A highly intelligent, open-licensed model from Alibaba, offering strong performance but at a premium price point and slower speeds.
Qwen3 14B (Non-reasoning) stands out as a formidable contender in the 14-billion parameter class, particularly noted for its exceptional intelligence. Developed by Alibaba and released under an open license, this model achieves a remarkable score of 29 on the Artificial Analysis Intelligence Index, significantly surpassing the average of comparable models. Its ability to process and generate coherent, high-quality text makes it suitable for a wide array of applications where nuanced understanding and robust output are paramount.
Despite its intellectual prowess, Qwen3 14B presents a trade-off in terms of cost and speed. Benchmarks reveal it to be considerably more expensive than its peers, with both input and output token prices ranking among the highest. Furthermore, its output speed, averaging around 55 tokens per second, falls below the industry average of 93 tokens per second. This combination of high cost and moderate speed necessitates careful consideration for budget-sensitive or latency-critical use cases.
The model supports a substantial context window of 33,000 tokens, allowing for the processing of lengthy documents and complex conversational histories. This large context, coupled with its strong intelligence, positions Qwen3 14B as an excellent choice for tasks requiring deep contextual understanding, such as advanced summarization, detailed content generation, and sophisticated question-answering systems. Its relatively concise verbosity, generating 8.0M tokens during intelligence evaluation compared to an average of 13M, suggests an efficient output style.
For developers and enterprises prioritizing intelligence and open-source flexibility over raw speed and cost efficiency, Qwen3 14B offers a compelling package. However, strategic provider selection is crucial to mitigate its inherent cost and speed limitations. Providers like Deepinfra, for instance, demonstrate significantly better performance metrics, including lower latency and higher output speeds, alongside more competitive pricing, making them the preferred choice for optimizing Qwen3 14B's deployment.
29 (#10 / 55 / 4 out of 4 units)
54.8 tokens/s
$0.35 per 1M tokens
$1.40 per 1M tokens
8.0M tokens
0.53 seconds (TTFT)
| Spec | Details |
|---|---|
| Owner | Alibaba |
| License | Open |
| Context Window | 33,000 tokens |
| Model Size | 14 Billion parameters |
| Input Type | Text |
| Output Type | Text |
| Intelligence Index | 29 (Top 20%) |
| Output Speed Rank | #29 / 55 |
| Input Price Rank | #50 / 55 |
| Output Price Rank | #51 / 55 |
| Verbosity Rank | #13 / 55 |
| Primary Use Case | Non-reasoning tasks |
Optimizing the deployment of Qwen3 14B (Non-reasoning) heavily relies on selecting the right API provider. Our benchmarks highlight significant differences in performance and pricing, making provider choice a critical factor in managing both cost and user experience.
Deepinfra (FP8) emerges as the clear leader, offering a superior balance of speed, latency, and affordability. Alibaba Cloud, while the model's owner, presents a less competitive offering in terms of raw performance and cost efficiency.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Best Overall | Deepinfra (FP8) | Lowest blended price ($0.10/M), fastest output (64 t/s), lowest latency (0.53s). | Limited to FP8 quantization. |
| Cost-Effective Input | Deepinfra (FP8) | Offers the lowest input token price at $0.06/M. | Still requires careful management of output tokens. |
| Cost-Effective Output | Deepinfra (FP8) | Lowest output token price at $0.24/M. | Output volume can still drive up costs. |
| Balanced Performance | Deepinfra (FP8) | Excellent blend of speed, latency, and competitive pricing across the board. | May not be suitable for all specific use cases requiring full precision. |
| Alternative Provider | Alibaba Cloud | Direct access from the model's owner. | Higher prices and slower performance compared to Deepinfra. |
Note: Prices and performance are based on benchmark data at the time of analysis and may vary. FP8 refers to 8-bit floating point quantization, which can offer speed and cost benefits.
Understanding the real-world cost implications of Qwen3 14B (Non-reasoning) requires examining various common scenarios. Given its premium pricing, especially with Alibaba Cloud, strategic usage and provider selection are paramount. The following examples use Deepinfra's more competitive pricing ($0.06/M input, $0.24/M output) to illustrate potential costs.
These scenarios highlight how even with the most cost-effective provider, the per-token pricing can accumulate, particularly for tasks involving substantial output generation or very long inputs.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input | Output | What it represents | Estimated cost (Deepinfra) |
| Intelligence Index Eval | ~10M tokens | ~8M tokens | Full benchmark evaluation for intelligence. | $27.65 |
| Chatbot Response | 50 tokens | 150 tokens | A single, concise conversational turn. | $0.000039 |
| Document Summarization | 10,000 tokens | 500 tokens | Summarizing a medium-sized article. | $0.00072 |
| Content Generation | 500 tokens | 2,000 tokens | Drafting a blog post or marketing copy. | $0.00051 |
| Data Extraction | 25,000 tokens | 1,000 tokens | Extracting key information from logs or reports. | $0.00174 |
While individual requests might seem inexpensive, the cumulative cost of Qwen3 14B (Non-reasoning) can quickly escalate in high-volume applications. Tasks involving extensive output generation or very large context windows will see the most significant cost impact, even with optimized providers like Deepinfra.
To effectively manage the costs associated with Qwen3 14B (Non-reasoning), a strategic approach is essential. Given its premium pricing, especially compared to other open-weight models, careful planning can yield significant savings without compromising on intelligence.
Here are key strategies to optimize your expenditure while leveraging the powerful capabilities of Qwen3 14B.
Our benchmarks clearly show Deepinfra (FP8) as the most cost-effective and performant provider for Qwen3 14B. Opting for this provider can drastically reduce your operational costs and improve latency.
Crafting concise and effective prompts can reduce input token count, and guiding the model to generate only necessary information can minimize output tokens.
If the model tends to be verbose for certain tasks, consider post-processing its output with a cheaper, smaller model to summarize or extract key information.
For non-latency-critical tasks, batching requests can improve overall throughput and potentially reduce per-request overhead, although token costs remain constant.
Qwen3 14B (Non-reasoning) is a 14-billion parameter large language model developed by Alibaba. It is designed for general text generation and understanding tasks, excelling in intelligence benchmarks, and is released under an open license.
It scores 29 on the Artificial Analysis Intelligence Index, placing it among the top performers (#10 out of 55 models benchmarked). This indicates a strong capability for complex language understanding and generation.
Yes, it is considered expensive. Its input token price ($0.35/M) and output token price ($1.40/M) are significantly higher than the average for comparable models. Provider choice, like Deepinfra, can mitigate these costs substantially.
Qwen3 14B supports a generous context window of 33,000 tokens. This allows it to process and generate responses based on very long inputs, making it suitable for tasks requiring extensive contextual understanding.
At an average output speed of 54.8 tokens per second, Qwen3 14B is slower than the average of 93 tokens per second. This might impact real-time or high-throughput applications.
Based on our benchmarks, Deepinfra (FP8) is the recommended provider. It offers the fastest output speed (64 t/s), lowest latency (0.53s), and the most competitive pricing for both input and output tokens.
Yes, its high intelligence and large 33k token context window make it well-suited for long-form content generation, summarization, and detailed question-answering. However, be mindful of the higher output token costs.