Friendli
6 indexed models
- Headquarters
United States- Server regions
- N/a
- Model types
- 6 text
Same-model provider benchmark
Friendli vs Weights & Biases: LLM provider comparison
Compare Friendli and Weights & Biases on 3 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.
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
1.59× typical advantage
3 exact models with complete recent speed data
Lowest token cost
$1.0633 / 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
Visual comparison
Price and performance charts
Estimated seconds using recent median response-start and output-speed data. Lower is better.
Friendli has the lower typical same-model response ratio across 3 measured models.
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 3 priced shared models.
| Model | Friendli | Weights & Biases |
|---|---|---|
 GLM 5.2Winner · Weights & Biases z-ai/glm-5.2 | Friendli
| Weights & Biases
|
 Gemma 4 31BWinner · Friendli google/gemma-4-31b-it | Friendli
| Weights & Biases
|
 MiniMax M2.5Winner · Friendli minimax/minimax-m2.5 | Friendli
| Weights & Biases
|
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.
Friendli vs Weights & Biases analysis
How Friendli and Weights & Biases compare for AI inference
Friendli and Weights & Biases share 3 indexed text models, including GLM 5.2, Gemma 4 31B, MiniMax M2.5. Friendli 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
Friendli has 6 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.
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