Qwen3 235B-A22B
alibaba/qwen3-235b-a22bVon Alibaba (Qwen) · Familie: qwen · veröffentlicht 2025-04 · Wissensstand: 2025-04
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding62
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents70
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit10/15
- Provider availability10/10
JSON / structured output43
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume13/20
Cost efficiency49
- Headline price (log-scaled)49/95
- Has prompt-cache pricing0/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness94
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $4.90 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $9.80 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $2.80 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $8.40 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $10.08 $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von alibaba · qwen3-235b-a22b
Günstigster Anbieter: nano-gpt · $0.300 Eingabe + $0.500 Ausgabe
Bei 11 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| Alibaba alibaba | qwen3-235b-a22b | $0.700 | $2.80 | 131K | 2025-04 |
| Alibaba (China) alibaba-cn | qwen3-235b-a22b | $0.287 | $1.15 | 131K | 2025-04 |
| OpenRouter openrouter | qwen/qwen3-235b-a22b | $0.455 | $1.82 | 131K | 2025-04 |
| Hugging Face huggingface | Qwen/Qwen3-235B-A22B | $0.200 | $0.800 | 41K | 2025-04 |
| Qiniu qiniu-ai | qwen3-235b-a22b | Unknown | Unknown | 128K | 2025-08-05 |
| NovitaAI novita-ai | qwen/qwen3-235b-a22b-fp8 | $0.200 | $0.800 | 41K | 2025-04-29 |
| 302.AI 302ai | qwen3-235b-a22b | $0.290 | $2.86 | 128K | 2025-04-29 |
| LLM Gateway llmgateway | qwen3-235b-a22b-fp8 | $0.200 | $0.800 | 41K | 2025-04-28 |
| Kilo Gateway kilo | qwen/qwen3-235b-a22b | $0.455 | $1.82 | 131K | 2024-12-01 |
| Jiekou.AI jiekou | qwen/qwen3-235b-a22b-fp8 | $0.200 | $0.800 | 41K | 2026-01 |
| NanoGPT nano-gpt | qwen/qwen3-235b-a22b | $0.300 | $0.500 | 41K | 2025-04-29 |
Datenunterschiede zwischen Anbietern
- context_window varies: 128000, 131072, 40960, 41000
- release_date varies (span 396d): 2024-12-01, 2025-04, 2025-04-28, 2025-04-29, 2025-08-05, 2026-01
- modalities varies across offerings
Anbieter melden unterschiedliche Werte für dieses Modell. Die Schnellinfos oben nutzen den repräsentativen Anbieter; pro Anbieter siehe Tabelle.
Frequently asked questions
How much does Qwen3 235B-A22B cost?
Qwen3 235B-A22B costs $0.700 per 1M input tokens and $2.80 per 1M output tokens, sourced from alibaba. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Qwen3 235B-A22B?
Qwen3 235B-A22B has a context window of 131K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Qwen3 235B-A22B support tool calling?
Yes. Qwen3 235B-A22B supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Qwen3 235B-A22B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Qwen3 235B-A22B in our data source. Verify with Alibaba (Qwen)'s official documentation before relying on it in production.
Can Qwen3 235B-A22B accept image input?
No. Qwen3 235B-A22B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Qwen3 235B-A22B open-weight?
Yes. Qwen3 235B-A22B's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Qwen3 235B-A22B?
If Qwen3 235B-A22B doesn't fit, consider Qwen3.5 397B-A17B, Qwen3 32B, Qwen3.7 Max. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Alibaba (Qwen) models
- Qwen3.5 397B-A17B$0.60 in / $3.60 out
- Qwen3 32B$0.70 in / $2.80 out
- Qwen3.7 Max$2.50 in / $7.50 out
- Qwen3.6 Plus$0.50 in / $3.00 out
- Qwen3 235B A22B Instruct 2507$0.10 in / $0.10 out
Capability lists this model is in
Zuletzt aktualisiert:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.