QwQ 1.5B: pricing, context window and benchmarks
QwQ 1.5B is a small open-weight model from Alibaba Cloud. It offers a 32,768-token context window, costs $0.61 per million input tokens and $3.19 per million output tokens, and averages 51.6 across the index's benchmark suite.
Pricing
QwQ 1.5B costs $0.61 per million input tokens and $3.19 per million output tokens. A blended 3:1 input-to-output mix works out to about $1.25 per million tokens. Cached input is listed at $0.07 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.61 | per 1M tokens |
| Output | $3.19 | per 1M tokens |
| Cached input | $0.07 | per 1M tokens |
| Blended 3:1 | $1.25 | estimate per 1M tokens |
| 1M in + 1M out | $3.80 | 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 | 57.5 | 555 | 8th | |
| GPQA Diamond | 47.3 | 545 | 9th | |
| SWE-bench Verified | 35.0 | 557 | 7th | |
| HumanEval | 53.1 | 554 | 8th | |
| MATH-500 | 50.8 | 549 | 9th | |
| AIME 2025 | 39.9 | 541 | 10th | |
| LiveCodeBench | 42.5 | 523 | 13th | |
| IFEval | 63.1 | 546 | 9th | |
| TruthfulQA | 57.3 | 551 | 8th | |
| HellaSwag | 69.5 | 541 | 10th | |
| MMMU | 51.7 | 536 | 11th | |
| Arena Elo | 1211.8 | 542 | 10th |
What QwQ 1.5B is good for
QwQ 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 $0.61 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.61 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- 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 |
|---|---|---|---|---|
| Llama 3.3 1.5B | Meta AI | 4096 | $0.61 | 52.2 |
| DBRX 7B | Databricks | 8192 | $0.65 | 51.9 |
| OLMo 2 7B | Allen Institute for AI | 16384 | $0.56 | 51.7 |
| QwQ 1.5B | Alibaba Cloud | 32768 | $0.61 | 51.6 |
| MiniMax-Text 1.5B | MiniMax | 32768 | $0.58 | 51.5 |
| Phi-3.5 8B | Microsoft | 16384 | $0.59 | 51.2 |
FAQ
How much does QwQ 1.5B cost?
QwQ 1.5B is listed at $0.61 per million input tokens and $3.19 per million output tokens. Cached input is $0.069 per million tokens. A blended 3:1 input-to-output mix is about $1.25 per million tokens.
What is the context window of QwQ 1.5B?
QwQ 1.5B has a context window of 32,768 tokens and can emit up to 4,096 output tokens per request in this index.
Is QwQ 1.5B open weights?
Yes. QwQ 1.5B is listed as an open-weight model under a open weights licence.
How does QwQ 1.5B score on benchmarks?
QwQ 1.5B averages 51.6 across the scored benchmarks in this index, ranking #544 of 600 models. Its strongest relative result is Arena Elo at 1211.8.
What is QwQ 1.5B best for?
QwQ 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 $0.61 per million input tokens it can handle a large share of production traffic.