DekaLLM
7 indexed models
- Headquarters
Indonesia- Server regions
- Indonesia
- Model types
- 7 text
Same-model provider benchmark
DekaLLM vs Google Vertex: LLM provider comparison
Compare DekaLLM and Google Vertex on 3 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 · Belgium
europe-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
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
1.43× typical advantage
3 exact models with complete recent speed data
Lowest token cost
$0.0987 / 1M
3 exact models using a 1K-input/500-output mix
Most models available
49 models
Complete catalog coverage across all indexed modalities
Shared-model benchmark summary
Visual comparison
Price and performance charts
Google VertexEstimated seconds using recent median response-start and output-speed data. Lower is better.
Google Vertex has the lower typical same-model response ratio across 3 measured models.
USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.
DekaLLM has the lower typical price ratio across 3 priced shared models.
| Model | DekaLLM | Google Vertex |
|---|---|---|
 Gemma 4 26B A4BWinner · DekaLLM google/gemma-4-26b-a4b-it | DekaLLM
| Google Vertex
|
 gpt-oss-120bWinner · DekaLLM openai/gpt-oss-120b | DekaLLM
| Google Vertex
|
gpt-oss-20bWinner · Google Vertex openai/gpt-oss-20b | DekaLLM
| 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.
DekaLLM vs Google Vertex analysis
How DekaLLM and Google Vertex compare for AI inference
DekaLLM and Google Vertex share 3 indexed text models, including Gemma 4 26B A4B, gpt-oss-120b, gpt-oss-20b. Google Vertex 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
DekaLLM has 7 indexed models and lists 1 published server region; Google Vertex has 49 models and lists 7 regions. Verify data residency, privacy terms, limits, and production latency directly before choosing.
Related same-model benchmarks
Compare with other providers
Explore qualified alternatives with the most shared measured models. Recommendations include comparisons for both DekaLLM and Google Vertex.
