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Same-model provider benchmark

Google Vertex vs Groq: LLM provider comparison

Compare Google Vertex and Groq 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

Groq

11 indexed models

Headquarters
United States
Server regions
N/a
Model types
9 text, 2 transcription

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

Groq

4.60× typical advantage

3 exact models with complete recent speed data

Lowest token cost

Groq

$0.3233 / 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 VertexGroq
Typical 500-token response6.83 s3.26 s
Typical response start0.42 s0.20 s
Typical output speed78.0 tok/s168.0 tok/s
Blended token price$0.3575 / 1M$0.3233 / 1M
Typical route uptime100.00%99.88%

Visual comparison

Price and performance charts

Google VertexGroq
500-token response by shared model

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

Groq 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.

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

Shared text models

4 exact models · newest first

ModelGoogle VertexGroq
gpt-oss-120bWinner · Groq

openai/gpt-oss-120b

Google Vertex
Blended price
$0.18 / 1M
500-token response
6.83 s
Response starts
0.42 s
Output speed
78.0 tok/s
Route uptime
95.91%
Context
131.1K
Route
google-vertex/global
Groq
Blended price
$0.3 / 1M
500-token response
1.48 s
Response starts
0.20 s
Output speed
391.0 tok/s
Route uptime
99.99%
Context
131.1K
Route
groq
gpt-oss-20b

openai/gpt-oss-20b

Google Vertex
Blended price
$0.13 / 1M
500-token response
N/a
Response starts
N/a
Output speed
N/a
Route uptime
N/a
Context
131.1K
Route
google-vertex/us-central1
Groq
Blended price
$0.15 / 1M
500-token response
2.51 s
Response starts
0.41 s
Output speed
238.0 tok/s
Route uptime
99.94%
Context
131.1K
Route
groq
Llama 4 ScoutWinner · Groq

meta-llama/llama-4-scout

Google Vertex
Blended price
$0.4 / 1M
500-token response
71.91 s
Response starts
0.48 s
Output speed
7.0 tok/s
Route uptime
100.00%
Context
1.3M
Route
google-vertex/us-east5
Groq
Blended price
$0.1867 / 1M
500-token response
10.61 s
Response starts
0.20 s
Output speed
48.0 tok/s
Route uptime
99.88%
Context
131.1K
Route
groq
Llama 3.3 70B InstructWinner · Groq

meta-llama/llama-3.3-70b-instruct

Google Vertex
Blended price
$0.72 / 1M
500-token response
6.50 s
Response starts
0.25 s
Output speed
80.0 tok/s
Route uptime
100.00%
Context
128K
Route
google-vertex
Groq
Blended price
$0.6567 / 1M
500-token response
3.26 s
Response starts
0.29 s
Output speed
168.0 tok/s
Route uptime
99.51%
Context
131.1K
Route
groq

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 Groq analysis

How Google Vertex and Groq compare for AI inference

Google Vertex and Groq share 4 indexed text models, including gpt-oss-120b, gpt-oss-20b, Llama 4 Scout, Llama 3.3 70B Instruct. Groq 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

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; Groq has 11 models and lists no regions. Verify data residency, privacy terms, limits, and production latency directly before choosing.

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