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Same-model provider benchmark

Cloudflare vs Inceptron: LLM provider comparison

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

Cloudflare

12 indexed models

Headquarters
United States
Server regions
N/a
Model types
12 text

Inceptron

4 indexed models

Headquarters
Sweden
Server regions
Finland
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

2.76× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$1.6056 / 1M

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

Most models available

12 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricCloudflareInceptron
Typical 500-token response10.35 s34.33 s
Typical response start0.79 s2.16 s
Typical output speed52.0 tok/s16.0 tok/s
Blended token price$2.1111 / 1M$1.6056 / 1M
Typical route uptime99.87%95.65%

Visual comparison

Price and performance charts

CloudflareInceptron
500-token response by shared model

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

Cloudflare 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.

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

Shared text models

3 exact models · newest first

ModelCloudflareInceptron
GLM 5.2Winner · Cloudflare

z-ai/glm-5.2

Cloudflare
Blended price
$2.4 / 1M
500-token response
10.35 s
Response starts
0.74 s
Output speed
52.0 tok/s
Route uptime
99.85%
Context
262.1K
Route
cloudflare
Inceptron
Blended price
$1.5933 / 1M
500-token response
64.66 s
Response starts
2.16 s
Output speed
8.0 tok/s
Route uptime
94.28%
Context
1M
Route
inceptron/fp4
Kimi K2.7 CodeWinner · Inceptron

moonshotai/kimi-k2.7-code

Cloudflare
Blended price
$1.9667 / 1M
500-token response
9.91 s
Response starts
1.58 s
Output speed
60.0 tok/s
Route uptime
N/a
Context
262.1K
Route
cloudflare
Inceptron
Blended price
$1.6467 / 1M
500-token response
11.33 s
Response starts
1.13 s
Output speed
49.0 tok/s
Route uptime
99.49%
Context
262.1K
Route
inceptron/int4
Kimi K2.6Winner · Cloudflare

moonshotai/kimi-k2.6

Cloudflare
Blended price
$1.9667 / 1M
500-token response
12.42 s
Response starts
0.79 s
Output speed
43.0 tok/s
Route uptime
99.90%
Context
262.1K
Route
cloudflare
Inceptron
Blended price
$1.5767 / 1M
500-token response
34.33 s
Response starts
3.08 s
Output speed
16.0 tok/s
Route uptime
97.02%
Context
262.1K
Route
inceptron/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.

Cloudflare vs Inceptron analysis

How Cloudflare and Inceptron compare for AI inference

Cloudflare and Inceptron share 3 indexed text models, including GLM 5.2, Kimi K2.7 Code, Kimi K2.6. Cloudflare 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

Cloudflare has 12 indexed models and lists no published server regions; Inceptron has 4 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.