qwen3-32bAlibaba (Qwen)
- endpoints
- 3
- regions
- 2
- api
- chat
- released
- April 2025
capabilities 3/8
The same weights cost 1.6x more on deepinfra inc. than on deepinfra inc.. That is what the endpoints table is for.
| # | flags | ||||||
|---|---|---|---|---|---|---|---|
| 1 |  DeepInfra Inc. | Global | 41K | $0.06 | $0.22 | - | zdr |
| 2 | DeepInfra Inc. | Global | 41K | $0.10 | $0.30 | $0.10 | zdr |
| 3 |  Nebius AI | EU | 128K | $0.10 | $0.30 | $0.10 | zdr |
no measured traffic for this model yet. the endpoints table above carries provider pricing
| eval | Score as a share of 100 | /100 |
|---|---|---|
| MMLU Pro | 72.7 | |
| GPQA Diamond | 53.5 | |
| LiveCodeBench | 28.8 | |
| Math Index | 19.7 | |
| AIME 2025 | 19.7 | |
| Intelligence Index | 7.3 | |
| Humanity's Last Exam | 4.1 | |
| 7 evals | mean | 29.4 |
Scores from Artificial Analysis and public leaderboards, normalised to 100. Bar colour is the band, not the rank: green 80 and up, blue 55 and up, amber 30 and up. Benchmarks measure narrow skills, so test on your own workload before committing. Released 2025-04-28.
deepinfra/Qwen/Qwen3-32B:flexthis model has no managed policy yet, so call the provider endpoint directly. every id in the endpoints table works the same way
Base url is https://router.requesty.ai/v1 for every id here. One key reaches the whole catalog.
from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1",) response = client.chat.completions.create( model="deepinfra/Qwen/Qwen3-32B:flex", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ],) print(response.choices[0].message.content)
Change the base url, use your Requesty key, set the model to any id in the call pane. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog, so switching later is a one-parameter change. Browse all models
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance. Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.
questions 5
Which providers serve qwen3-32b?
How much does qwen3-32b cost?
Is qwen3-32b open weights?
What is the context window of qwen3-32b?
How do I use qwen3-32b with the OpenAI SDK?
more from alibaba (qwen) 6
| model | context | from /M | providers |
|---|---|---|---|
| qwen3.8-2.4t-a95b | 1M | $2.50 | 1 |
| qwen3.8-flash-next | 262K | $0.20 | 1 |
| qwen3.8-flash | 1.0M | $0.16 | 1 |
| qwen3.8-max | 1.0M | $2.00 | 3 |
| qwen3.7-plus | 1.0M | $0.32 | 2 |
| qwen3.7-max | 1.0M | $2.50 | 1 |
route qwen3-32b through one endpoint
One key for 2 providers on this model and 600+ others. No markup on provider prices, automatic failover, caching built in. Weekly aggregates in the measured pane come from production traffic routed through Requesty, one line per provider on a shared axis. Methodology