Modal vs TensorX: Token Pricing, Speed & Intelligence
Full comparison of Modal and TensorX — 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.
TensorX
EU inference — Dublin, Helsinki, Paris — GLM-5.3, Kimi K3, zero data retention
TensorX is an EU inference provider with nodes in Dublin, Helsinki, and Paris. It offers GLM-5.3, Kimi K3, and GLM-5.3 Flash with zero data retention and prepaid credits. USD pricing. Suited for EU workloads that need geographic diversity within Europe and a zero-retention policy.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Modal
TensorX
Key differentiators
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.
TensorX FAQs
What is TensorX?
TensorX is an EU inference provider with nodes in Dublin, Helsinki, and Paris. It offers GLM-5.3, Kimi K3, and GLM-5.3 Flash with zero data retention and prepaid credits. See tensorx.ai/pricing for current rates.
Does TensorX retain my data?
No. TensorX operates a zero data retention policy.
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.
TensorX — EU inference — Dublin, Helsinki, Paris — GLM-5.3, Kimi K3, zero data retention
TensorX is an EU inference provider with nodes in Dublin, Helsinki, and Paris. It offers GLM-5.3, Kimi K3, and GLM-5.3 Flash with zero data retention and prepaid credits. USD pricing. Suited for EU workloads that need geographic diversity within Europe and a zero-retention policy.
EU inference with nodes in Dublin, Helsinki, and Paris, zero data retention, and prompt caching on GLM-5.3.
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
TensorX
- EU nodes in Dublin, Helsinki, and Paris — geographic diversity
- Zero data retention policy
- Prompt caching on GLM-5.3 (cached $0.44/1M vs $1.75/1M standard)
- Small model catalog
- Prepaid credits only — no post-pay billing
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
TensorX
- Small model catalog
- Prepaid credits only — no post-pay billing
- Newer provider — limited third-party benchmark transparency
Provider category context
Modal is a inference api. TensorX is a inference api, founded in 2024. 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 TensorX 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.