> ## Documentation Index > Fetch the complete documentation index at: https://prices.voicegateway.dev/llms.txt > Use this file to discover all available pages before exploring further. # HuggingFace (novita) LLM pricing > 61 LLM model(s) from HuggingFace (novita), with input and output token rates. Priced from `input_mtok` and `output_mtok`, US dollars per 1,000,000 tokens. Source of truth: [`prices/providers/huggingface_novita.yml`](https://github.com/mahimailabs/voice-prices/blob/main/prices/providers/huggingface_novita.yml). | Model | Name | Input \$ / Mtok | Output \$ / Mtok | Context | | --------------------------------------------------- | -------------------------------------- | --------------: | ---------------: | --------: | | `MiniMaxAI/MiniMax-M1-80k` | MiniMax-M1-80k | \$0.55 | \$2.2 | 1,000,000 | | `MiniMaxAI/MiniMax-M2` | MiniMax-M2 | \$0.3 | \$1.2 | 204,800 | | `NousResearch/Hermes-2-Pro-Llama-3-8B` | Hermes-2-Pro-Llama-3-8B | \$0.14 | \$0.14 | 8,192 | | `Qwen/Qwen2.5-72B-Instruct` | Qwen2.5-72B-Instruct | \$0.38 | \$0.4 | 32,000 | | `Qwen/Qwen3-235B-A22B` | Qwen3-235B-A22B | \$0.2 | \$0.8 | 40,960 | | `Qwen/Qwen3-235B-A22B-Instruct-2507` | Qwen3-235B-A22B-Instruct-2507 | \$0.09 | \$0.58 | 131,072 | | `Qwen/Qwen3-235B-A22B-Thinking-2507` | Qwen3-235B-A22B-Thinking-2507 | \$0.3 | \$3 | 131,072 | | `Qwen/Qwen3-30B-A3B` | Qwen3-30B-A3B | \$0.09 | \$0.45 | 40,960 | | `Qwen/Qwen3-32B` | Qwen3-32B | \$0.1 | \$0.45 | 40,960 | | `Qwen/Qwen3-Coder-480B-A35B-Instruct` | Qwen3-Coder-480B-A35B-Instruct | \$0.3 | \$1.3 | 262,144 | | `Qwen/Qwen3-Coder-Next` | Qwen3-Coder-Next | \$0.2 | \$1.5 | 262,144 | | `Qwen/Qwen3-Next-80B-A3B-Instruct` | Qwen3-Next-80B-A3B-Instruct | \$0.15 | \$1.5 | 131,072 | | `Qwen/Qwen3-Next-80B-A3B-Thinking` | Qwen3-Next-80B-A3B-Thinking | \$0.15 | \$1.5 | 131,072 | | `Qwen/Qwen3-VL-235B-A22B-Instruct` | Qwen3-VL-235B-A22B-Instruct | \$0.3 | \$1.5 | 131,072 | | `Qwen/Qwen3-VL-235B-A22B-Thinking` | Qwen3-VL-235B-A22B-Thinking | \$0.98 | \$3.95 | 131,072 | | `Qwen/Qwen3-VL-30B-A3B-Instruct` | Qwen3-VL-30B-A3B-Instruct | \$0.2 | \$0.7 | 131,072 | | `Qwen/Qwen3-VL-30B-A3B-Thinking` | Qwen3-VL-30B-A3B-Thinking | \$0.2 | \$1 | 131,072 | | `Qwen/Qwen3-VL-8B-Instruct` | Qwen3-VL-8B-Instruct | \$0.08 | \$0.5 | 131,072 | | `Qwen/Qwen3.5-122B-A10B` | Qwen3.5-122B-A10B | \$0.4 | \$3.2 | 262,144 | | `Qwen/Qwen3.5-27B` | Qwen3.5-27B | \$0.3 | \$2.4 | 262,144 | | `Qwen/Qwen3.5-35B-A3B` | Qwen3.5-35B-A3B | \$0.25 | \$2 | 262,144 | | `Qwen/Qwen3.5-397B-A17B` | Qwen3.5-397B-A17B | \$0.6 | \$3.6 | 262,144 | | `Sao10K/L3-70B-Euryale-v2.1` | L3-70B-Euryale-v2.1 | \$1.48 | \$1.48 | 8,192 | | `Sao10K/L3-8B-Lunaris-v1` | L3-8B-Lunaris-v1 | \$0.05 | \$0.05 | 8,192 | | `Sao10K/L3-8B-Stheno-v3.2` | L3-8B-Stheno-v3.2 | \$0.05 | \$0.05 | 8,192 | | `XiaomiMiMo/MiMo-V2-Flash` | MiMo-V2-Flash | \$0.1 | \$0.3 | 262,144 | | `alpindale/WizardLM-2-8x22B` | WizardLM-2-8x22B | \$0.62 | \$0.62 | 65,535 | | `baidu/ERNIE-4.5-21B-A3B-PT` | ERNIE-4.5-21B-A3B-PT | \$0.07 | \$0.28 | 120,000 | | `baidu/ERNIE-4.5-300B-A47B-Base-PT` | ERNIE-4.5-300B-A47B-Base-PT | \$0.28 | \$1.1 | 123,000 | | `baidu/ERNIE-4.5-VL-28B-A3B-PT` | ERNIE-4.5-VL-28B-A3B-PT | \$0.14 | \$0.56 | 30,000 | | `baidu/ERNIE-4.5-VL-424B-A47B-Base-PT` | ERNIE-4.5-VL-424B-A47B-Base-PT | \$0.42 | \$1.25 | 123,000 | | `deepseek-ai/DeepSeek-Prover-V2-671B` | DeepSeek-Prover-V2-671B | \$0.7 | \$2.5 | 160,000 | | `deepseek-ai/DeepSeek-R1` | DeepSeek-R1 | \$0.7 | \$2.5 | 64,000 | | `deepseek-ai/DeepSeek-R1-Distill-Llama-70B` | DeepSeek-R1-Distill-Llama-70B | \$0.8 | \$0.8 | 8,192 | | `deepseek-ai/DeepSeek-V3` | DeepSeek-V3 | \$0.4 | \$1.3 | 64,000 | | `deepseek-ai/DeepSeek-V3-0324` | DeepSeek-V3-0324 | \$0.27 | \$1.12 | 163,840 | | `deepseek-ai/DeepSeek-V3.1` | DeepSeek-V3.1 | \$0.27 | \$1 | 131,072 | | `deepseek-ai/DeepSeek-V3.2` | DeepSeek-V3.2 | \$0.269 | \$0.4 | 163,840 | | `deepseek-ai/DeepSeek-V3.2-Exp` | DeepSeek-V3.2-Exp | \$0.27 | \$0.41 | 163,840 | | `meta-llama/Llama-3.1-8B-Instruct` | Llama-3.1-8B-Instruct | \$0.02 | \$0.05 | 16,384 | | `meta-llama/Llama-3.3-70B-Instruct` | Llama-3.3-70B-Instruct | \$0.135 | \$0.4 | 131,072 | | `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | Llama-4-Maverick-17B-128E-Instruct-FP8 | \$0.27 | \$0.85 | 1,048,576 | | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | Llama-4-Scout-17B-16E-Instruct | \$0.18 | \$0.59 | 131,072 | | `meta-llama/Meta-Llama-3-70B-Instruct` | Meta-Llama-3-70B-Instruct | \$0.51 | \$0.74 | 8,192 | | `meta-llama/Meta-Llama-3-8B-Instruct` | Meta-Llama-3-8B-Instruct | \$0.04 | \$0.04 | 8,192 | | `moonshotai/Kimi-K2-Instruct` | Kimi-K2-Instruct | \$0.57 | \$2.3 | 131,072 | | `moonshotai/Kimi-K2-Instruct-0905` | Kimi-K2-Instruct-0905 | \$0.6 | \$2.5 | 262,144 | | `moonshotai/Kimi-K2-Thinking` | Kimi-K2-Thinking | \$0.6 | \$2.5 | 262,144 | | `moonshotai/Kimi-K2.5` | Kimi-K2.5 | \$0.6 | \$3 | 262,144 | | `openai/gpt-oss-120b` | gpt-oss-120b | \$0.05 | \$0.25 | 131,072 | | `openai/gpt-oss-20b` | gpt-oss-20b | \$0.04 | \$0.15 | 131,072 | | `zai-org/AutoGLM-Phone-9B-Multilingual` | AutoGLM-Phone-9B-Multilingual | \$0.035 | \$0.138 | 65,536 | | `zai-org/GLM-4-32B-0414` | GLM-4-32B-0414 | \$0.55 | \$1.66 | 32,000 | | `zai-org/GLM-4.5` | GLM-4.5 | \$0.6 | \$2.2 | 131,072 | | `zai-org/GLM-4.5-Air` | GLM-4.5-Air | \$0.13 | \$0.85 | 131,072 | | `zai-org/GLM-4.5V` | GLM-4.5V | \$0.6 | \$1.8 | 65,536 | | `zai-org/GLM-4.6` | GLM-4.6 | \$0.55 | \$2.2 | 204,800 | | `zai-org/GLM-4.6V-Flash` | GLM-4.6V-Flash | \$0.3 | \$0.9 | 131,072 | | `zai-org/GLM-4.7` | GLM-4.7 | \$0.6 | \$2.2 | 204,800 | | `zai-org/GLM-4.7-Flash` | GLM-4.7-Flash | \$0.07 | \$0.4 | 200,000 | | `zai-org/GLM-5` | GLM-5 | \$1 | \$3.2 | 202,800 |