What affects cost
- Selected serverless model and modality
- Input, cached input, and output volume
- Batch eligibility and processing mode
- Dedicated endpoint hardware and running time
- Image, video, audio, embedding, or reranking units
LLM API
Together AI API pricing
Together AI prices serverless models per token, dedicated endpoints per reserved hardware minute, and selected Batch workloads at discounted rates.
Decision summary: Consider Together AI when its open-model catalog and serverless, Batch, or dedicated deployment options match the workload and operating model.
Pricing overview
Billing varies by serverless model, cached-input support, Batch eligibility, modality, or dedicated endpoint hardware.
| Plan or model | Pricing posture | Decision note |
|---|---|---|
| DeepSeek V4 Pro | $1.74 / 1M input; $0.20 cached input; $3.48 / 1M output | Official Together serverless chat-model rate. |
| MiniMax M3 | $0.30 / 1M input; $0.06 cached input; $1.20 / 1M output | Official Together serverless chat-model rate. |
| Kimi K2.7 Code | $0.95 / 1M input; $0.19 cached input; $4.00 / 1M output | Official Together serverless chat-model rate. |
| GLM-5.2 | $1.40 / 1M input; $0.26 cached input; $4.40 / 1M output | Official Together serverless chat-model rate. |
| Kimi K2.6 | $1.20 / 1M input; $0.20 cached input; $4.50 / 1M output | Official Together serverless chat-model rate. |
| gpt-oss-120B | $0.15 / 1M input; $0.60 / 1M output | Official Together serverless chat-model rate; no cached-input value is shown in the tracked row. |
Lower-cost options from the same provider
- gpt-oss-20B or other lower-rate models when task quality is sufficient
- Cached-input pricing on supported chat models
- Batch processing for selected asynchronous serverless workloads
Alternative providers or products
- GroqCloud
- OpenRouter
- Direct model-provider APIs
- Self-hosted inference when hardware and operations are included
Best for
- teams evaluating hosted open-model inference
- workloads that can compare serverless and Batch processing
- buyers considering dedicated endpoints at sustained scale
Not ideal for
- budgets that mix serverless and dedicated billing units
- workloads assuming every model supports cached input
- real-time traffic modeled with Batch discounts
FAQ
Questions teams ask before choosing
How does Together AI serverless pricing differ from dedicated endpoints?
Serverless text models are generally billed by input and output tokens, while dedicated endpoints are billed by reserved hardware time while the endpoint is running.
Can every Together AI model use cached-input or Batch discounts?
No. Cached-input and Batch support are model- and workflow-specific. Use the current model catalog and Batch documentation before budgeting a discount.