Qwen3 235B (Reasoning) offers competitive speed and a substantial context window, but its intelligence scores are average and its overall pricing is on the higher side compared to similar open-weight models.
The Qwen3 235B A22B (Reasoning) model, developed by Alibaba, positions itself as a powerful contender in the large language model space, particularly for tasks requiring extensive context and rapid processing. With a substantial 33,000-token context window, it is well-suited for complex applications such as detailed document analysis, long-form content generation, and intricate coding tasks where retaining conversational history or large data inputs is critical. This model supports text-to-text generation, making it a versatile tool for a wide array of natural language processing applications.
While Qwen3 235B (Reasoning) demonstrates impressive speed, clocking in at 63 tokens per second on average, it presents a mixed bag when it comes to intelligence and cost-efficiency. Our Artificial Analysis Intelligence Index places it at 42, which is on par with the average for comparable models but doesn't set it apart as a top-tier performer in raw intelligence. Furthermore, its verbosity, generating 85 million tokens during our intelligence evaluation compared to an average of 22 million, suggests that while it can produce extensive output, this may come with increased processing and cost implications.
From a pricing perspective, Qwen3 235B (Reasoning) is notably more expensive than many of its open-weight counterparts. With an input token price of $0.70 per million and an output token price of $8.40 per million, it ranks among the higher-cost options. This elevated pricing, especially for output tokens, means that applications requiring high volumes of generated text will need careful cost management. However, the availability of various API providers, such as Together.ai (FP8) and Fireworks, offers significant opportunities for cost optimization, as these providers deliver the model at substantially lower price points than Alibaba Cloud's direct offering.
The model's open license is a significant advantage, fostering broader adoption and integration into diverse ecosystems. This openness, combined with its robust context window and above-average speed, makes Qwen3 235B (Reasoning) an attractive option for developers and enterprises looking for a powerful, flexible model, provided they carefully manage its inherent verbosity and leverage competitive API pricing to mitigate costs. Its performance characteristics suggest it's best utilized in scenarios where speed and context are paramount, and where the value derived from its outputs justifies the investment.
42 (27 / 51 / 51)
63 tokens/s
$0.70 /M tokens
$8.40 /M tokens
85M tokens
0.40s TTFT
| Spec | Details |
|---|---|
| Model Family | Qwen3 |
| Variant | 235B A22B (Reasoning) |
| Owner | Alibaba |
| License | Open |
| Context Window | 33,000 tokens |
| Input Type | Text |
| Output Type | Text |
| Intelligence Index | 42 (Rank #27/51) |
| Average Output Speed | 63 tokens/s |
| Average Input Price | $0.70 / 1M tokens |
| Average Output Price | $8.40 / 1M tokens |
| Evaluation Verbosity | 85M tokens |
| Primary Use Case | Reasoning, long-context tasks |
Selecting the right API provider for Qwen3 235B (Reasoning) is paramount, as performance and pricing vary dramatically. Our benchmarks highlight significant differences in output speed, latency, and token costs across Fireworks, Together.ai (FP8), and Alibaba Cloud.
For optimal performance and cost-efficiency, it's crucial to align your specific application needs with the strengths of each provider. Whether your priority is the absolute lowest latency, maximum output speed, or the most competitive blended price, there's a provider that stands out.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Overall Value | Together.ai (FP8) | Offers the best blended price ($0.30/M tokens) and lowest latency (0.40s TTFT). | Output speed (45 t/s) is lower than Fireworks. |
| Highest Speed | Fireworks | Delivers the fastest output speed (99 t/s) and competitive pricing ($0.39/M tokens). | Latency (0.84s TTFT) is higher than Together.ai (FP8). |
| Lowest Latency | Together.ai (FP8) | Achieves the lowest time-to-first-token (0.40s), ideal for interactive applications. | Output speed is not the absolute fastest. |
| Lowest Input Price | Together.ai (FP8) | Most cost-effective for input tokens at $0.20/M, beneficial for prompt-heavy workflows. | Output token price is slightly higher than its input price advantage. |
| Lowest Output Price | Together.ai (FP8) | Offers the most economical output tokens at $0.60/M, critical for verbose generation. | May require balancing with other performance metrics. |
| Baseline Reference | Alibaba Cloud | Direct access to the model from its owner. | Significantly higher prices and latency compared to optimized third-party providers. |
Note: FP8 refers to the use of FP8 quantization by Together.ai, which can offer performance and cost benefits but may have minor impacts on precision for certain tasks.
Understanding the real-world cost implications of Qwen3 235B (Reasoning) requires looking beyond raw token prices and considering typical usage scenarios. The model's high output token cost and verbosity mean that even seemingly small tasks can accumulate significant expenses if not managed efficiently.
Below are several common AI application scenarios, detailing estimated input/output token counts and the corresponding costs when leveraging the most cost-effective provider, Together.ai (FP8), which offers $0.20/M input and $0.60/M output tokens.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input (tokens) | Output (tokens) | What it represents | Estimated Cost (Together.ai FP8) |
| Long Document Summarization | 25,000 | 1,500 | Summarizing a 50-page report into a concise overview. | $0.0059 |
| Complex Code Generation | 5,000 | 2,000 | Generating a complex function or script based on detailed requirements. | $0.0022 |
| Advanced Chatbot Interaction | 1,000 | 500 | A single, detailed turn in a customer support or technical assistance chatbot. | $0.0005 |
| Multi-turn Creative Writing | 8,000 | 3,000 | Collaborating on a story or marketing copy over several exchanges. | $0.0034 |
| Data Extraction & Structuring | 15,000 | 1,000 | Extracting specific entities and structuring data from unstructured text. | $0.0036 |
| Research Paper Analysis | 30,000 | 2,500 | Analyzing key arguments and findings from a lengthy academic paper. | $0.0075 |
These examples highlight that while individual interactions might seem inexpensive, the cumulative cost for high-volume applications or those requiring extensive output can quickly add up. Strategic prompt engineering to minimize output tokens and careful provider selection are crucial for managing expenses with Qwen3 235B (Reasoning).
Optimizing costs for Qwen3 235B (Reasoning) is essential given its higher price points, especially for output tokens. A proactive approach to managing usage and provider selection can yield significant savings without compromising performance.
Here are key strategies to implement for a cost-effective deployment of this powerful model:
The choice of API provider is the single most impactful decision for cost management with Qwen3 235B. Providers like Together.ai (FP8) and Fireworks offer dramatically lower prices than Alibaba Cloud's direct offering.
Given Qwen3 235B's verbosity and high output token cost, crafting efficient prompts is critical to minimize unnecessary token generation.
Even with optimized prompts, models can sometimes be verbose. Post-processing outputs can help manage costs and ensure relevance.
While the 33k context window is a strength, using it judiciously can prevent unnecessary input token costs.
Qwen3 235B A22B (Reasoning) is a large language model developed by Alibaba, specifically designed with a focus on reasoning capabilities. It features a substantial 33,000-token context window and is known for its above-average output speed, making it suitable for complex analytical and generative tasks.
On the Artificial Analysis Intelligence Index, Qwen3 235B (Reasoning) scores 42, which is on par with the average for comparable models. This indicates solid performance in general intelligence tasks, though it doesn't stand out as a top-tier performer in raw intelligence when compared to the absolute best models in its class.
Yes, its overall pricing is considered expensive, particularly for output tokens, which can cost up to $8.40 per million. However, costs can be significantly reduced by choosing optimized third-party API providers like Together.ai (FP8) or Fireworks, which offer much more competitive rates.
Qwen3 235B (Reasoning) boasts a large context window of 33,000 tokens. This allows it to process and retain a significant amount of information, making it highly effective for applications requiring deep understanding of long documents, extensive conversations, or large codebases.
For raw output speed, Fireworks leads with 99 tokens/s. For the lowest latency (TTFT) and best blended price, Together.ai (FP8) is the top choice. Alibaba Cloud, while the model owner, generally offers higher prices and latency compared to these optimized third-party providers.
The primary trade-offs are its higher cost, especially for output tokens, and its tendency for verbosity, which can further increase token usage and costs. While it offers good speed and a large context, users must actively manage prompt engineering and provider selection to mitigate these cost factors.
Yes, especially when paired with providers offering low latency. Together.ai (FP8) provides a time-to-first-token (TTFT) of 0.40s, which is excellent for interactive and real-time applications where quick initial responses are critical.