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MiniMax vs TensorX: Token Pricing, Speed & Intelligence

Full comparison of MiniMax and TensorX — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.

MiniMax

MiniMax M3 — 1M context frontier model available on Together AI and Amazon Bedrock

MiniMax is a Chinese AI company offering the MiniMax M-series of large language models. MiniMax M3 is the latest generation, supporting a 1M token context window and designed for enterprise chat, long-document analysis, and agentic workflows. M3 is now available on Together AI and Amazon Bedrock, significantly expanding its global accessibility.

ChatLong-document analysisAgentic workflowsEnterprise AIAWS-native workloads
Proprietary models

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 inferencePrivacy-sensitive workloadsGeographic diversity
Open-weight hostHosts open weights

Key metric comparison

Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths and weaknesses

MiniMax

1M token context window on MiniMax M3
Available on Together AI and Amazon Bedrock
Competitive pricing for long-context workloads
Strong multilingual performance
Agentic workflow support
Smaller international developer community
Less third-party tooling than OpenAI

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)
USD pricing — no currency conversion for international customers
Small model catalog
Prepaid credits only — no post-pay billing
Newer provider — limited third-party benchmark transparency

Key differentiators

MiniMax

MiniMax M3 combines a 1M token context window with broad availability (Together AI, Amazon Bedrock) — making it one of the most accessible long-context models for global enterprise customers.

TensorX

EU inference with nodes in Dublin, Helsinki, and Paris, zero data retention, and prompt caching on GLM-5.3.

Frequently asked questions

MiniMax FAQs

What is MiniMax M3?

MiniMax M3 is MiniMax's latest frontier model, supporting a 1M token context window. It is designed for enterprise chat, long-document analysis, and agentic tasks with competitive pricing — available on Together AI and Amazon Bedrock. Check api.together.ai/models for current rates.

Where can I access MiniMax M3?

MiniMax M3 is available via the MiniMax API directly, through Together AI's inference platform, and on Amazon Bedrock — making it accessible without a separate MiniMax account for AWS and Together AI customers.

Is MiniMax available internationally?

Yes. The MiniMax API is accessible globally, and M3 is also available through Together AI and Amazon Bedrock for international customers who prefer not to create a direct MiniMax account.

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

MiniMax — MiniMax M3 — 1M context frontier model available on Together AI and Amazon Bedrock

MiniMax is a Chinese AI company offering the MiniMax M-series of large language models. MiniMax M3 is the latest generation, supporting a 1M token context window and designed for enterprise chat, long-document analysis, and agentic workflows. M3 is now available on Together AI and Amazon Bedrock, significantly expanding its global accessibility.

MiniMax M3 combines a 1M token context window with broad availability (Together AI, Amazon Bedrock) — making it one of the most accessible long-context models for global enterprise customers.

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

MiniMax

  • 1M token context window on MiniMax M3
  • Available on Together AI and Amazon Bedrock
  • Competitive pricing for long-context workloads
  • Smaller international developer community
  • Less third-party tooling than OpenAI

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

MiniMax

  • Smaller international developer community
  • Less third-party tooling than OpenAI

TensorX

  • Small model catalog
  • Prepaid credits only — no post-pay billing
  • Newer provider — limited third-party benchmark transparency

Provider category context

MiniMax is a frontier lab, founded in 2021. TensorX is a inference api, founded in 2024. MiniMax as a frontier lab trains and serves its own proprietary models. TensorX as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.

How to choose between them

Choose MiniMax if you need 1m token context window on minimax m3. Choose TensorX if you need EU inference. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.