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MoonshotAI: Kimi K2 Thinking provider comparison

moonshotai/kimi-k2-thinking

Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...

Endpoints:
2
Context:
262.1K
Input:
text
Output:
text
Compare this model in a set →

At a glance

Comparison winners

Based on 1,000 input and 500 output tokens using the latest published median speed data.

Best cost/speed trade-off

Google Vertexgoogle-vertex

$1.2333 / 1M tokens

3.19 s estimated response

Cheapest

NovitaAInovita/bf16

$1.2333 / 1M tokens

$0.00185 for the sample request

Fastest

Google Vertexgoogle-vertex

3.19 s estimated response

$1.2333 / 1M tokens

Visual comparison

Price and performance charts

Compare published endpoint pricing and estimated response time visually.

Effective price by endpoint

USD per 1 million tokens for input and output. Lower is better.

NovitaAI has the lowest estimated cost for 1,000 input and 500 output tokens at $0.00185.

Estimated cost vs. response time

Based on 1,000 input and 500 output tokens. Cost is shown in USD per 1 million tokens. Lower and further left is better; the single best cost/speed trade-off appears at full opacity.

2 providers have complete pricing and speed data. Google Vertex is closest to the ideal combination of lowest cost and fastest response.

Server-rendered comparison

Provider endpoints

Prompt and completion prices are effective USD per token as published by OpenRouter. Missing speed data never removes an endpoint.

Endpoint comparison for MoonshotAI: Kimi K2 Thinking
Provider / routeCurrent pricing ↗Speed measurementsEndpoint details
Provider / route
NovitaAInovita/bf16
Cheapest
Current pricing
Input price
$0.6 / 1M tokens
Output price
$2.5 / 1M tokens
Sample cost
$0.00185
Speed measurements
500-token response
13.38 s
Response starts
1.18 s
Output speed
41.0 tok/s
Endpoint details
Uptime (30 min)
99.49%
Context
262.1K
Model format
bf16
Max output
100.4K
15 parameters

frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p

Provider / route
Google Vertexgoogle-vertex
FastestBest cost/speed trade-off
Current pricing
Input price
$0.6 / 1M tokens
Output price
$2.5 / 1M tokens
Sample cost
$0.00185
Speed measurements
500-token response
3.19 s
Response starts
0.39 s
Output speed
178.5 tok/s
Endpoint details
Uptime (30 min)
100.00%
Context
262.1K
Model format
unknown
Max output
262.1K
17 parameters

frequency_penalty, include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p

Endpoint data fetched .

Kimi K2 Thinking deployment guide

How to choose a Kimi K2 Thinking inference provider

Kimi K2 Thinking is indexed here as a text model by MoonshotAI, with 2 provider endpoints from NovitaAI, Google Vertex. The comparison preserves each exact OpenRouter routing tag so pricing and performance observations can be connected to the route an application would actually request.

Match the endpoint to the workload

The model publishes a 262.1K-token context window. It accepts text input and returns text output. 17 distinct supported parameters appear across the listed routes. Confirm limits on the specific endpoint rather than assuming every host exposes the same configuration.

Compare the real request economics

NovitaAI currently has the lowest estimated cost for the standard 1,000-input/500-output-token sample at $0.00185. Input-heavy and output-heavy applications can produce a different result, so review both per-million-token prices in the endpoint table.

Balance response start and generation speed

Google Vertex currently has the shortest estimated 500-token response at 3.19 seconds. Google Vertex is the single endpoint closest to the current ideal cost/speed combination. Recent observations can change, so validate finalists with your own prompts.