Moonshot AIKimi Dev 72B
Developed by Moonshot AI, Kimi Dev 72B is an open-source chat model optimized for software engineering tasks, particularly issue resolution. It achieves a state-of-the-art result among open-source models on the SWE-bench Verified benchmark with a score of 60.4%, demonstrating its strength in this area.
Input
Output
Context
131K
Max Output
512
Parameters
72.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Kimi Dev 72B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Kimi Dev 72B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Kimi Dev 72B advertises a context window of 131,072 tokens, with a maximum output of 512 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Kimi Dev 72B is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Kimi Dev 72B accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Kimi Dev 72B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Kimi Dev 72B is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 71.0 GB at INT8 for the weights and a default context, from the catalog's 72.7B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 14, 2026
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