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Qwen: Qwen3 235B A22B Instruct 2507
qwen/qwen3-235b-a22b-2507
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement "thinking mode" (<think> blocks).
Compared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.
Recent activity on Qwen3 235B A22B Instruct 2507
Tokens processed per day
Thoughput
(tokens/s)
| Providers | Min (tokens/s) | Max (tokens/s) | Avg (tokens/s) |
|---|---|---|---|
| Theta | 8.56 | 9.83 | 9.00 |
First Token Latency
(ms)
| Providers | Min (ms) | Max (ms) | Avg (ms) |
|---|---|---|---|
| Theta | 762 | 919 | 858.76 |
Providers for Qwen3 235B A22B Instruct 2507
ZenMux Provider to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.
Latency
0.88
s
Throughput
8.29
tps
Uptime
100.00
%
Recent uptime
Oct 10,2025 - 3 PM100.00%
Price
Input
$ 0.28
/ M tokens
Output
$ 1.11
/ M tokens
Cache read
-
Cache write 5m
-
Cache write 1h
-
Cache write
-
Web search
-
Model limitation
Context
256.00K
Max output
262.10K
Supported Parameters
max_completion_tokens
temperature
top_p
frequency_penalty
presence_penalty
seed
-
logit_bias
-
logprobs
top_logprobs
-
response_format
stop
tools
tool_choice
parallel_tool_calls
-
Model Protocol Compatibility
openai
anthropic
-
Data policy
Prompt training
false
Prompt Logging
Zero retention
Moderation
Responsibility of developer
Sample code and API for Qwen3 235B A22B Instruct 2507
ZenMux normalizes requests and responses across providers for you.
OpenAI-PythonPythonTypeScriptOpenAI-TypeScriptcURL
pythonfrom openai import OpenAI client = OpenAI( base_url="https://zenmux.ai/api/v1", api_key="<ZenMux_API_KEY>", ) completion = client.chat.completions.create( model="qwen/qwen3-235b-a22b-2507", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image?" } ] } ] ) print(completion.choices[0].message.content)