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Model infoModelsSnapshot: 15 Oct 2026

Moonshot AI / Kimi / Kimi k1.5 research

Kimi k1.5 Short-CoT

Multimodal research model that transfers longer reasoning into shorter responses through long-to-short training.

restrictedClosed weightsText generationReasoningVision
Official documentation (opens in a new tab)Reviewed 14 Oct 2026
6 of 19 specification fields collected
Context windowNot collectedAwaiting source review
Maximum outputNot collectedAwaiting source review
Input billingNot collectedMoonshot research
Output billingNot collectedNo inferred price

Specifications

Provider-documented facts. “Not collected” means we have not verified that field yet, not that the feature is unsupported.

Context / input limit
Not collected
Maximum output
Not collected
Tool calling
Not collected
Structured output
Not collected
Streaming
Not collected
Batch processing
Not collected
Fine-tuning
Not collected
Knowledge cutoff
Not collected
Release date
Not collected
Parameters
Not collected
Architecture
Not collected
Languages
Not collected
Context caching
Not collected
Published training method
Long-to-short transfer; the paper studies model merging, shortest rejection sampling, DPO and reinforcement learningSource for Published training method (opens in a new tab)
Training context length
Training reaches 128K tokensSource for Training context length (opens in a new tab)

Research training context, not a verified public API input or output limit.

trainingLanguages
English, ChineseSource for trainingLanguages (opens in a new tab)

Named language pretraining domains, not an exhaustive supported-language contract.

Access & pricing

Prices belong to an endpoint and billing tier. A provider listing does not guarantee access for every account or region.

API model ID
Not collected

Research identifier, not a verified public API selector.

Access
Research release. No current public model endpoint, model-weight download or price is established for this exact variant.
Regions
Historical native platform or research access only; no current regional availability is asserted.

Endpoint pricing has not been collected. Check the provider for applicable rates.

Data & enterprise terms

Retention, training use, regional processing and enterprise terms have not been summarized for this endpoint.

Caching

Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.

01 / INITIAL INPUTProcess & retain

The system processes eligible input and retains reusable state.

02 / LATER REQUESTCheck available state

Reuse depends on matching input, session state and the cache policy.

03 / CACHE HITReuse & generate

Reusing state can reduce repeated work. Any charges follow the billing rules below.

Moonshot research

Not verified
How it works
Not collected
Minimum reusable input
Not collected
Lifetime & refresh
Not collected
How to enable it
Not collected
Usage fields
Not collected
Billing rules
Not collected
Limits & data boundaries
Not collected

No verified cache rate for this endpoint. An empty rate is not a zero charge.

What the measurements say

AIME 2024 Pass@1

Moonshot AI · provider-reported
60.8%

Provider-reported results in Table 3 of arXiv 2501.12599v3 for the short-CoT variant. Dataset split and metric are part of the benchmark name. Not an independent rerun or a claim about a current API.

Evaluation source (opens in a new tab)

MATH-500 exact match

Moonshot AI · provider-reported
94.6%

Provider-reported results in Table 3 of arXiv 2501.12599v3 for the short-CoT variant. Dataset split and metric are part of the benchmark name. Not an independent rerun or a claim about a current API.

Evaluation source (opens in a new tab)

LiveCodeBench Pass@1

Moonshot AI · provider-reported
47.3%

Provider-reported results in Table 3 of arXiv 2501.12599v3 for the short-CoT variant. Dataset split and metric are part of the benchmark name. Not an independent rerun or a claim about a current API.

Evaluation source (opens in a new tab)

MathVista-Test Pass@1

Moonshot AI · provider-reported
70.1%

Provider-reported results in Table 3 of arXiv 2501.12599v3 for the short-CoT variant. Dataset split and metric are part of the benchmark name. Not an independent rerun or a claim about a current API.

Evaluation source (opens in a new tab)

Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.

The record behind the model

Why it is included

Inferred from the named proprietary API configuration or internal trained research model in official documentation, checked against the developer’s published-weight inventory. Exact weights for this release were not found. Current open-weight Kimi releases are excluded; API branding alone does not establish a separate checkpoint.

Classification evidence (opens in a new tab)

Lifecycle dates

Initial research paper submission22 Jan 2025Source (opens in a new tab)

Sources reviewed