A high-cost, multimodal model with a vast 200k token context window, positioned for complex reasoning tasks but showing surprising weakness in specific intelligence benchmarks.
Claude 3 Opus is the flagship large language model from Anthropic, engineered to be the pinnacle of their model family. It is positioned as a direct competitor to other top-tier models like GPT-4, designed for complex, open-ended problem-solving, advanced reasoning, and tasks requiring a deep understanding of extensive information. Its most prominent feature is a massive 200,000-token context window, allowing it to process and analyze entire books, lengthy financial reports, or extensive codebases in a single prompt. This, combined with its multimodal capability to understand and interpret images, makes it a powerful tool for a wide range of sophisticated applications, from scientific research to enterprise-level strategic analysis.
However, this analysis reveals a significant and surprising discrepancy between its market reputation and its performance on the Artificial Analysis Intelligence Index. On this specific benchmark, Claude 3 Opus scores a mere 21, placing it in the bottom quartile of models tested at rank #46 out of 54. This result is starkly at odds with its positioning as a premier reasoning engine and is well below the average score of 30 for comparable models. This finding suggests that while Opus may excel at the qualitative, human-like conversational tasks it's often praised for, it may have specific, measurable weaknesses in the types of logical, mathematical, or instructional reasoning problems that constitute this particular index. It's a critical data point that challenges potential users to look beyond marketing and evaluate the model's intelligence on their own specific workloads.
The model's pricing structure further complicates the value proposition. At $15.00 per million input tokens and a staggering $75.00 per million output tokens, Opus is one of the most expensive models on the market. For context, the average input price for models in this analysis is around $2.00, and the average output price is $10.00. This premium pricing means that any application built on Opus must deliver exceptionally high value to justify its operational costs. The cost is particularly punitive for tasks that generate lengthy outputs, making verbosity a significant financial liability.
Despite the concerning intelligence score and high cost, Opus demonstrates strong raw performance when served via cloud APIs. Benchmarks show that on Amazon Bedrock, it achieves the lowest latency (1.26s time-to-first-token) and the highest throughput (19 tokens/second) compared to Google Vertex. This makes Bedrock the clear choice for applications where responsiveness and speed are critical. Ultimately, Claude 3 Opus presents a complex trade-off: users gain access to a massive context window and fast API performance on select platforms, but at a very high cost and with performance on some intelligence metrics that is, according to this data, far from best-in-class.
21 (46 / 54)
19 tokens/s
$15.00 / 1M tokens
$75.00 / 1M tokens
N/A
1.26 s (TTFT)
| Spec | Details |
|---|---|
| Owner | Anthropic |
| License | Proprietary |
| Context Window | 200,000 tokens |
| Knowledge Cutoff | July 2023 |
| Multimodality | Yes (Image and Text Input) |
| Input Pricing | $15.00 / 1M tokens |
| Output Pricing | $75.00 / 1M tokens |
| Intelligence Index Score | 21 / 100 |
| Intelligence Index Rank | #46 / 54 |
| Fastest Provider (Speed) | Amazon Bedrock (19 tokens/s) |
| Fastest Provider (Latency) | Amazon Bedrock (1.26s TTFT) |
| Primary Competitors | GPT-4 Turbo, Gemini 1.5 Pro |
Choosing the right API provider for Claude 3 Opus is straightforward, as the performance differences are stark while the pricing is identical. The benchmark data clearly points to one provider for nearly every priority.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Latency | Amazon Bedrock | At 1.26s TTFT, it's nearly twice as fast to respond as Google Vertex (2.22s), which is critical for interactive applications. | None. It is also the fastest and is tied for the lowest price. |
| Highest Throughput | Amazon Bedrock | Delivers 19 tokens/second, a 27% speed advantage over Google Vertex (15 t/s), making it better for generating longer answers. | None. It also has the lowest latency and is tied for price. |
| Lowest Cost | Tie: Amazon Bedrock & Google Vertex | Both providers offer the exact same pricing: $15.00/1M input and $75.00/1M output tokens. | Choosing Google Vertex means accepting significantly worse performance for the same cost. |
| Best Overall Value | Amazon Bedrock | It decisively leads in both latency and output speed at the same price point as its competitor, making it the unambiguous choice for performance. | Potential for deeper platform lock-in with AWS services if your stack is not already there. |
Provider performance metrics are based on benchmarks conducted by Artificial Analysis. Real-world performance may vary based on geographic region, specific workload, and API traffic.
The abstract price-per-token can be difficult to conceptualize. The following table estimates the cost of running Claude 3 Opus for several common, real-world scenarios, highlighting how quickly costs can accumulate, especially with lengthy outputs.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Summarize a Long Report | 50,000 tokens (~125 pages) | 1,000 tokens | Core use case for a large context window model. | $0.83 |
| Complex RAG Chat Turn | 15,000 tokens (context) + 500 tokens (query) | 500 tokens | A single turn in a retrieval-augmented generation chat. | $0.27 |
| Analyze a Business Chart | 1,500 tokens (image cost) + 100 tokens (prompt) | 300 tokens | A standard multimodal vision task. | $0.05 |
| Generate a Python Class | 2,000 tokens (instructions, examples) | 8,000 tokens (code file) | A common code generation task with a large output. | $0.63 |
| Draft a Marketing Email | 500 tokens (prompt) | 400 tokens | A simple, short-form content generation task. | $0.04 |
The key takeaway is the punishing cost of output. Scenarios that generate extensive text, like code generation, become significantly more expensive than those that provide concise analysis. Even a single complex chat turn can cost over a quarter, which adds up rapidly in a production environment.
Given its premium pricing, managing the cost of Claude 3 Opus is not just an optimization—it's a necessity. Implementing a clear strategy to control token consumption is essential for any application using this model at scale. Below are several effective tactics.
The single most effective cost-control measure is to reduce the number of output tokens, which are 5x more expensive than input tokens. You can achieve this through careful prompt engineering.
Not every query requires the power (and cost) of Opus. Implement a multi-model strategy where a cheaper, faster model like Claude 3 Haiku or Sonnet acts as a "router" or initial triage layer.
Many applications receive repetitive queries. Calling the Opus API for the same question multiple times is an unnecessary expense. Implement a caching layer (like Redis or a simple database) to store results.
When working with a large document in the context window, avoid making multiple separate API calls for different questions about the same document. This is inefficient and costly.
The score of 21 comes from the Artificial Analysis Intelligence Index, which is a specific, proprietary benchmark. This index may heavily weigh certain types of logical, mathematical, or instruction-following tasks where Opus, in this test, did not perform well. It does not necessarily reflect the model's full range of capabilities in areas like creative writing, nuanced conversation, or real-world reasoning. This result is an outlier compared to the model's general reputation and highlights the importance of evaluating models on your own specific tasks rather than relying on a single metric.
It depends entirely on the use case. If your application's core function relies on processing documents larger than 100k tokens or requires the specific reasoning style unique to Opus, the high price might be a necessary business expense. However, for more general tasks like chatbots, content summarization, or standard RAG, the price-to-performance ratio suggested by this analysis is poor. Cheaper models may provide 80-90% of the quality for less than 20% of the cost.
According to the benchmark data, Amazon Bedrock is the superior choice for running Claude 3 Opus. It offers significantly lower latency (faster response time) and higher throughput (faster generation speed) for the exact same price as Google Vertex. Unless your infrastructure is deeply integrated with Google Cloud and migrating is not feasible, Bedrock provides better performance for your money.
Opus and GPT-4 Turbo are direct competitors. Opus boasts a larger official context window (200k vs. GPT-4T's 128k). However, Opus is substantially more expensive; its input price is 50% higher ($15 vs $10) and its output price is 150% higher ($75 vs $30). Performance is task-dependent: some users find Opus better for creative and long-form writing, while others prefer GPT-4T for coding and complex instruction-following. This analysis's low intelligence score for Opus suggests GPT-4T may have an edge on similar benchmarks.
It means the model can accept and process images as part of its input, in addition to text. You can upload a photograph, diagram, chart, or screenshot and ask the model questions about it. For example, you could upload a graph of your company's quarterly earnings and ask Opus to summarize the key trends, or upload a picture of a plant and ask for its species. This capability is priced based on the image size, converted to an equivalent number of tokens.
The 200,000-token context window allows the model to "remember" and reason over a vast amount of information in a single interaction. This is equivalent to about 150,000 words or a 500-page book. Key use cases include: