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Same-model provider benchmark

Together vs Weights & Biases: LLM provider comparison

Compare Together and Weights & Biases on 8 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

Together

19 indexed models

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

Weights & Biases

19 indexed models

Headquarters
United States
Server regions
United States
Model types
19 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

1.15× typical advantage

8 exact models with complete recent speed data

Lowest token cost

$1.2104 / 1M

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

Most models available

Equal result

19 models each

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricTogetherWeights & Biases
Typical 500-token response8.79 s6.15 s
Typical response start0.55 s0.51 s
Typical output speed61.0 tok/s97.0 tok/s
Blended token price$1.3625 / 1M$1.2104 / 1M
Typical route uptime94.14%99.84%

Visual comparison

Price and performance charts

TogetherWeights & Biases
500-token response by shared model

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

Weights & Biases has the lower typical same-model response ratio across 8 measured models.

Blended token price by shared model

USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.

Weights & Biases has the lower typical price ratio across 8 priced shared models.

Shared text models

8 exact models · newest first

ModelTogetherWeights & Biases
GLM 5.2Winner · Weights & Biases

z-ai/glm-5.2

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
Weights & Biases
Blended price
$2.3933 / 1M
500-token response
4.48 s
Response starts
0.58 s
Output speed
128.0 tok/s
Route uptime
99.89%
Context
262.1K
Route
wandb/fp4
Kimi K2.7 CodeWinner · Weights & Biases

moonshotai/kimi-k2.7-code

Together
Blended price
$1.9667 / 1M
500-token response
3.47 s
Response starts
0.49 s
Output speed
168.0 tok/s
Route uptime
99.29%
Context
262.1K
Route
together
Weights & Biases
Blended price
$1.96 / 1M
500-token response
3.20 s
Response starts
0.46 s
Output speed
182.0 tok/s
Route uptime
N/a
Context
262.1K
Route
wandb/int4
DeepSeek V4 ProWinner · Together

deepseek/deepseek-v4-pro

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
Weights & Biases
Blended price
$2.32 / 1M
500-token response
126.79 s
Response starts
1.79 s
Output speed
4.0 tok/s
Route uptime
97.62%
Context
1M
Route
wandb/fp8
Kimi K2.6Winner · Weights & Biases

moonshotai/kimi-k2.6

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
Weights & Biases
Blended price
$1.9667 / 1M
500-token response
4.30 s
Response starts
0.45 s
Output speed
130.0 tok/s
Route uptime
100.00%
Context
262.1K
Route
wandb/fp4
Gemma 4 31BWinner · Weights & Biases

google/gemma-4-31b-it

Together
Blended price
$0.4733 / 1M
500-token response
24.13 s
Response starts
2.39 s
Output speed
23.0 tok/s
Route uptime
83.73%
Context
262.1K
Route
together
Weights & Biases
Blended price
$0.1967 / 1M
500-token response
17.81 s
Response starts
0.57 s
Output speed
29.0 tok/s
Route uptime
99.79%
Context
262.1K
Route
wandb/bf16
gpt-oss-120bWinner · Weights & Biases

openai/gpt-oss-120b

Together
Blended price
$0.3 / 1M
500-token response
8.63 s
Response starts
0.30 s
Output speed
60.0 tok/s
Route uptime
93.50%
Context
131.1K
Route
together
Weights & Biases
Blended price
$0.0733 / 1M
500-token response
16.33 s
Response starts
1.18 s
Output speed
33.0 tok/s
Route uptime
29.94%
Context
131.1K
Route
wandb/fp4
gpt-oss-20bWinner · Weights & Biases

openai/gpt-oss-20b

Together
Blended price
$0.1 / 1M
500-token response
2.95 s
Response starts
0.23 s
Output speed
184.0 tok/s
Route uptime
N/a
Context
131.1K
Route
together
Weights & Biases
Blended price
$0.0633 / 1M
500-token response
3.58 s
Response starts
0.27 s
Output speed
151.0 tok/s
Route uptime
99.67%
Context
131.1K
Route
wandb/fp4
Llama 3.3 70B InstructWinner · Weights & Biases

meta-llama/llama-3.3-70b-instruct

Together
Blended price
$1.04 / 1M
500-token response
16.41 s
Response starts
0.79 s
Output speed
32.0 tok/s
Route uptime
97.87%
Context
131.1K
Route
together/fp8
Weights & Biases
Blended price
$0.71 / 1M
500-token response
7.82 s
Response starts
0.24 s
Output speed
66.0 tok/s
Route uptime
100.00%
Context
128K
Route
wandb/fp16

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.

Together vs Weights & Biases analysis

How Together and Weights & Biases compare for AI inference

Together and Weights & Biases share 8 indexed text models, including GLM 5.2, Kimi K2.7 Code, DeepSeek V4 Pro, Kimi K2.6, Gemma 4 31B. Weights & Biases has the stronger typical response-time result on the directly measured set.

Same-model speed evidence

8 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

8 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

Together has 19 indexed models and lists no published server regions; Weights & Biases has 19 models and lists 1 region. Verify data residency, privacy terms, limits, and production latency directly before choosing.