DBRX 1.5B: pricing, context window and benchmarks
DBRX 1.5B is a small open-weight model from Databricks. It offers a 16,384-token context window, costs $0.32 per million input tokens and $1.22 per million output tokens, and averages 47.0 across the index's benchmark suite.
Pricing
DBRX 1.5B costs $0.32 per million input tokens and $1.22 per million output tokens. A blended 3:1 input-to-output mix works out to about $0.54 per million tokens. Cached input is listed at $0.03 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.32 | per 1M tokens |
| Output | $1.22 | per 1M tokens |
| Cached input | $0.03 | per 1M tokens |
| Blended 3:1 | $0.54 | estimate per 1M tokens |
| 1M in + 1M out | $1.54 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 52.8 | 581 | 3th | |
| GPQA Diamond | 43.6 | 573 | 5th | |
| SWE-bench Verified | 32.7 | 572 | 5th | |
| HumanEval | 48.3 | 583 | 3th | |
| MATH-500 | 44.0 | 583 | 3th | |
| AIME 2025 | 34.8 | 571 | 5th | |
| LiveCodeBench | 33.6 | 574 | 4th | |
| IFEval | 58.1 | 582 | 3th | |
| TruthfulQA | 54.9 | 575 | 4th | |
| HellaSwag | 66.2 | 575 | 4th | |
| MMMU | 48.0 | 563 | 6th | |
| Arena Elo | 1180.3 | 576 | 4th |
What DBRX 1.5B is good for
DBRX 1.5B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.32 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.32 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- Context window of 16,384 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Mixtral 1.5B | Mistral AI | 16384 | $0.36 | 47.7 |
| Mixtral Turbo 1.5B | Together AI | 4096 | $0.31 | 47.6 |
| Nemotron Nano 7B | NVIDIA | 8192 | $0.35 | 47.5 |
| DBRX 1.5B | Databricks | 16384 | $0.32 | 47.0 |
| StableLM 2 8B | Stability AI | 32768 | $0.38 | 47.0 |
| Yi-Large 1.5B | 01.AI | 8192 | $0.42 | 46.8 |
FAQ
How much does DBRX 1.5B cost?
DBRX 1.5B is listed at $0.32 per million input tokens and $1.22 per million output tokens. Cached input is $0.028 per million tokens. A blended 3:1 input-to-output mix is about $0.54 per million tokens.
What is the context window of DBRX 1.5B?
DBRX 1.5B has a context window of 16,384 tokens and can emit up to 16,384 output tokens per request in this index.
Is DBRX 1.5B open weights?
Yes. DBRX 1.5B is listed as an open-weight model under a open weights licence.
How does DBRX 1.5B score on benchmarks?
DBRX 1.5B averages 47.0 across the scored benchmarks in this index, ranking #578 of 600 models. Its strongest relative result is Arena Elo at 1180.3.
What is DBRX 1.5B best for?
DBRX 1.5B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.32 per million input tokens it can handle a large share of production traffic.