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Same-model provider benchmark

DeepInfra vs MiniMax: LLM provider comparison

Compare DeepInfra 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.

DeepInfra

87 indexed models

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

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

MiniMax

1.23× typical advantage

3 exact models with complete recent speed data

Lowest token cost

DeepInfra

$0.5278 / 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

MetricDeepInfraMiniMax
Typical 500-token response16.88 s13.73 s
Typical response start0.97 s1.49 s
Typical output speed31.0 tok/s40.0 tok/s
Blended token price$0.5278 / 1M$0.6 / 1M
Typical route uptime98.55%99.13%

Visual comparison

Price and performance charts

DeepInfraMiniMax
500-token response by shared model

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

MiniMax 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

ModelDeepInfraMiniMax
MiniMax M3Winner · MiniMax

minimax/minimax-m3

DeepInfra
Blended price
$0.6 / 1M
500-token response
24.78 s
Response starts
0.97 s
Output speed
21.0 tok/s
Route uptime
98.34%
Context
524.3K
Route
deepinfra/bf16
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

DeepInfra
Blended price
$0.5 / 1M
500-token response
16.88 s
Response starts
0.75 s
Output speed
31.0 tok/s
Route uptime
98.77%
Context
196.6K
Route
deepinfra/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 · DeepInfra

minimax/minimax-m2.5

DeepInfra
Blended price
$0.4833 / 1M
500-token response
11.21 s
Response starts
1.01 s
Output speed
49.0 tok/s
Route uptime
100.00%
Context
196.6K
Route
deepinfra/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.

DeepInfra vs MiniMax analysis

How DeepInfra and MiniMax compare for AI inference

DeepInfra and MiniMax share 3 indexed text models, including MiniMax M3, MiniMax M2.7, MiniMax M2.5. MiniMax 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; MiniMax has 9 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.

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