DeepInfra
87 indexed models
- Headquarters
United States
- Server regions
- N/a
- Model types
- 67 text, 15 embeddings, 5 speech
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Same-model provider benchmark
Compare DeepInfra and Google Vertex on 12 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.
87 indexed models
49 indexed models
At a glance
There is no overall score. Each winner answers one specific question using only directly comparable data.
Fastest on shared models
1.43× typical advantage
10 exact models with complete recent speed data
Lowest token cost
$0.3237 / 1M
12 exact models using a 1K-input/500-output mix
Most models available
87 models
Complete catalog coverage across all indexed modalities
| Metric | DeepInfra | Google Vertex |
|---|---|---|
| Typical 500-token response | 17.94 s | 9.39 s |
| Typical response start | 0.44 s | 0.53 s |
| Typical output speed | 31.0 tok/s | 61.5 tok/s |
| Blended token price | $0.3237 / 1M | $0.5889 / 1M |
| Typical route uptime | 99.62% | 99.97% |
Visual comparison
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 10 measured models.
USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.
DeepInfra has the lower typical price ratio across 12 priced shared models.
| Model | DeepInfra | Google Vertex |
|---|---|---|
Gemma 4 26B A4BWinner · DeepInfra google/gemma-4-26b-a4b-it | DeepInfra
| Google Vertex
|
GLM 4.7Winner · Google Vertex z-ai/glm-4.7 | DeepInfra
| Google Vertex
|
deepseek/deepseek-v3.2 | DeepInfra
| Google Vertex
|
Qwen3 Next 80B A3B InstructWinner · Google Vertex qwen/qwen3-next-80b-a3b-instruct | DeepInfra
| Google Vertex
|
DeepSeek V3.1Winner · Google Vertex deepseek/deepseek-chat-v3.1 | DeepInfra
| Google Vertex
|
gpt-oss-120bWinner · DeepInfra openai/gpt-oss-120b | DeepInfra
| Google Vertex
|
openai/gpt-oss-20b | DeepInfra
| Google Vertex
|
Qwen3 Coder 480B A35BWinner · DeepInfra qwen/qwen3-coder | DeepInfra
| Google Vertex
|
Qwen3 235B A22B Instruct 2507Winner · DeepInfra qwen/qwen3-235b-a22b-2507 | DeepInfra
| Google Vertex
|
Llama 4 MaverickWinner · DeepInfra meta-llama/llama-4-maverick | DeepInfra
| Google Vertex
|
Llama 4 ScoutWinner · DeepInfra meta-llama/llama-4-scout | DeepInfra
| Google Vertex
|
Llama 3.3 70B InstructWinner · Google Vertex meta-llama/llama-3.3-70b-instruct | DeepInfra
| Google Vertex
|
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 Google Vertex analysis
DeepInfra and Google Vertex share 12 indexed text models, including Gemma 4 26B A4B, GLM 4.7, DeepSeek V3.2, Qwen3 Next 80B A3B Instruct, DeepSeek V3.1. Google Vertex has the stronger typical response-time result on the directly measured set.
10 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.
12 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.
DeepInfra has 87 indexed models and lists no published server regions; Google Vertex has 49 models and lists 7 regions. Verify data residency, privacy terms, limits, and production latency directly before choosing.
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