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Same-model provider benchmark

Google Vertex vs Phala: LLM provider comparison

Compare Google Vertex and Phala on 4 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

Google Vertex

49 indexed models

Headquarters
United States
Server regions
europe-west1 · Belgiumeurope-west4 · NetherlandsGlobalus-central1 · United Statesus-east5 · United Statesus-south1 · United Statesus-west2 · United States
Model types
36 text, 6 image, 3 video, 2 embeddings, 1 speech, 1 transcription

Phala

18 indexed models

Headquarters
United States
Server regions
N/a
Model types
18 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

Google Vertex

1.67× typical advantage

4 exact models with complete recent speed data

Lowest token cost

Google Vertex

$0.5942 / 1M

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

Most models available

Google Vertex

49 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricGoogle Vertex(0)Phala(0)
Typical 500-token response12.72 s19.81 s
Typical response start1.06 s1.47 s
Typical output speed62.0 tok/s27.5 tok/s
Blended token price$0.5942 / 1M$0.7608 / 1M
Typical route uptime98.22%99.59%

Visual comparison

Price and performance charts

Google VertexPhala
500-token response by shared model

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

Google Vertex has the lower typical same-model response ratio across 4 measured models.

Blended token price by shared model

USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.

Google Vertex has the lower typical price ratio across 4 priced shared models.

Shared text models

4 exact models · newest first

ModelGoogle Vertex(0)Phala(0)
GLM 4.7Winner · Google Vertex

z-ai/glm-4.7

Google Vertex
Blended price
$1.1333 / 1M
500-token response
4.68 s
Response starts
0.52 s
Output speed
120.0 tok/s
Route uptime
100.00%
Context
200K
Route
google-vertex
Phala
Blended price
$1.6667 / 1M
500-token response
22.20 s
Response starts
2.20 s
Output speed
25.0 tok/s
Route uptime
N/a
Context
131.1K
Route
phala
DeepSeek V3.2Winner · Google Vertex

deepseek/deepseek-v3.2

Google Vertex
Blended price
$0.9333 / 1M
500-token response
32.30 s
Response starts
1.04 s
Output speed
16.0 tok/s
Route uptime
99.05%
Context
163.8K
Route
google-vertex
Phala
Blended price
$1 / 1M
500-token response
66.86 s
Response starts
4.36 s
Output speed
8.0 tok/s
Route uptime
99.46%
Context
163.8K
Route
phala
gpt-oss-120bWinner · Google Vertex

openai/gpt-oss-120b

Google Vertex
Blended price
$0.18 / 1M
500-token response
12.44 s
Response starts
1.08 s
Output speed
44.0 tok/s
Route uptime
97.39%
Context
131.1K
Route
google-vertex/global
Phala
Blended price
$0.3 / 1M
500-token response
17.41 s
Response starts
0.74 s
Output speed
30.0 tok/s
Route uptime
99.72%
Context
131.1K
Route
phala
gpt-oss-20bWinner · Phala

openai/gpt-oss-20b

Google Vertex
Blended price
$0.13 / 1M
500-token response
12.99 s
Response starts
6.74 s
Output speed
80.0 tok/s
Route uptime
N/a
Context
131.1K
Route
google-vertex/us-central1
Phala
Blended price
$0.0767 / 1M
500-token response
10.28 s
Response starts
0.67 s
Output speed
52.0 tok/s
Route uptime
99.73%
Context
131.1K
Route
phala

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.

Google Vertex vs Phala analysis

How Google Vertex and Phala compare for AI inference

Google Vertex and Phala share 4 indexed text models, including GLM 4.7, DeepSeek V3.2, gpt-oss-120b, gpt-oss-20b. Google Vertex has the stronger typical response-time result on the directly measured set.

Same-model speed evidence

4 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

4 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

Google Vertex has 49 indexed models and lists 7 published server regions; Phala has 18 models and lists no regions. Verify data residency, privacy terms, limits, and production latency directly before choosing.

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