Modal vs Together AI: Token Pricing, Speed & Intelligence
Full comparison of Modal and Together AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Modal
Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Together AI
MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro, gpt-oss-120B — plus GB200/B200 GPU clusters
Together AI offers hosted inference for open-weight models, GPU clusters, provisioned throughput and fine-tuning. Compare listed rates and check the provider for current models, availability and terms.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Modal
Together AI
Key differentiators
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
An open-weight model catalog — MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro, gpt-oss-120B — with GPU clusters (GB200/B200), Provisioned Throughput, and a Serverless Training API.
Frequently asked questions
Modal FAQs
What models does Modal offer on Shared Endpoints?
Modal Shared Endpoints currently serve 10 models: GLM 5.3 Flash NVFP4, DeepSeek V4.1 Flash 1017, DeepSeek V4.1 Flash, GLM 5.3 Flash, Muse Spark Open, GLM 5.3, Inkling NVFP4, Qwen3.8-Max, Qwen3.8-Max VL, and Kimi K3. Use the model table above for per-model pricing and capability details. Source: Modal's published Shared Endpoints catalog, re-read 2026-10-31.
How much does Modal charge per 1M tokens?
Modal Shared Endpoints input prices range from $0.15/1M (GLM 5.3 Flash NVFP4) to $3.00/1M (Kimi K3). All models include cached-input pricing. See the model table for full per-model rates.
Does Modal offer prompt caching?
Yes. All 10 models in the Modal Shared Endpoints catalog have published cached-input pricing, ranging from $0.005/1M (DeepSeek V4.1 Flash 1017) to $0.30/1M (Kimi K3). Depending on the model, the cached-input rate is 77.5% to 98% below the standard input rate. Source: Modal's published Shared Endpoints rates, re-read 2026-10-31.
Together AI FAQs
What new models did Together AI add in 2026?
Together AI expanded its catalog with MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro 0813, gpt-oss-120B, Muse Glimmer 30B, and the full Qwen3.x family. Check api.together.ai/models for current pricing.
How much does Together AI cost?
Together AI uses per-token pricing across its model catalog. Check api.together.ai/models for current rates on MiniMax M3, DeepSeek V4 Pro, GLM-5.3, Llama 3.3 70B, and other models.
Does Together AI offer GPU compute?
Yes. Together AI offers GPU clusters with H100, H200, B200, and GB200/GB300 NVL72 (contact sales). Check together.ai/pricing for current GPU compute rates.
Provider resources
Modal — Managed inference API — 10 shared-endpoint models with per-token pricing
Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.
Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.
Together AI — MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro, gpt-oss-120B — plus GB200/B200 GPU clusters
Together AI offers hosted inference for open-weight models, GPU clusters, provisioned throughput and fine-tuning. Compare listed rates and check the provider for current models, availability and terms.
An open-weight model catalog — MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro, gpt-oss-120B — with GPU clusters (GB200/B200), Provisioned Throughput, and a Serverless Training API.
Key strengths compared
Modal
- Managed hardware and autoscaling — no infrastructure to operate
- Published per-token rates including cached-input pricing
- OpenAI-style per-token billing across all 10 models
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
Together AI
- Open-model catalog — MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro, gpt-oss-120B, Qwen3.x, Llama 4
- GB200/B200/H200/H100 GPU clusters with preemptible and reserved pricing
- Provisioned Throughput (PTUs) for SLA-backed capacity
- No proprietary frontier models
- Throughput lower than Groq/Cerebras for speed-critical apps
Key weaknesses compared
Modal
- Small fixed catalog of 10 models
- Model-specific concurrency limits on shared pools
- Prices are published rates re-read 2026-10-31, not continuously collected
Together AI
- No proprietary frontier models
- Throughput lower than Groq/Cerebras for speed-critical apps
- Fine-tuning adds complexity vs. pure inference providers
Provider category context
Modal is a inference api. Together AI is a inference api, founded in 2022. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Modal and Together AI host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.