Qwen3 235B A22B Instruct 2507
vercel/3-235b出品方： vercel · 系列: qwen · 发布 2025-04 · 知识截止: 2025-04
⚠ 本模型为社区微调 / 衍生版本，非厂商官方发布。
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
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.
Coding41
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability1/10
Agents46
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit10/15
- Provider availability1/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency66
- Headline price (log-scaled)66/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness50
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)0/15
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 | $0.95 < $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 | $1.90 < $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 | $0.56 < $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 | $1.64 < $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 | $1.92 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定价详情
推荐定价来自 vercel · alibaba/qwen-3-235b
在 1 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| Vercel AI Gateway vercel | alibaba/qwen-3-235b | $0.130 | $0.600 | 41K | 2025-04 |
Frequently asked questions
How much does Qwen3 235B A22B Instruct 2507 cost?
Qwen3 235B A22B Instruct 2507 costs $0.130 per 1M input tokens and $0.600 per 1M output tokens, sourced from vercel. 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 Instruct 2507?
Qwen3 235B A22B Instruct 2507 has a context window of 41K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Qwen3 235B A22B Instruct 2507 support tool calling?
Yes. Qwen3 235B A22B Instruct 2507 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 Instruct 2507 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Qwen3 235B A22B Instruct 2507 in our data source. Verify with vercel's official documentation before relying on it in production.
Can Qwen3 235B A22B Instruct 2507 accept image input?
No. Qwen3 235B A22B Instruct 2507 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Qwen3 235B A22B Instruct 2507 open-weight?
No. Qwen3 235B A22B Instruct 2507 is a proprietary model — only vercel (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Qwen3 235B A22B Instruct 2507?
If Qwen3 235B A22B Instruct 2507 doesn't fit, consider Trinity Mini, Trinity Large Thinking, Trinity Large Preview. 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 come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
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- Titan Text Embeddings V2$0.02 in / $0.00 out
Capability lists this model is in
最近更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.