Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
Get Started
Sign Up
Sign Up

Qwen3 235B A22B Instruct 2507

Up to 30%

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.

Providers

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

List price by provider ($ / 1M tokens)

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.

ProviderPricingContextCapabilities
nebius30% offin $200; out $600 per 1M tokens262K tokenstools, streaming, JSON, structured
novita30% offin $90; out $580 per 1M tokens262K tokenstools, streaming, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 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.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_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)
{
  "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."}
  ]
}

5 Core Capabilities

  • 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.

6 Most Valuable Use Cases

  • Multilingual localization drafts for UX copy with Qwen3 235B A22B Instruct 2507
  • Coding agents for refactors, tests, and PR explanations
  • Product analytics narration and anomaly explanations with Qwen3 235B A22B Instruct 2507
  • Research synthesis across long documents and tickets
  • Data extraction into JSON for downstream systems with Qwen3 235B A22B Instruct 2507
  • Internal knowledge assistants grounded with your retrieval layer

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Qwen3 235B A22B Instruct 2507 on LLM.API?

  • Unified AI Routing

    Reach Qwen3 235B A22B Instruct 2507 and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Qwen3 235B A22B Instruct 2507.

  • Reliability Layer

    Production: Retry and route across configured providers when a single upstream blips.

  • Observability

    Production: Trace prompts, tokens, and errors for Qwen3 235B A22B Instruct 2507 alongside the rest of your stack.

  • Drop-in SDKs

    Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Qwen3 235B A22B Instruct 2507 for chat, media, or embedding alternatives without rewriting auth.

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

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.

Artificial Analysis Intelligence Index12
Median index across all tracked models12
Output speed59 tokens/s
Reference input price$0.23 / 1M tokens
Reference output price$0.92 / 1M tokens

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

COMMUNITY

What developers say about Qwen models

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`.

COMPARE

Competitive Models

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales