Qwen2.5 14B Instruct
alibaba/qwen2-5-14b-instruct出品方： Alibaba (Qwen) · 系列: qwen · 发布 2024-09 · 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.
Coding46
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability4/10
Agents44
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability4/10
JSON / structured output47
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume17/20
Cost efficiency56
- Headline price (log-scaled)56/95
- Has prompt-cache pricing0/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness76
- Number of independent providers20/40
- 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 | $2.45 < $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 | $4.90 < $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 | $1.40 < $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 | $4.20 < $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 | $5.04 < $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 alibaba · qwen2-5-14b-instruct
最便宜的渠道： siliconflow-cn · $0.100 输入 + $0.100 输出
在 4 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 alibaba | qwen2-5-14b-instruct | $0.350 | $1.40 | 131K | 2024-09 |
| Alibaba (China) alibaba-cn | qwen2-5-14b-instruct | $0.144 | $0.431 | 131K | 2024-09 |
| SiliconFlow (China) siliconflow-cn | Qwen/Qwen2.5-14B-Instruct | $0.100 | $0.100 | 33K | 2024-09-18 |
| SiliconFlow siliconflow | Qwen/Qwen2.5-14B-Instruct | $0.100 | $0.100 | 33K | 2024-09-18 |
各渠道数据存在不一致
- context_window varies: 131072, 33000
- release_date varies (span 17d): 2024-09, 2024-09-18
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 14B Instruct cost?
Qwen2.5 14B Instruct costs $0.350 per 1M input tokens and $1.40 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 Qwen2.5 14B Instruct?
Qwen2.5 14B 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 14B Instruct support tool calling?
Yes. Qwen2.5 14B 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 14B Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Qwen2.5 14B Instruct in our data source. Verify with Alibaba (Qwen)'s official documentation before relying on it in production.
Can Qwen2.5 14B Instruct accept image input?
No. Qwen2.5 14B Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Qwen2.5 14B Instruct open-weight?
Yes. Qwen2.5 14B 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 14B Instruct?
If Qwen2.5 14B 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.