Groq
11 indexed models
- Headquarters
United States- Server regions
- N/a
- Model types
- 9 text, 2 transcription
Same-model provider benchmark
Groq vs Weights & Biases: LLM provider comparison
Compare Groq and Weights & Biases on 4 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
1.79× typical advantage
4 exact models with complete recent speed data
Lowest token cost
$0.2667 / 1M
4 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
| Metric | Groq | Weights & Biases |
|---|---|---|
| Typical 500-token response | 2.89 s | 5.70 s |
| Typical response start | 0.26 s | 0.26 s |
| Typical output speed | 203.0 tok/s | 106.5 tok/s |
| Blended token price | $0.2917 / 1M | $0.2667 / 1M |
| Typical route uptime | 99.96% | 99.83% |
Visual comparison
Price and performance charts
Estimated seconds using recent median response-start and output-speed data. Lower is better.
Groq has the lower typical same-model response ratio across 4 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 4 priced shared models.
| Model | Groq | Weights & Biases |
|---|---|---|
 gpt-oss-120bWinner · Groq openai/gpt-oss-120b | Groq
| Weights & Biases
|
gpt-oss-20bWinner · Weights & Biases openai/gpt-oss-20b | Groq
| Weights & Biases
|
 Llama 3.3 70B InstructWinner · Groq meta-llama/llama-3.3-70b-instruct | Groq
| Weights & Biases
|
Llama 3.1 8B InstructWinner · Groq meta-llama/llama-3.1-8b-instruct | Groq
| 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.
Groq vs Weights & Biases analysis
How Groq and Weights & Biases compare for AI inference
Groq and Weights & Biases share 4 indexed text models, including gpt-oss-120b, gpt-oss-20b, Llama 3.3 70B Instruct, Llama 3.1 8B Instruct. Groq has the stronger typical response-time result on the directly measured set.
Same-model speed evidence
4 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
4 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
Groq has 11 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.
Related same-model benchmarks
Compare with other providers
Explore qualified alternatives with the most shared measured models. Recommendations include comparisons for both Groq and Weights & Biases.
