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Models / Kimi K3.1 Pro Highspeed

Kimi K3.1 Pro Highspeed

Kimi K3.1 series family · Provider: moonshot-ai · Status: active

Kimi K3.1 Pro Highspeed is a Kimi K3.1 series model developed by moonshot-ai, a Chinese AI company headquartered in 13F, Building 1, JD Technology Building, 76 Zhichun Road, Haidian District, Beijing, China with a 1,048,576-token context window and open weights.

Last verified: · Review cadence: 90 days · Next review:
Key facts for Kimi K3.1 Pro Highspeed
Key factValue
Model IDkimi-k3.1-pro-highspeed
ArchitectureHigh-throughput serving tier of Kimi K3.1 Pro; same model, faster inference path
Context window 1,048,576 tokens
Open weightsYes
LicenseUnknown
Self-hostingYes
API availableYes
API pricing ¥26 input / ¥136 output per 1M tokens (CNY) · provider pricing page
Cloud providersMoonshot AI Platform

Capabilities

Capabilities of Kimi K3.1 Pro Highspeed
CapabilitySupported
ReasoningYes
CodingYes
MathUnknown
ChineseUnknown
EnglishUnknown
MultilingualUnknown
VisionYes
AudioUnknown
VideoUnknown
Tool callingYes
Function callingUnknown
Structured outputYes
Agent capabilityYes
RAGUnknown
Computer useUnknown

Known limitations

Kimi K3.1 Pro Highspeed is the fast serving tier of K3.1 Pro: the same model and context window, priced at ¥26 per 1M input and ¥136 per 1M output — exactly double the standard tier — with roughly 2–3x the throughput.

The trade

StandardHighspeed
Input /1M¥13¥26
Output /1M¥68¥136
Throughputbaseline~2–3x
Cost per token1x2x
Cost per second of work1x~0.7–1.0x

The per-token price doubles while throughput rises 2–3x, so the cost of a fixed amount of work stays roughly flat and the cost of wall-clock time falls. Highspeed is therefore a latency purchase, not a throughput optimisation: it pays when a human is waiting, and does nothing for batch pipelines.

When it is worth it

  • Interactive coding assistants and review tools where time-to-first-token is visible.
  • Agent loops with many short calls, where per-call overhead dominates total latency.
  • Anything with a service-level agreement measured in seconds.

It is not worth it for nightly batch jobs, evaluation runs or document processing — the standard tier produces the same output for half the token cost, and the queue does not care how fast each call returns.

What to verify

The multiplier is stated as “2–3x” rather than a fixed figure, so the effective cost per unit of work depends on your workload’s concurrency. At 2x throughput for 2x price, Highspeed is cost-neutral per unit of work and strictly better on latency; at 3x, it is also cheaper per unit of work. Measure on your own traffic before standardising on it.

Provider

Pricing

Related models (same family)

API

Model family timeline

Release timeline for the Kimi K3.1 series family
ModelReleasedStatus
Kimi K3.1 Pro Highspeed (this page) — active
Kimi K3.1 Pro — active

Data interpretation

Evidence types for Kimi K3.1 Pro Highspeed
FieldValueEvidence type
Context window1,048,576 tokensOfficial
ArchitectureHigh-throughput serving tier of Kimi K3.1 Pro; same model, faster inference pathOfficial
Open weightsYesOfficial
API pricing ¥26 / ¥136 per 1M tokens (CNY) Official
Family position2 models in the Kimi K3.1 series familyChina AI Hub analysis

Evidence types: Official = vendor documentation, pricing pages or model cards. Vendor-reported = benchmark scores published by the vendor. China AI Hub analysis = derived from the database itself. See the sourcing policy.

Sources

Confidence and source hierarchy per the sourcing policy. Facts change; check the source before relying on this page.

What is the context window of Kimi K3.1 Pro Highspeed?

Kimi K3.1 Pro Highspeed has a 1,048,576-token context window.

Is Kimi K3.1 Pro Highspeed open weight?

Yes — Kimi K3.1 Pro Highspeed weights are openly available.

How much does Kimi K3.1 Pro Highspeed cost through the API?

¥26 per 1M input tokens and ¥136 per 1M output tokens (CNY).

Where does China AI Hub get its Kimi K3.1 Pro Highspeed data?

From 2 sources (official pages first), last verified 2026-11-05. Benchmark scores are labeled by source type; see the sourcing policy for details.