Claude Opus vs Sonnet vs Haiku: Which Model is Better?

Claude's latest model family has three different pricing tiers, ranging from the premium Opus 5.5 to the much cheaper Haiku 5.5.

But does paying more actually get you proportionally better performance?

At BestHunt, we wanted to understand the differences between Claude models, so we compared their quality scores, calculated costs across different workloads, and looked at how batch processing and caching affect pricing.

Here's what we found.

Pricing differences

We started by comparing the three Claude models side by side. 

Source: BestHunt AI Models leaderboard, October 2026.

In our leaderboard, Opus ranks first in overall quality, while Sonnet comes fourth and Haiku ranks 24th. But when it comes to pricing, Sonnet was approximately 55% cheaper than Opus for both input and output tokens. Haiku was almost 95% cheaper than Sonnet and around 98% cheaper than Opus..

Sonnet offers comparable overall quality to Opus at less than half the price

We looked at the Arena Elo scores of all three models to understand how far apart they were in overall quality.

Opus scored 1389, Sonnet scored 1376, and Haiku scored 1297.

The Opus and Sonnet comparison was interesting because Opus costs 120% more per token, but its Arena Elo score is only 13 points higher than Sonnet's.

It's important to note that, for our BestHunt findings, we use an 18-point Elo threshold to identify models with comparable overall quality. A model qualifies as a lower-cost alternative when it falls within that quality range and costs less for the same workload.

Sonnet meets those criteria when compared with Opus.

Haiku tells a different story. Its score is 79 points below Sonnet and 92 points below Opus. Although Haiku is considerably cheaper, the difference in overall quality is much larger.

This doesn't necessarily mean Haiku would perform poorly on every task. A model with a lower overall score could still handle simpler operations, such as classification, extraction, or routing, effectively.

How much can businesses save by switching Claude models?

To put the pricing differences into perspective, we calculated the monthly cost of using each model at three different usage levels.

We kept the input-to-output ratio constant at 5:1 and used the standard token prices listed on BestHunt.

At 100 million input tokens and 20 million output tokens, Opus would cost $880 per month.

Sonnet would cost $400 for the same workload, while Haiku would cost just $21.

If a business switched from Opus to Sonnet without changing its token consumption, it would save $480 per month, or $5,760 annually.

Moving from Opus to Haiku would save $859 per month, equivalent to $10,308 annually.

But cost savings alone don’t make a model a good alternative, so we also compared their performance:

• Sonnet's overall quality score is close to Opus's, making it a more promising alternative for businesses trying to reduce costs while maintaining a similar level of performance.

• Haiku offers much greater savings, but the wider quality gap means businesses would need to check whether it can complete their tasks reliably.

These calculations also assume that all three models use the same number of tokens. In practice, a model might need more retries, generate longer responses, or use additional tool calls.

Batch processing can reduce Claude costs without changing models

After comparing the standard prices, we looked at whether businesses could save money without switching to a different Claude model.

One option is batch processing.

Anthropic offers discounted pricing for requests that can be processed asynchronously. We compared the batch and standard rates for all three models.

Prices per million tokens, based on BestHunt's tracked listings.

One interesting thing we noticed was that Opus batch costs the same per token as standard Sonnet. So a business that needs Opus's capabilities but doesn't require immediate responses could reduce its token bill by switching to batch processing.

We calculated the difference using our earlier workload of 100 million input tokens and 20 million output tokens.

Opus batch reduces the estimated monthly bill from $880 to $400, a saving of approximately 55% at the tracked rates. But switching from standard Opus to Sonnet batch would reduce the bill further, from $880 to $200. That's a reduction of approximately 77%.

We found this comparison useful because it shows that businesses have two separate decisions to make, like which model to use and how to process their requests.

For example, for applications such as scheduled document analysis, bulk classification, or overnight data processing, batch pricing could substantially reduce costs.

However, it isn't suitable for applications like customer-facing chatbots, interactive coding assistants, and real-time support tools that generally need immediate responses.

For those workloads, the standard pricing comparison remains more relevant.

Prompt caching can make repeated context much cheaper

Many AI applications repeatedly send the same information to a model. This could include system instructions, product documentation, knowledge bases, or conversation history.

Without caching, that information may be billed at the standard input rate each time it is processed. With caching, previously stored content can be reused at a discounted rate.

We checked Anthropic's official API pricing for the cache-read rates of all three models.

Opus and Sonnet have a 95% cache-read discount compared with their direct API input rates. Haiku has a 90% discount.

We then calculated how much a business could save if it processed 100 million input tokens per month, with 60% of those tokens served from cache.

Under these assumptions, caching would reduce input costs by 57% for Opus and Sonnet, and 54% for Haiku. The savings would be particularly relevant for applications that repeatedly use large amounts of context.

However, the calculation doesn't include the cost of creating or refreshing the cache. Actual savings would depend on how often the application reuses the cached information.

This gave us another reason to look beyond standard token prices when comparing models.

Haiku's long-context pricing can reduce its cost advantage

Haiku was by far the cheapest model in our initial comparison, so we looked more closely at its pricing conditions.

One detail was that Haiku 5.5 has different direct API rates depending on prompt length. 

For prompts up to 100,000 tokens, it costs $0.10 per million input tokens and $0.50 per million output tokens. For prompts exceeding 100,000 tokens, the rates increase to $0.50 and $2.50.

That's a fivefold increase for longer prompts. 

For example, a business using Haiku to classify short customer messages might benefit from its lowest pricing tier. But a business processing large documents or extensive conversation histories could face considerably higher rates.

This doesn't eliminate Haiku's cost advantage, but it means the savings depend on the type of workload.

What did we learn about Claude's price-to-performance value?

After comparing the three models, we found that each offers a different trade-off between price and overall quality.

• Opus 5.5 ranks highest, with an Arena Elo score of 1389. Its higher price may be justified for demanding tasks where stronger reasoning or coding performance reduces retries and human intervention.

• Sonnet 5.5 stands out because its performance is close to Opus, with an Arena Elo difference of just 13 points, while costing approximately 55% less per token. 

• Haiku 5.5 offers the largest potential savings, but its overall quality score is substantially lower. We would therefore evaluate it for simpler, high-volume tasks rather than assume it can replace Sonnet or Opus across every workload.

Which Claude model would we choose?

Based on the pricing and performance data we analysed, here's how you can approach choosing between the three Claude models:

• You can start with Sonnet 5.5 for general-purpose applications, since its performance is close to Opus while costing approximately 55% less per token.

• For simpler, high-volume tasks like classification, extraction, and routing, you can consider Haiku 5.5 to see whether its lower cost makes up for the difference in performance.

• For complex reasoning and coding tasks, you should compare Opus 5.5 and Sonnet 5.5 to understand whether the additional performance justifies the higher price.

• For agentic workflows, you can test both Opus and Sonnet on the same tasks, comparing completion rates, token consumption, retries, and human intervention.

• You should also check whether batch processing or prompt caching could reduce your costs before deciding to switch models.

The main takeaway from our analysis is that the highest-ranked model isn't necessarily the best-value option for your application. A cheaper model that performs well enough for your specific tasks could be the more economical choice.

Explore the BestHunt AI Models leaderboard to compare Claude Opus, Sonnet, and Haiku against other models, calculate costs for your own token usage, and see whether a cheaper alternative offers the performance you need.