Claude Haiku 4.5 Deep Dive: Cost, Capabilities, and the Multi-Agent Opportunity

The 25% price increase from Haiku 3.5 to Haiku 4.5 is modest compared to the capability gains, but it does shift the model's position in the cost-performance landscape. Haiku 3.5 was often the default choice for production workloads that didn't require significant intelligence, even after the price increase it suffered compared to Claude 3 Haiku. 

The main problem with the price increase isn't the number itself, but the trend that it's setting. Claude 3 Haiku used to cost $0.25/$1.25 per million input/output tokens, one twelfth of the price of Claude 3 Sonnet. The next iteration in the Haiku family, Haiku 3.5, raised the price to $0.80/$4 per million tokens, which is slightly over three times more expensive than Claude 3 Haiku. Now the prices are up by 25%, and the Haiku family, which used to be 12 times cheaper than the Sonnet, is now merely 3 times cheaper. It's still competitive, especially considering it's not far behind on capabilities, but we're concerned about the continued increase.

With Haiku 4.5, you might be more selective. It’s the right choice when you need advanced capabilities like extended thinking or computer use, or when you want performance that’s comparable to Sonnet 4 and close to Sonnet 4.5. For simpler, routine tasks where the intelligence gap isn’t significant, it often makes more sense to stick with Haiku 3.5 or another more cost-efficient model.

If you're curious to find out how much Haiku 4.5 would cost your organization, explore our dynamic token cost model tool and calculate it for yourself.

Computer Use at Scale

Computer use gives Claude Haiku 4.5 the ability to control applications through screenshots and actions. The model analyzes a screenshot of the current screen state, decides what action to take—such as clicking a button, typing text, scrolling down, or navigating to a URL —and executes that action. The 50.7% success rate on OSWorld represents Haiku 4.5's performance on a benchmark designed to test these capabilities across real-world workflows.

For context, OSWorld evaluates models on tasks like "find the quarterly revenue figure in this financial dashboard" or "fill out this vendor onboarding form with the provided information." These are realistic automation scenarios where you need to interact with systems that don't have APIs (or where the API doesn't expose the functionality you need). The 50.7% success rate indicates that Haiku 4.5 completes about half of these tasks successfully under benchmark conditions.

That success rate is impressive compared to where computer use capabilities were six months ago, but it's not reliable enough for autonomous production deployment. It requires human oversight, approval workflows, and validation layers. The 49.3% failure rate matters; it means roughly half the time, something goes wrong. The model might click the wrong button, misinterpret the screen state, or fail to complete a multi-step sequence.

Today, computer use is best suited for workflows that would otherwise require manual effort and where the impact of errors is manageable. It is not appropriate for mission-critical, real-time operations, but for tasks where a human might spend hours clicking through interfaces. A 50.7% success rate, combined with proper oversight, can deliver meaningful value.

Multi-Agent Orchestration Patterns with Haiku 4.5

The most interesting opportunity with Haiku 4.5 involves multi-agent architectures where you combine models strategically based on their strengths. A common pattern we're using for production systems uses a state-of-the-art model like Sonnet 4.5 for high-level planning and orchestration, with less expensive models like Haiku 4.5 handling the execution of individual sub-tasks. This architecture takes advantage of Sonnet 4.5's superior reasoning for complex planning while leveraging Haiku 4.5's cost efficiency for scaled execution.

Another pattern we're considering implementing uses Haiku 4.5 for real-time user-facing interactions while Sonnet 4.5 handles background analysis and learning. A customer support system may use Haiku 4.5 to respond to customer inquiries in real-time, offering low latency, reasonable cost, good-enough intelligence for most questions. When Haiku 4.5 encounters a complex issue it can't resolve, it can escalate to Sonnet 4.5. Meanwhile, Sonnet 4.5 may run nightly batch processing to analyze the day's support interactions, identify patterns, and update the knowledge base that Haiku 4.5 uses. This would provide you with fast, cost-effective real-time responses while still benefiting from cutting-edge intelligence where it matters.

Free-tier economics represents another significant opportunity. Building a free-tier AI product with Sonnet 4.5 is economically challenging because you're paying $3/$15 per million tokens for interactions from non-paying users. With Haiku 4.5 at $1/$5, the economics become more viable while still providing near-frontier intelligence. You're not compromising as much on capability compared to using Haiku 3.5, but you're also not absorbing Sonnet-level costs for users who might not convert to paid plans.

When to Choose Haiku 4.5 (vs Sonnet 4.5, Opus 4.1, or Haiku 3.5)

The model selection decision comes down to matching your use case requirements against the performance-cost-capability tradeoffs of each model. Here's the framework we're using at Caylent when advising customers.

Choose Haiku 4.5 when:

• You need near-frontier performance for sub-agent tasks in multi-agent architectures
• You're building free-tier products where you need good intelligence without Sonnet-level costs
• Latency matters, and you don't need the absolute highest quality (Haiku 4.5 is faster than Sonnet 4.5)
• You specifically need extended thinking or computer use at an accessible price point
• You're running high-volume workloads where quality requirements sit between Haiku 3.5 and Sonnet 4.5

Choose Sonnet 4.5 when:

• You're building the primary planning agent in a multi-agent system
• You need the highest quality reasoning and you've validated that Sonnet 4.5 consistently outperforms Haiku 4.5 for your use case
• You're working on complex coding tasks where the 77.2% SWE-bench score (vs 73.3% for Haiku 4.5) matters
• Budget allows for $3/$15 pricing, and quality is the primary optimization metric

Choose Opus 4.1 when:

• Your prompts are already optimized for Opus 4.1's specific strengths, and you've validated that it performs better than Sonnet 4.5 for your use case
• You're working on specialized creative or analytical tasks where Opus 4.1 has demonstrated advantages
• Cost is not a primary constraint, and you need the highest-quality output for low-volume, high-value work

Stay with Haiku 3.5 when:

• You're running simple classification, extraction, or straightforward question-answering at high volume
• The intelligence gap between Haiku 3.5 and Haiku 4.5 doesn't matter for your use case
• You've validated that Haiku 3.5 meets your quality requirements, and the 25% cost increase for Haiku 4.5 doesn't justify the capability gains
• You don't need extended thinking, computer use, or the enhanced context awareness features

Should I Migrate from Haiku 3.5 to Haiku 4.5?

The migration decision depends on whether the capability improvements justify the 25% cost increase for your specific workloads. If your use cases involve complex reasoning, multi-step workflows, or tasks where Haiku 3.5's limitations have been causing problems, the upgrade makes strong sense—you're getting Sonnet 4-level intelligence in the efficient model tier. For simple classification or extraction tasks where Haiku 3.5 already performs well, staying on the older model might be more economical.

What's the Difference Between Haiku 4.5 and Sonnet 4.5?

Haiku 4.5 delivers performance comparable to Sonnet 4, the previous state-of-the-art model, while Sonnet 4.5 represents the current frontier. On SWE-bench Verified, Sonnet 4.5 scores 77.2% versus Haiku 4.5's 73.3%. 

There’s also a significant cost difference: Sonnet 4.5 costs $3/$15 per million tokens while Haiku 4.5 costs $1/$5, a 3x difference on both input and output. Haiku 4.5 also processes requests faster, which is a key advantage for latency-sensitive applications.

In practical terms, the decision comes down to use case. Choose Sonnet 4.5 when you need the highest possible quality for complex reasoning, strategic planning, or critical decision-making. Choose Haiku 4.5 for execution tasks and high-volume workloads where near-frontier performance at a lower cost delivers better overall value.

Is Haiku 4.5 Reliable for Production Workloads?

Haiku 4.5 performs reliably at a level comparable to Sonnet 4 for text generation, analysis, and coding tasks. For computer use specifically, the 50.7% success rate isn't reliable enough for autonomous operation. The general pattern: Haiku 4.5 is production-ready for tasks where you'd previously have used Haiku 3.5 or Sonnet 4.

Conclusion

Claude Haiku 4.5 fills a spot in the Claude family that has been vacant for a year, ever since the last Haiku model (Haiku 3.5) came out. The price increase is an unpleasant surprise, but it's not as significant as the increase in capabilities. At Caylent we were still using Claude Haiku 3.5 in some workloads, simply because there are instances where you don't need the best levels of intelligence. Claude Haiku 4.5 might not replace Haiku 3.5 where it has already proven useful, but with the significant increase in intelligence we've seen from Sonnet 4.5, a cheaper model that's still reasonably capable is a very welcome addition.

Ready to put Claude Sonnet 4.5 to the test? Try it out in Bedrock Battleground. Caylent’s interactive LLM comparison tool that lets you evaluate, benchmark, and select models across real-world scenarios. And if you’re curious how model performance stacks up against cost, explore our Tokenomics Dashboard to see exactly what each LLM could cost your organization in production.

Caylent helps organizations design, implement, and scale generative AI solutions—leveraging our deep expertise in data, machine learning, and AWS technologies to turn cutting-edge models like Claude Haiku 4.5 into real business impact.