Qwen/Qwen3-235B-A22B-Thinking-2507
DeepInfra Inc./🇺🇸 US/chat
Qwen3-235B-A22B-Thinking-2507 is the Qwen3's new model with scaling the thinking capability of Qwen3-235B-A22B, improving both the quality and depth of reasoning.MoreLess
Which id to call
deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507This exact deployment on DeepInfra Inc., with no routing and no failover. Send it as the model field.
Input /1M
$0.23
$0.20 cached
Output /1M
$2.30
10.0x input
Context
262K
tokens
Added
Jul 2025
chat
Capabilities 4/8
Provider rates
What Qwen/Qwen3-235B-A22B-Thinking-2507 costs
Provider prices per 1M tokens, updated September 8, 2026.moreless
These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.
Input /1M
$0.23
Output /1M
$2.30
Cache write /1M
-
Cache read /1M
$0.20
What a workload costs
- 100K input + 10K output
- $0.0460
- 1M input + 100K output
- $0.46
- 10M input + 1M output
- $4.60
At the rates above, before caching. A cache read costs $0.20 per 1M, so repeated context lands under these figures.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507. Existing OpenAI SDK code needs no other edit.
123456789101112131415from 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/Qwen3-235B-A22B-Thinking-2507", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Public leaderboards
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
No benchmarks are published for this exact variant yet.
Region-specific deployments and highspeed tiers usually share scores with their base model. Try the base model page or the DeepInfra Inc. models overview.
Same provider
More from DeepInfra Inc.
Newest first, on the same provider and the same key.
Reference
Qwen/Qwen3-235B-A22B-Thinking-2507 questions
How much does Qwen/Qwen3-235B-A22B-Thinking-2507 cost?
Qwen/Qwen3-235B-A22B-Thinking-2507 is priced at $0.23 per million input tokens and $2.30 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.
What is the context window of Qwen/Qwen3-235B-A22B-Thinking-2507?
Qwen/Qwen3-235B-A22B-Thinking-2507 has a context window of 262K tokens. That's roughly 350 words of input you can fit in a single prompt.
What can Qwen/Qwen3-235B-A22B-Thinking-2507 do?
Qwen/Qwen3-235B-A22B-Thinking-2507 supports tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use Qwen/Qwen3-235B-A22B-Thinking-2507 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run Qwen/Qwen3-235B-A22B-Thinking-2507 through Requesty?
Yes. Qwen/Qwen3-235B-A22B-Thinking-2507 runs through Requesty's OpenAI-compatible API, served from DeepInfra Inc.. You do not host the model yourself: point base_url at Requesty, set the model to "deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call Qwen/Qwen3-235B-A22B-Thinking-2507 through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.