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Same-model provider benchmark

io.net vs SiliconFlow: LLM provider comparison

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

io.net

3 indexed models

Headquarters
United States
Server regions
N/a
Model types
3 text

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

1.08× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$1.2262 / 1M

3 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

Metricio.netSiliconFlow
Typical 500-token response34.11 s20.11 s
Typical response start1.79 s1.75 s
Typical output speed15.0 tok/s30.0 tok/s
Blended token price$1.2455 / 1M$1.2262 / 1M
Typical route uptime99.27%98.57%

Visual comparison

Price and performance charts

io.netSiliconFlow
500-token response by shared model

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

io.net 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.

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

Shared text models

3 exact models · newest first

Modelio.netSiliconFlow
GLM 5.2Winner · SiliconFlow

z-ai/glm-5.2

io.net
Blended price
$2.4567 / 1M
500-token response
18.70 s
Response starts
2.57 s
Output speed
31.0 tok/s
Route uptime
97.19%
Context
262.1K
Route
io-net/fp8
SiliconFlow
Blended price
$2.232 / 1M
500-token response
20.11 s
Response starts
3.44 s
Output speed
30.0 tok/s
Route uptime
99.96%
Context
1M
Route
siliconflow/fp8
Qwen3.6 27BWinner · io.net

qwen/qwen3.6-27b

io.net
Blended price
$0.9801 / 1M
500-token response
34.11 s
Response starts
0.77 s
Output speed
15.0 tok/s
Route uptime
99.66%
Context
262.1K
Route
io-net/fp8
SiliconFlow
Blended price
$1.2667 / 1M
500-token response
64.25 s
Response starts
1.75 s
Output speed
8.0 tok/s
Route uptime
83.18%
Context
262.1K
Route
siliconflow/fp8
DeepSeek V4 FlashWinner · SiliconFlow

deepseek/deepseek-v4-flash

io.net
Blended price
$0.2997 / 1M
500-token response
35.12 s
Response starts
1.79 s
Output speed
15.0 tok/s
Route uptime
99.27%
Context
1M
Route
io-net/fp8
SiliconFlow
Blended price
$0.18 / 1M
500-token response
11.49 s
Response starts
1.69 s
Output speed
51.0 tok/s
Route uptime
98.57%
Context
1M
Route
siliconflow/fp8

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.

io.net vs SiliconFlow analysis

How io.net and SiliconFlow compare for AI inference

io.net and SiliconFlow share 3 indexed text models, including GLM 5.2, Qwen3.6 27B, DeepSeek V4 Flash. io.net 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

io.net has 3 indexed models and lists no published server regions; SiliconFlow has 36 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.