system path
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import litellm
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from litellm import completion_cost
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from litellm.types.utils import CompletionTokensDetailsWrapper, ModelResponse, Usage
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VARIANTS = [
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"gemini-3.1-flash-lite-image",
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"gemini/gemini-3.1-flash-lite-image",
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"vertex_ai/gemini-3.1-flash-lite-image",
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]
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EXPECTED = {
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"input_cost_per_token": 2.5e-07,
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"output_cost_per_token": 1.5e-06,
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"output_cost_per_image_token": 3e-05,
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"mode": "image_generation",
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}
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def _load_json(path: str) -> dict:
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with open(path, encoding="utf-8") as f:
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return json.load(f)
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def _backup_path() -> str:
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return os.path.join(
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os.path.dirname(litellm.__file__),
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"model_prices_and_context_window_backup.json",
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)
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def _main_path() -> str:
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return os.path.join(
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os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json"
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)
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class TestGeminiFlashLiteImagePricingData:
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"""Both price maps must carry Google's published Nano Banana 2 Lite costs."""
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def test_present_in_both_maps(self):
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main = _load_json(_main_path())
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backup = _load_json(_backup_path())
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for key in VARIANTS:
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for label, data in (("main", main), ("backup", backup)):
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assert key in data, f"{key} missing from {label} JSON"
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entry = data[key]
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for field, value in EXPECTED.items():
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assert entry[field] == value, f"{key} {field} in {label}: {entry.get(field)} != {value}"
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def test_image_output_pricing_consistent(self):
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"""1120 image-output tokens * output_cost_per_image_token == output_cost_per_image."""
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backup = _load_json(_backup_path())
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entry = backup["gemini-3.1-flash-lite-image"]
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assert round(1120 * entry["output_cost_per_image_token"], 6) == entry["output_cost_per_image"]
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class TestGeminiFlashLiteImageModelInfo:
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"""``get_model_info`` and ``completion_cost`` must report the new costs."""
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def test_get_model_info_and_cost(self):
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original = litellm.model_cost
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try:
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litellm.model_cost = _load_json(_backup_path())
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info = litellm.get_model_info("gemini-3.1-flash-lite-image")
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assert info["input_cost_per_token"] == EXPECTED["input_cost_per_token"]
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assert info["output_cost_per_token"] == EXPECTED["output_cost_per_token"]
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# A 1K image => 1120 output image tokens => ~$0.0336 (billed at
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# output_cost_per_image_token, not the text output rate).
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resp = ModelResponse()
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resp.model = "gemini-3.1-flash-lite-image"
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resp.usage = Usage(
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prompt_tokens=7,
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completion_tokens=1120,
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total_tokens=1127,
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completion_tokens_details=CompletionTokensDetailsWrapper(
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image_tokens=1120, text_tokens=0
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),
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)
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cost = completion_cost(
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completion_response=resp,
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model="gemini-3.1-flash-lite-image",
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custom_llm_provider="vertex_ai",
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)
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# 1120 * 3e-5 (image) + 7 * 2.5e-7 (input) == 0.03360175
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assert abs(cost - 0.03360175) < 1e-6, f"unexpected cost {cost}"
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finally:
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litellm.model_cost = original
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