Evan
2fa48bfdcb
- Merge unique models from duplicate providers into their hand-written counterparts and remove the duplicates: - alibaba (tongyi-deepresearch) → qwen - amazon (nova-2-lite, nova-micro, nova-premier) → bedrock - bytedance (ui-tars) → volcengine - nvidia (nemotron-3-nano, nemotron-3-super, etc.) → nvidia-nim - rekaai (reka-flash-3) → reka - Set correct official API base_url for providers with public APIs: arcee-ai, inception, morph, reka, upstage - Set key_required=false for 20 providers only accessible through hosting platforms (no public API) - Update sync-pricing.py with SKIP_DUPLICATES, PROVIDER_API mapping, and default key_required=false for future auto-generated providers
149 lines
3.2 KiB
TOML
149 lines
3.2 KiB
TOML
# meta-llama — auto-generated from OpenRouter API
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[provider]
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id = "meta-llama"
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display_name = "Meta Llama"
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api_key_env = "META_LLAMA_API_KEY"
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base_url = ""
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key_required = false
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[[models]]
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id = "llama-3-70b-instruct"
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display_name = "Meta: Llama 3 70B Instruct"
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tier = "smart"
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context_window = 8192
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max_output_tokens = 8000
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input_cost_per_m = 0.51
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output_cost_per_m = 0.74
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supports_streaming = true
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[[models]]
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id = "llama-3-8b-instruct"
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display_name = "Meta: Llama 3 8B Instruct"
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tier = "fast"
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context_window = 8192
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max_output_tokens = 16384
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input_cost_per_m = 0.03
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output_cost_per_m = 0.04
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supports_streaming = true
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[[models]]
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id = "llama-3.1-70b-instruct"
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display_name = "Meta: Llama 3.1 70B Instruct"
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tier = "fast"
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context_window = 131072
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max_output_tokens = 16384
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input_cost_per_m = 0.4
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output_cost_per_m = 0.4
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supports_streaming = true
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[[models]]
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id = "llama-3.1-8b-instruct"
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display_name = "Meta: Llama 3.1 8B Instruct"
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tier = "fast"
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context_window = 16384
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max_output_tokens = 16384
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input_cost_per_m = 0.02
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output_cost_per_m = 0.05
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supports_streaming = true
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[[models]]
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id = "llama-3.2-11b-vision-instruct"
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display_name = "Meta: Llama 3.2 11B Vision Instruct"
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tier = "fast"
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context_window = 131072
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max_output_tokens = 16384
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input_cost_per_m = 0.049
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output_cost_per_m = 0.049
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supports_streaming = true
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[[models]]
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id = "llama-3.2-1b-instruct"
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display_name = "Meta: Llama 3.2 1B Instruct"
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tier = "fast"
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context_window = 60000
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max_output_tokens = 15000
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input_cost_per_m = 0.027
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output_cost_per_m = 0.2
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supports_streaming = true
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[[models]]
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id = "llama-3.2-3b-instruct"
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display_name = "Meta: Llama 3.2 3B Instruct"
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tier = "fast"
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context_window = 80000
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max_output_tokens = 16384
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input_cost_per_m = 0.051
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output_cost_per_m = 0.34
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supports_streaming = true
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[[models]]
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id = "llama-3.2-3b-instruct:free"
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display_name = "Meta: Llama 3.2 3B Instruct (free)"
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tier = "fast"
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context_window = 131072
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max_output_tokens = 16384
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input_cost_per_m = 0.0
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output_cost_per_m = 0.0
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supports_streaming = true
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[[models]]
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id = "llama-3.3-70b-instruct"
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display_name = "Meta: Llama 3.3 70B Instruct"
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tier = "fast"
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context_window = 131072
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max_output_tokens = 16384
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input_cost_per_m = 0.1
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output_cost_per_m = 0.32
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supports_streaming = true
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[[models]]
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id = "llama-3.3-70b-instruct:free"
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display_name = "Meta: Llama 3.3 70B Instruct (free)"
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tier = "fast"
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context_window = 65536
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max_output_tokens = 16384
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input_cost_per_m = 0.0
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output_cost_per_m = 0.0
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supports_streaming = true
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[[models]]
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id = "llama-4-maverick"
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display_name = "Meta: Llama 4 Maverick"
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tier = "fast"
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context_window = 1048576
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max_output_tokens = 16384
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input_cost_per_m = 0.15
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output_cost_per_m = 0.6
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supports_streaming = true
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[[models]]
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id = "llama-4-scout"
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display_name = "Meta: Llama 4 Scout"
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tier = "fast"
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context_window = 327680
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max_output_tokens = 16384
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input_cost_per_m = 0.08
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output_cost_per_m = 0.3
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supports_streaming = true
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[[models]]
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id = "llama-guard-3-8b"
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display_name = "Llama Guard 3 8B"
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tier = "fast"
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context_window = 131072
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max_output_tokens = 16384
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input_cost_per_m = 0.02
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output_cost_per_m = 0.06
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supports_streaming = true
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[[models]]
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id = "llama-guard-4-12b"
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display_name = "Meta: Llama Guard 4 12B"
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tier = "fast"
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context_window = 163840
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max_output_tokens = 16384
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input_cost_per_m = 0.18
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output_cost_per_m = 0.18
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supports_streaming = true
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