Modal vs Moonshot AI: Token Pricing, Speed & Intelligence
Full comparison of Modal and Moonshot 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.
Moonshot AI
Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
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Live token pricing
Strengths and weaknesses
Modal
Moonshot 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.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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.
Moonshot AI FAQs
What is Kimi K3?
Kimi K3 is Moonshot AI's latest 1-trillion-parameter mixture-of-experts model, succeeding K2. It activates approximately 32B parameters per token and leads on coding and agentic benchmarks as of September 2026.
What is the difference between Kimi K3 and K2.6?
Kimi K3 is the latest generation with improved benchmark scores. Kimi K2.6 is a stable, battle-tested version suitable for production workloads that prioritise reliability over cutting-edge performance.
Is Kimi K3 open-weight?
Yes. Kimi K3 weights are publicly available on Hugging Face, making it one of the largest open-weight models available. Teams can self-host it on multi-GPU clusters or access it via the Moonshot API.
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.
Moonshot AI — Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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
Moonshot AI
- Kimi K3: 1T MoE open-weight model with strong coding and agentic benchmark scores
- Kimi K2.6 available as stable production option
- Competitive on agentic and tool-use benchmarks
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
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
Moonshot AI
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
- Fewer third-party integrations available
Provider category context
Modal is a inference api. Moonshot AI is a frontier lab, founded in 2023. Modal as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. Moonshot AI as a frontier lab trains and serves proprietary models with capabilities not available elsewhere.
How to choose between them
Choose Modal if you need managed hardware and autoscaling — no infrastructure to operate. Choose Moonshot AI if you need kimi k3: 1t moe open-weight model with strong coding and agentic benchmark scores. 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.