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Same-model provider benchmark

Baidu Qianfan vs Together: LLM provider comparison

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

Baidu Qianfan

7 indexed models

Headquarters
China
Server regions
N/a
Model types
7 text

Together

19 indexed models

Headquarters
United States
Server regions
N/a
Model types
17 text, 2 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

1.26× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$1.4199 / 1M

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

Most models available

19 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricBaidu QianfanTogether
Typical 500-token response12.62 s8.95 s
Typical response start1.05 s0.56 s
Typical output speed44.0 tok/s62.0 tok/s
Blended token price$1.4199 / 1M$2.34 / 1M
Typical route uptime99.34%94.55%

Visual comparison

Price and performance charts

Baidu QianfanTogether
500-token response by shared model

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

Together 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.

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

Shared text models

3 exact models · newest first

ModelBaidu QianfanTogether
GLM 5.2Winner · Baidu Qianfan

z-ai/glm-5.2

Baidu Qianfan
Blended price
$1.67 / 1M
500-token response
11.25 s
Response starts
1.05 s
Output speed
49.0 tok/s
Route uptime
99.53%
Context
1M
Route
baidu/fp8
Together
Blended price
$2.4 / 1M
500-token response
8.95 s
Response starts
0.88 s
Output speed
62.0 tok/s
Route uptime
94.55%
Context
262.1K
Route
together
DeepSeek V4 ProWinner · Baidu Qianfan

deepseek/deepseek-v4-pro

Baidu Qianfan
Blended price
$0.9937 / 1M
500-token response
12.62 s
Response starts
1.26 s
Output speed
44.0 tok/s
Route uptime
98.14%
Context
1M
Route
baidu/fp8
Together
Blended price
$2.32 / 1M
500-token response
16.18 s
Response starts
0.56 s
Output speed
32.0 tok/s
Route uptime
93.72%
Context
512K
Route
together
Kimi K2.6Winner · Together

moonshotai/kimi-k2.6

Baidu Qianfan
Blended price
$1.596 / 1M
500-token response
14.79 s
Response starts
0.90 s
Output speed
36.0 tok/s
Route uptime
99.34%
Context
262.1K
Route
baidu/fp4
Together
Blended price
$2.3 / 1M
500-token response
5.21 s
Response starts
0.54 s
Output speed
107.0 tok/s
Route uptime
99.73%
Context
262.1K
Route
together

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.

Baidu Qianfan vs Together analysis

How Baidu Qianfan and Together compare for AI inference

Baidu Qianfan and Together share 3 indexed text models, including GLM 5.2, DeepSeek V4 Pro, Kimi K2.6. Together 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

Baidu Qianfan has 7 indexed models and lists no published server regions; Together has 19 models and lists no regions. Verify data residency, privacy terms, limits, and production latency directly before choosing.