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Same-model provider benchmark

DigitalOcean vs Moonshot AI: LLM provider comparison

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

DigitalOcean

16 indexed models

Headquarters
N/a
Server regions
N/a
Model types
16 text

Moonshot AI

4 indexed models

Headquarters
Singapore
Server regions
Singapore
Model types
4 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

Moonshot AI

2.13× typical advantage

3 exact models with complete recent speed data

Lowest token cost

DigitalOcean

$3.1661 / 1M

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

Most models available

DigitalOcean

16 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricDigitalOcean5.0(1)Moonshot AI(0)
SpeedDigitalOcean46.38 sMoonshot AI22.62 s
TTFTDigitalOcean0.93 sMoonshot AI2.62 s
TPSDigitalOcean11.0 tok/sMoonshot AI25.0 tok/s
UptimeDigitalOcean98.62%Moonshot AI99.97%
BlendedDigitalOcean$3.1661 / 1MMoonshot AI$3.4556 / 1M
Green value Better comparable resultRed value Worse comparable result

Visual comparison

Price and performance charts

DigitalOceanMoonshot AI
500-token response by shared model

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

Moonshot AI 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.

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

Shared text models

3 exact models · newest first

ModelDigitalOcean5.0(1)Moonshot AI(0)
Kimi K3Winner · Moonshot AI
DigitalOcean
Speed— Worst comparable value
56.74 s
TTFT— Worst comparable value
6.74 s
TPS— Worst comparable value
10.0 tok/s
Uptime— Worst comparable value
89.21%
Context
1M
Route
digitalocean
Blended
$7 / 1M
Moonshot AI
Speed— Best comparable value
26.60 s
TTFT— Best comparable value
4.86 s
TPS— Best comparable value
23.0 tok/s
Uptime— Best comparable value
99.97%
Context
1M
Route
moonshotai/mxfp4
Blended
$7 / 1M
Kimi K2.6Winner · DigitalOcean
DigitalOcean
Speed— Best comparable value
9.57 s
TTFT— Best comparable value
0.80 s
TPS— Best comparable value
57.0 tok/s
Uptime— Best comparable value
100.00%
Context
262.1K
Route
digitalocean
Blended— Best comparable value
$1.5733 / 1M
Moonshot AI
Speed— Worst comparable value
22.62 s
TTFT— Worst comparable value
2.62 s
TPS— Worst comparable value
25.0 tok/s
Uptime— Worst comparable value
99.82%
Context
262.1K
Route
moonshotai/int4
Blended— Worst comparable value
$1.9667 / 1M
Kimi K2.5Winner · Moonshot AI
DigitalOcean
Speed— Worst comparable value
46.38 s
TTFT— Best comparable value
0.93 s
TPS— Worst comparable value
11.0 tok/s
Uptime— Worst comparable value
98.62%
Context
262.1K
Route
digitalocean
Blended— Best comparable value
$0.925 / 1M
Moonshot AI
Speed— Best comparable value
16.08 s
TTFT— Worst comparable value
1.37 s
TPS— Best comparable value
34.0 tok/s
Uptime— Best comparable value
100.00%
Context
262.1K
Route
moonshotai/int4
Blended— Worst comparable value
$1.4 / 1M

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.

DigitalOcean vs Moonshot AI analysis

How DigitalOcean and Moonshot AI compare for AI inference

DigitalOcean and Moonshot AI share 3 indexed text models, including Kimi K3, Kimi K2.6, Kimi K2.5. Moonshot AI 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

DigitalOcean has 16 indexed models and lists no published server regions; Moonshot AI has 4 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.

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