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Same-model provider benchmark

AtlasCloud vs MiniMax: LLM provider comparison

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

AtlasCloud

31 indexed models

Headquarters
United States
Server regions
N/a
Model types
26 text, 5 video

MiniMax

9 indexed models

Headquarters
Singapore
Server regions
United States
Model types
8 text, 1 video

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

1.23× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$0.5989 / 1M

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

Most models available

31 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricAtlasCloudMiniMax
Typical 500-token response12.16 s13.73 s
Typical response start1.12 s1.49 s
Typical output speed45.0 tok/s40.0 tok/s
Blended token price$0.5989 / 1M$0.6 / 1M
Typical route uptime97.95%99.13%

Visual comparison

Price and performance charts

AtlasCloudMiniMax
500-token response by shared model

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

AtlasCloud 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.

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

Shared text models

3 exact models · newest first

ModelAtlasCloudMiniMax
MiniMax M3Winner · AtlasCloud

minimax/minimax-m3

AtlasCloud
Blended price
$0.6 / 1M
500-token response
12.16 s
Response starts
1.05 s
Output speed
45.0 tok/s
Route uptime
97.90%
Context
524.3K
Route
atlas-cloud/fp8
MiniMax
Blended price
$0.6 / 1M
500-token response
14.92 s
Response starts
2.42 s
Output speed
40.0 tok/s
Route uptime
99.38%
Context
524.3K
Route
minimax/fp8
MiniMax M2.7Winner · MiniMax

minimax/minimax-m2.7

AtlasCloud
Blended price
$0.6 / 1M
500-token response
15.96 s
Response starts
1.67 s
Output speed
35.0 tok/s
Route uptime
98.00%
Context
196.6K
Route
atlas-cloud/fp8
MiniMax
Blended price
$0.6 / 1M
500-token response
13.73 s
Response starts
1.23 s
Output speed
40.0 tok/s
Route uptime
98.88%
Context
204.8K
Route
minimax/fp8
MiniMax M2.5Winner · AtlasCloud

minimax/minimax-m2.5

AtlasCloud
Blended price
$0.5967 / 1M
500-token response
7.97 s
Response starts
1.12 s
Output speed
73.0 tok/s
Route uptime
N/a
Context
196.6K
Route
atlas-cloud/fp8
MiniMax
Blended price
$0.6 / 1M
500-token response
12.36 s
Response starts
1.49 s
Output speed
46.0 tok/s
Route uptime
N/a
Context
204.8K
Route
minimax/fp8

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.

AtlasCloud vs MiniMax analysis

How AtlasCloud and MiniMax compare for AI inference

AtlasCloud and MiniMax share 3 indexed text models, including MiniMax M3, MiniMax M2.7, MiniMax M2.5. AtlasCloud 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

AtlasCloud has 31 indexed models and lists no published server regions; MiniMax has 9 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.