Chutes
8 indexed models
- Headquarters
United States- Server regions
- N/a
- Model types
- 8 text
Same-model provider benchmark
Chutes vs Weights & Biases: LLM provider comparison
Compare Chutes 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
2.82× typical advantage
4 exact models with complete recent speed data
Lowest token cost
$0.7942 / 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 | Chutes | Weights & Biases |
|---|---|---|
| Typical 500-token response | 38.99 s | 11.97 s |
| Typical response start | 2.29 s | 0.41 s |
| Typical output speed | 15.0 tok/s | 56.0 tok/s |
| Blended token price | $0.7942 / 1M | $1.0908 / 1M |
| Typical route uptime | 98.50% | 100.00% |
Visual comparison
Price and performance charts
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 4 measured models.
USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.
Chutes has the lower typical price ratio across 4 priced shared models.
| Model | Chutes | Weights & Biases |
|---|---|---|
 Qwen3.6 27BWinner · Weights & Biases qwen/qwen3.6-27b | Chutes
| Weights & Biases
|
 Kimi K2.6Winner · Weights & Biases moonshotai/kimi-k2.6 | Chutes
| Weights & Biases
|
 Gemma 4 31BWinner · Weights & Biases google/gemma-4-31b-it | Chutes
| Weights & Biases
|
 MiniMax M2.5Winner · Weights & Biases minimax/minimax-m2.5 | Chutes
| 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.
Chutes vs Weights & Biases analysis
How Chutes and Weights & Biases compare for AI inference
Chutes and Weights & Biases share 4 indexed text models, including Qwen3.6 27B, Kimi K2.6, Gemma 4 31B, MiniMax M2.5. Weights & Biases 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
Chutes has 8 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 Chutes and Weights & Biases.
