Qwen2.5-Coder 32B Instruct
alibaba/qwen2-5-coder-32b-instruct出品方： Alibaba (Qwen) · 系列: qwen · 发布 2024-11 · 知识截止: 2024-04
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.
Coding47
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability5/10
Agents45
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability5/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency72
- Headline price (log-scaled)72/95
- Has prompt-cache pricing0/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness81
- Number of independent providers25/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- 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 | $1.10 < $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 | $2.21 < $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 | $0.50 < $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 | $1.81 < $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 | $2.53 < $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 nano-gpt · qwen/Qwen2.5-Coder-32B-Instruct
最便宜的渠道： nvidia · Unknown 输入 + Unknown 输出
在 5 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 |
|---|---|---|---|---|---|
| Alibaba (China) alibaba-cn | qwen2-5-coder-32b-instruct | $0.287 | $0.861 | 131K | 2024-11 |
| Cloudflare Workers AI cloudflare-workers-ai | @cf/qwen/qwen2.5-coder-32b-instruct | $0.660 | $1.00 | 33K | 2025-02-27 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/qwen/qwen2.5-coder-32b-instruct | $0.660 | $1.00 | 128K | 2025-04-11 |
| Nvidia nvidia | qwen/qwen2.5-coder-32b-instruct | Unknown | Unknown | 128K | 2024-11-06 |
| NanoGPT nano-gpt | qwen/Qwen2.5-Coder-32B-Instruct | $0.201 | $0.201 | 32K | 2025-07-03 |
各渠道数据存在不一致
- context_window varies: 128000, 131072, 32000, 32768
- release_date varies (span 244d): 2024-11, 2024-11-06, 2025-02-27, 2025-04-11, 2025-07-03
各服务商对此模型的报告值存在差异。上方「核心数据」使用代表性服务商的值；逐项请以下表为准。
Frequently asked questions
How much does Qwen2.5-Coder 32B Instruct cost?
Qwen2.5-Coder 32B Instruct costs $0.201 per 1M input tokens and $0.201 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Qwen2.5-Coder 32B Instruct?
Qwen2.5-Coder 32B Instruct has a context window of 131K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does Qwen2.5-Coder 32B Instruct support tool calling?
Yes. Qwen2.5-Coder 32B Instruct 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 Qwen2.5-Coder 32B Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Qwen2.5-Coder 32B Instruct in our data source. Verify with Alibaba (Qwen)'s official documentation before relying on it in production.
Can Qwen2.5-Coder 32B Instruct accept image input?
No. Qwen2.5-Coder 32B Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Qwen2.5-Coder 32B Instruct open-weight?
Yes. Qwen2.5-Coder 32B Instruct'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 Qwen2.5-Coder 32B Instruct?
If Qwen2.5-Coder 32B Instruct 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
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- 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
最近更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.