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Same-model provider benchmark

DekaLLM vs Venice: LLM provider comparison

Compare DekaLLM and Venice on 3 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

DekaLLM

8 indexed models

Headquarters
Indonesia
Server regions
Indonesia
Model types
8 text

Venice

30 indexed models

Headquarters
United States
Server regions
United States
Model types
30 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

1.24× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$0.5207 / 1M

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

Most models available

30 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricDekaLLM(0)Venice(0)
SpeedDekaLLM12.53 sVenice9.77 s
TTFTDekaLLM1.06 sVenice0.68 s
TPSDekaLLM44.0 tok/sVenice55.0 tok/s
UptimeDekaLLM95.49%Venice99.71%
BlendedDekaLLM$0.5207 / 1MVenice$0.6511 / 1M
Green value Better comparable resultRed value Worse comparable result

Visual comparison

Price and performance charts

DekaLLMVenice
500-token response by shared model

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

Venice 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.

DekaLLM has the lower typical price ratio across 3 priced shared models.

Shared text models

3 exact models · newest first

ModelDekaLLM(0)Venice(0)
Qwen3.6 35B A3BWinner · Venice
DekaLLM
Speed— Worst comparable value
3.62 s
TTFT— Worst comparable value
0.62 s
TPS— Worst comparable value
167.0 tok/s
Uptime— Worst comparable value
96.07%
Context
262.1K
Route
dekallm
Blended— Best comparable value
$0.42 / 1M
Venice
Speed— Best comparable value
2.91 s
TTFT— Best comparable value
0.57 s
TPS— Best comparable value
214.0 tok/s
Uptime— Best comparable value
100.00%
Context
256K
Route
venice/fp8
Blended— Worst comparable value
$0.4333 / 1M
Qwen3.6 27BWinner · Venice
DekaLLM
Speed— Worst comparable value
42.73 s
TTFT— Worst comparable value
1.06 s
TPS— Worst comparable value
12.0 tok/s
Uptime— Worst comparable value
90.82%
Context
262.1K
Route
dekallm
Blended— Best comparable value
$0.992 / 1M
Venice
Speed— Best comparable value
9.77 s
TTFT— Best comparable value
0.68 s
TPS— Best comparable value
55.0 tok/s
Uptime— Best comparable value
99.71%
Context
256K
Route
venice/fp8
Blended— Worst comparable value
$1.3 / 1M
Gemma 4 26B A4BWinner · DekaLLM
DekaLLM
Speed— Best comparable value
12.53 s
TTFT— Worst comparable value
1.16 s
TPS— Best comparable value
44.0 tok/s
Uptime— Worst comparable value
95.49%
Context
262.1K
Route
dekallm/bf16
Blended— Best comparable value
$0.15 / 1M
Venice
Speed— Worst comparable value
13.43 s
TTFT— Best comparable value
0.93 s
TPS— Worst comparable value
40.0 tok/s
Uptime— Best comparable value
99.36%
Context
256K
Route
venice/bf16
Blended— Worst comparable value
$0.22 / 1M

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 Venice analysis

How DekaLLM and Venice compare for AI inference

DekaLLM and Venice share 3 indexed text models, including Qwen3.6 35B A3B, Qwen3.6 27B, Gemma 4 26B A4B. Venice 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 8 indexed models and lists 1 published server region; Venice has 30 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 Venice.