Qwen2.5-Coder 32B Instruct
alibaba/qwen2-5-coder-32b-instruct提供： Alibaba (Qwen) · ファミリー: qwen · リリース 2024-11-11 · 知識カットオフ: 2024-04
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
機能一覧
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.
Coding49
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability7/10
Agents47
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability7/10
JSON / structured output50
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency88
- Headline price (log-scaled)83/95
- Has prompt-cache pricing5/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness91
- Number of independent providers35/40
- 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 | $0.19 < $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 | $0.38 < $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 | $0.11 < $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 | $0.33 < $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 | $0.39 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
料金詳細
推奨料金 (提供元): chutes · Qwen/Qwen2.5-Coder-32B-Instruct
最安プロバイダー: nvidia · Unknown 入力 + Unknown 出力
7 か所で利用可能
| プロバイダー | プロバイダーモデルID | 入力 / 1M | 出力 / 1M | コンテキスト | リリース日 |
|---|---|---|---|---|---|
| Alibaba (China) alibaba-cn | qwen2-5-coder-32b-instruct | $0.287 | $0.861 | 131K | 2024-11 |
| SiliconFlow (China) siliconflow-cn | Qwen/Qwen2.5-Coder-32B-Instruct | $0.180 | $0.180 | 33K | 2024-11-11 |
| Chutes chutes | Qwen/Qwen2.5-Coder-32B-Instruct | $0.027 | $0.109 | 33K | 2025-12-29 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/qwen/qwen2.5-coder-32b-instruct | $0.660 | $1.00 | 128K | 2025-04-11 |
| Synthetic synthetic | hf:Qwen/Qwen2.5-Coder-32B-Instruct | $0.800 | $0.800 | 33K | 2024-11-11 |
| Nvidia nvidia | qwen/qwen2.5-coder-32b-instruct | Unknown | Unknown | 128K | 2024-11-06 |
| SiliconFlow siliconflow | Qwen/Qwen2.5-Coder-32B-Instruct | $0.180 | $0.180 | 33K | 2024-11-11 |
プロバイダー間でデータに差異
- context_window varies: 128000, 131072, 32768, 33000
- release_date varies (span 423d): 2024-11, 2024-11-06, 2024-11-11, 2025-04-11, 2025-12-29
プロバイダーごとに本モデルの値が異なります。上部の「主要数値」は代表的プロバイダーを使用しています。詳細は表をご確認ください。
Frequently asked questions
How much does Qwen2.5-Coder 32B Instruct cost?
Qwen2.5-Coder 32B Instruct costs $0.027 per 1M input tokens and $0.109 per 1M output tokens, sourced from chutes. 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 235B A22B Instruct 2507. 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
More Alibaba (Qwen) models
- Qwen3.5 397B-A17B$0.60 in / $3.60 out
- Qwen3 32B$0.70 in / $2.80 out
- Qwen3 235B A22B Instruct 2507$0.10 in / $0.10 out
- Qwen3-Coder 480B-A35B Instruct$1.50 in / $7.50 out
- Qwen3-235B-A22B-Thinking-2507$0.10 in / $0.10 out
Capability lists this model is in
最終更新：
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.