How do I call Qwen3 235B A22B Instruct 2507 via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "qwen3-235b-a22b-instruct-2507" and your LLM.API key. See the code snippet on this page.

What modalities does Qwen3 235B A22B Instruct 2507 support?

Qwen3 235B A22B Instruct 2507 accepts text and produces text according to its architecture metadata on LLM.API.

How is Qwen3 235B A22B Instruct 2507 priced on LLM.API?

Listed pricing metadata shows: In $0.2 / 1M tokens · Out $0.6 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

Is Qwen3 235B A22B Instruct 2507 a chat model?

Yes—Qwen3 235B A22B Instruct 2507 is exposed as a chat/completions-style endpoint on LLM.API.

What is the context length for Qwen3 235B A22B Instruct 2507?

Reported context for Qwen3 235B A22B Instruct 2507 is 262K tokens. Always verify the active provider row if multiple providers are listed.

Which providers serve Qwen3 235B A22B Instruct 2507?

LLM.API currently lists: nebius, novita. Availability can vary by region and account.

When should I choose Qwen3 235B A22B Instruct 2507?

Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need Qwen3 235B A22B Instruct 2507.

Can I use tools or structured outputs with Qwen3 235B A22B Instruct 2507?

Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

Which endpoint do I call for Qwen3 235B A22B Instruct 2507?

POST https://api.llmapi.ai/v1/chat/completions with model "qwen3-235b-a22b-instruct-2507" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Qwen3 235B A22B Instruct 2507 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Qwen3 235B A22B Instruct 2507 support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format.

Qwen3 235B A22B Instruct 2507: Self-Hosting & Deployment Guide

Qwen3 235B A22B Instruct 2507 is available on LLM.API as an OpenAI-compatible chat endpoint—route Alibaba quality through one key with transparent token pricing.

Start Using API

What is Qwen3 235B A22B Instruct 2507?

On LLM.API, Qwen3 235B A22B Instruct 2507 (qwen3-235b-a22b-instruct-2507) serves as a Alibaba conversational model for products that need 262K tokens context windows and predictable token pricing. qwen3-235b-a22b-instruct-2507 provided by nebius, novita. It is wired for API access through LLM.API with OpenAI-compatible patterns.

How to run Qwen3 235B A22B Instruct 2507 yourself

Open weights (apache-2.0), 235.1B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Qwen3 235B A22B Instruct 2507 VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	470.2 GB	Estimate	235.1B params × 2 bytes
FP8	236.4 GB	File size	Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
AWQ 4-bit	124.1 GB	File size	QuantTrio/Qwen3-235B-A22B-Instruct-2507-AWQ
MLX 4-bit	132.2 GB	File size	lmstudio-community/Qwen3-235B-A22B-Instruct-2507-MLX-4bit
GGUF Q8_0	249.9 GB	File size	unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF
GGUF Q4_K_M	142.2 GB	File size	unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF
GGUF Q2_K	85.7 GB	File size	unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy Qwen3 235B A22B Instruct 2507 on your hardware

Datacenter GPUsConsumer GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
NVIDIA H200141 GB	4×	2×	2×	2×	3×	2×	1×
NVIDIA H10080 GB	7×	4×	2×	2×	4×	3×	2×
NVIDIA A100 80GB80 GB	7×	4×	2×	2×	4×	3×	2×
NVIDIA L40S48 GB	—	6×	3×	4×	6×	4×	3×

GPUs needed per variant.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
RTX 509032 GB	—	6×	4×
RTX 409024 GB	—	7×	5×
RTX 309024 GB	—	7×	5×

GGUF via llama.cpp; more cards or CPU offload for larger files.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits	Fits	Fits	Fits
Mac, 192 GBM2 Ultra	—	—	Fits	—
Mac, 128 GBM4 Max / M3 Max	—	—	—	—
Mac, 64 GBM4 Pro / Max	—	—	—	—

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	—	—	Fits	Fits	—	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLangllama.cpp (GGUF)MLX (Mac)

pip install -U vllm
vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507

pip install 'sglang[all]'
python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507

llama-server -hf unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99

pip install -U mlx-lm
mlx_lm.generate --model lmstudio-community/Qwen3-235B-A22B-Instruct-2507-MLX-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Get API key See API code ↓

Providers

LLM.API routes Qwen3 235B A22B Instruct 2507 to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

nebius$0.2 in

$0.6 out

novita$0.09 in

$0.58 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
nebius	in $0.2; out $0.6 per 1M tokens	262K tokens	tools, streaming, JSON, structured
novita	in $0.09; out $0.58 per 1M tokens	262K tokens	tools, streaming, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test Qwen3 235B A22B Instruct 2507 right here — free to start.

Qwen3 235B A22B Instruct 2507

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Qwen3 235B A22B Instruct 2507 through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to qwen3-235b-a22b-instruct-2507 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-235b-a22b-instruct-2507",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="qwen3-235b-a22b-instruct-2507",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "qwen3-235b-a22b-instruct-2507",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="qwen3-235b-a22b-instruct-2507",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "qwen3-235b-a22b-instruct-2507",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID qwen3-235b-a22b-instruct-2507 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Qwen3 235B A22B Instruct 2507 in production

Best workloadsLLM.API features

What the model is good at.

Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Relevant for `qwen3-235b-a22b-instruct-2507` workloads on LLM.API.
Code assistance
Helps write, explain, refactor, and debug application code across common languages. Relevant for `qwen3-235b-a22b-instruct-2507` workloads on LLM.API.
Tool-ready dialogue
Works well in agent loops that call functions, browsers, or retrieval APIs. Grounded in the model's chat role rather than generic chat claims.

What you get when you call Qwen3 235B A22B Instruct 2507 through LLM.API.

Unified AI Routing
Reach Qwen3 235B A22B Instruct 2507 and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale Qwen3 235B A22B Instruct 2507.
More performance, less spend.
Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Production: Trace prompts, tokens, and errors for Qwen3 235B A22B Instruct 2507 alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Swap Qwen3 235B A22B Instruct 2507 for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

You need a general-purpose text model for assistants, agents, or content workflows (Qwen3 235B A22B Instruct 2507)
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Qwen3 235B A22B Instruct 2507)
You need provider failover options exposed for this model id (Qwen3 235B A22B Instruct 2507)
You want OpenAI-compatible chat completions through a single LLM.API key (Qwen3 235B A22B Instruct 2507)

Avoid if...

You need guaranteed real-time hard latency SLAs without benchmarking the provider
You need pure embedding, OCR, or media generation instead of chat
You require on-prem only deployment with no cloud inference
Your use case depends on unpublished proprietary benchmarks not listed here

LLM API rating for Qwen3 235B A22B Instruct 2507

★ 3.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

4.2Production adoptionStrong open-weight adoption

2.6Benchmark qualityArtificial Analysis Intelligence Index 12.0 · LMArena text rank #114, Elo 1419

4.0Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.7Coding 3.7Tool & function calling 4.3Long context 4.0

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

BENCHMARKS

Qwen3 235B A22B Instruct 2507 benchmark scores

Intelligence index

This model12.

Tracked median12.

Scale: 0-100 index points

Output speed

This model59 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$0.23

Output$0.92

Bars compare input and output list prices for this model.

Intelligence index+0% vs median

Qwen3 235B A22B Instruct 2507: Self-Hosting & Deployment Guide12

Median tracked model12

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed

59tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens

Input$0.23

Output$0.92

Provider list prices, before the LLM.API discount.

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

COMMUNITY

What developers say about Qwen models

LMArena#114 · Elo 1419Arena coding#105 · Elo 1445Speed59 tokens/sSources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Qwen3 235B A22B Instruct 2507.

Qwen is widely described as the strongest open-weight coding family, with reviewers testing whether local Qwen can replace hosted frontier assistants for agentic coding.
Benchmarks published by developer blogs show production-usable code and solid architectural reasoning, especially in the Coder variants.
The honest verdict in most write-ups: excellent value and privacy, still behind the top proprietary models on the hardest long-horizon tasks.

SOURCES

Frequently Asked Questions

How do I call Qwen3 235B A22B Instruct 2507 via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "qwen3-235b-a22b-instruct-2507" and your LLM.API key. See the code snippet on this page.
What modalities does Qwen3 235B A22B Instruct 2507 support?
Qwen3 235B A22B Instruct 2507 accepts text and produces text according to its architecture metadata on LLM.API.
How is Qwen3 235B A22B Instruct 2507 priced on LLM.API?
Listed pricing metadata shows: In $0.2 / 1M tokens · Out $0.6 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Is Qwen3 235B A22B Instruct 2507 a chat model?
Yes—Qwen3 235B A22B Instruct 2507 is exposed as a chat/completions-style endpoint on LLM.API.
What is the context length for Qwen3 235B A22B Instruct 2507?
Reported context for Qwen3 235B A22B Instruct 2507 is 262K tokens. Always verify the active provider row if multiple providers are listed.
Which providers serve Qwen3 235B A22B Instruct 2507?
LLM.API currently lists: nebius, novita. Availability can vary by region and account.
When should I choose Qwen3 235B A22B Instruct 2507?
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need Qwen3 235B A22B Instruct 2507.
Can I use tools or structured outputs with Qwen3 235B A22B Instruct 2507?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
What is Qwen3 235B A22B Instruct 2507?
qwen3-235b-a22b-instruct-2507 provided by nebius, novita. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `qwen3-235b-a22b-instruct-2507`.

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Qwen3 235B A22B Instruct 2507: Self-Hosting & Deployment Guide

What is Qwen3 235B A22B Instruct 2507?

How to run Qwen3 235B A22B Instruct 2507 yourself

Qwen3 235B A22B Instruct 2507 VRAM requirements

GPU requirements: deploy Qwen3 235B A22B Instruct 2507 on your hardware

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Qwen3 235B A22B Instruct 2507 in production

Conversational UX

Long-context synthesis

Analytical writing

Code assistance

Tool-ready dialogue

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth

When to Use — When NOT to Use

Use it if...

Avoid if...

LLM API rating for Qwen3 235B A22B Instruct 2507

Qwen3 235B A22B Instruct 2507 benchmark scores

Intelligence index

Output speed

Reference price per 1M tokens

What developers say about Qwen models

Frequently Asked Questions

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