Skip to content

Same-model provider benchmark

Moonshot AI vs Venice: LLM provider comparison

Compare Moonshot AI and Venice on 3 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

Moonshot AI

3 indexed models

Headquarters
Singapore
Server regions
Singapore
Model types
3 text

Venice

29 indexed models

Headquarters
United States
Server regions
United States
Model types
29 text

At a glance

Metric winners

There is no overall score. Each winner answers one specific question using only directly comparable data.

Fastest on shared models

2.86× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$1.6244 / 1M

3 exact models using a 1K-input/500-output mix

Most models available

29 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricMoonshot AIVenice
Typical 500-token response25.58 s8.94 s
Typical response start2.58 s1.43 s
Typical output speed22.0 tok/s65.0 tok/s
Blended token price$1.7778 / 1M$1.6244 / 1M
Typical route uptime99.83%99.22%

Visual comparison

Price and performance charts

Moonshot AIVenice
500-token response by shared model

Estimated seconds using recent median response-start and output-speed data. Lower is better.

Venice has the lower typical same-model response ratio across 3 measured models.

Blended token price by shared model

USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.

Venice has the lower typical price ratio across 3 priced shared models.

Shared text models

3 exact models · newest first

ModelMoonshot AIVenice
Kimi K2.7 CodeWinner · Venice

moonshotai/kimi-k2.7-code

Moonshot AI
Blended price
$1.9667 / 1M
500-token response
25.58 s
Response starts
2.86 s
Output speed
22.0 tok/s
Route uptime
100.00%
Context
262.1K
Route
moonshotai/int4
Venice
Blended price
$1.6667 / 1M
500-token response
8.94 s
Response starts
1.25 s
Output speed
65.0 tok/s
Route uptime
N/a
Context
256K
Route
venice/int4
Kimi K2.6Winner · Venice

moonshotai/kimi-k2.6

Moonshot AI
Blended price
$1.9667 / 1M
500-token response
26.39 s
Response starts
2.58 s
Output speed
21.0 tok/s
Route uptime
99.83%
Context
262.1K
Route
moonshotai/int4
Venice
Blended price
$1.6667 / 1M
500-token response
12.06 s
Response starts
1.43 s
Output speed
47.0 tok/s
Route uptime
99.22%
Context
256K
Route
venice/int4
Kimi K2.5Winner · Venice

moonshotai/kimi-k2.5

Moonshot AI
Blended price
$1.4 / 1M
500-token response
17.35 s
Response starts
1.22 s
Output speed
31.0 tok/s
Route uptime
100.00%
Context
262.1K
Route
moonshotai/int4
Venice
Blended price
$1.54 / 1M
500-token response
5.81 s
Response starts
1.45 s
Output speed
114.5 tok/s
Route uptime
N/a
Context
256K
Route
venice

A per-model winner combines blended price and estimated 500-token response time with equal proportional weight. Ties and rows missing either measurement receive no badge. Comparison data calculated . Values use one deterministic route per provider and model; missing measurements remain visible as N/a.

Moonshot AI vs Venice analysis

How Moonshot AI and Venice compare for AI inference

Moonshot AI and Venice share 3 indexed text models, including Kimi K2.7 Code, Kimi K2.6, Kimi K2.5. Venice has the stronger typical response-time result on the directly measured set.

Same-model speed evidence

3 shared models currently have complete response-start and output-speed measurements on both providers. The speed comparison uses per-model ratios before taking the median, so naturally faster model catalogs do not improve the result.

Token pricing on one workload

3 shared models have complete input and output prices on both providers. Prices use the same 1,000-input/500-output-token mix and are normalized to one million tokens for readability.

Catalog and deployment differences

Moonshot AI has 3 indexed models and lists 1 published server region; Venice has 29 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.