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Same-model provider benchmark

DeepInfra vs Moonshot AI: LLM provider comparison

Compare DeepInfra 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.

DeepInfra

87 indexed models

Headquarters
United States
Server regions
N/a
Model types
67 text, 15 embeddings, 5 speech

Moonshot AI

3 indexed models

Headquarters
Singapore
Server regions
Singapore
Model types
3 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

DeepInfra

1.61× typical advantage

3 exact models with complete recent speed data

Lowest token cost

DeepInfra

$1.4589 / 1M

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

Most models available

DeepInfra

87 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricDeepInfraMoonshot AI
Typical 500-token response15.92 s25.58 s
Typical response start0.93 s2.58 s
Typical output speed33.0 tok/s22.0 tok/s
Blended token price$1.4589 / 1M$1.7778 / 1M
Typical route uptime99.96%100.00%

Visual comparison

Price and performance charts

DeepInfraMoonshot AI
500-token response by shared model

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

DeepInfra 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.

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

Shared text models

3 exact models · newest first

ModelDeepInfraMoonshot AI
Kimi K2.7 CodeWinner · DeepInfra

moonshotai/kimi-k2.7-code

DeepInfra
Blended price
$1.66 / 1M
500-token response
15.92 s
Response starts
0.77 s
Output speed
33.0 tok/s
Route uptime
100.00%
Context
262.1K
Route
deepinfra/fp4
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
Kimi K2.6Winner · DeepInfra

moonshotai/kimi-k2.6

DeepInfra
Blended price
$1.6667 / 1M
500-token response
12.84 s
Response starts
0.93 s
Output speed
42.0 tok/s
Route uptime
99.94%
Context
262.1K
Route
deepinfra/fp4
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
Kimi K2.5Winner · DeepInfra

moonshotai/kimi-k2.5

DeepInfra
Blended price
$1.05 / 1M
500-token response
16.56 s
Response starts
0.93 s
Output speed
32.0 tok/s
Route uptime
99.96%
Context
262.1K
Route
deepinfra/fp4
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

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.

DeepInfra vs Moonshot AI analysis

How DeepInfra and Moonshot AI compare for AI inference

DeepInfra and Moonshot AI share 3 indexed text models, including Kimi K2.7 Code, Kimi K2.6, Kimi K2.5. DeepInfra 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

DeepInfra has 87 indexed models and lists no published server regions; Moonshot AI has 3 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.

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