DekaLLM
7 indexed models
- Headquarters
Indonesia- Server regions
- Indonesia
- Model types
- 7 text
Same-model provider benchmark
DekaLLM vs SiliconFlow: LLM provider comparison
Compare DekaLLM and SiliconFlow on 5 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.
SiliconFlow
36 indexed models
- Headquarters
Singapore- Server regions
United States- Model types
- 35 text, 1 embeddings
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
1.72× typical advantage
5 exact models with complete recent speed data
Lowest token cost
$0.1752 / 1M
5 exact models using a 1K-input/500-output mix
Most models available
36 models
Complete catalog coverage across all indexed modalities
Shared-model benchmark summary
| Metric | DekaLLM | SiliconFlow |
|---|---|---|
| Typical 500-token response | 11.47 s | 21.58 s |
| Typical response start | 0.90 s | 1.57 s |
| Typical output speed | 48.0 tok/s | 25.0 tok/s |
| Blended token price | $0.1752 / 1M | $0.262 / 1M |
| Typical route uptime | 98.29% | 97.82% |
Visual comparison
Price and performance charts
SiliconFlowEstimated seconds using recent median response-start and output-speed data. Lower is better.
DekaLLM has the lower typical same-model response ratio across 5 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 5 priced shared models.
| Model | DekaLLM | SiliconFlow |
|---|---|---|
 Qwen3.6 35B A3BWinner · DekaLLM qwen/qwen3.6-35b-a3b | DekaLLM
| SiliconFlow
|
 Gemma 4 26B A4BWinner · DekaLLM google/gemma-4-26b-a4b-it | DekaLLM
| SiliconFlow
|
 gpt-oss-120bWinner · DekaLLM openai/gpt-oss-120b | DekaLLM
| SiliconFlow
|
gpt-oss-20bWinner · DekaLLM openai/gpt-oss-20b | DekaLLM
| SiliconFlow
|
Qwen3 30B A3B Instruct 2507Winner · DekaLLM qwen/qwen3-30b-a3b-instruct-2507 | DekaLLM
| SiliconFlow
|
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 SiliconFlow analysis
How DekaLLM and SiliconFlow compare for AI inference
DekaLLM and SiliconFlow share 5 indexed text models, including Qwen3.6 35B A3B, Gemma 4 26B A4B, gpt-oss-120b, gpt-oss-20b, Qwen3 30B A3B Instruct 2507. DekaLLM has the stronger typical response-time result on the directly measured set.
Same-model speed evidence
5 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
5 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; SiliconFlow has 36 models and lists 1 region. 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 SiliconFlow.
