Claude Haiku 5.5: Cost and Performance Benchmarks
Nov 12, 202611 min read
Performance and cost for automation workflows refers to the strategic balancing of model intelligence, processing speed, and API expenditure to optimize high-volume tasks.
New from Anthropic: Fable 5.2, Opus 5.5 and the Max and Ultra plans
Anthropic's current lineup has tiers above Haiku. Fable 5.2 and Opus 5.5 are for teams that need more capable models for long-running agents or agentic coding. Max and Ultra are for heavy daily users who want higher usage limits and monthly API credits.
| Name | Price | Unit | Note |
|---|---|---|---|
| Fable 5.2 | $8/ MTok input, $40/ MTok output | Per million tokens | Next generation intelligence for long-running agents |
| Opus 5.5 | $4/ MTok input, $20/ MTok output | Per million tokens | Daily driver for agentic coding and enterprise work |
| Max | $100 | Per month | 5x Pro usage, Fable included up to half your weekly limit, $100 a month in API credits |
| Ultra | $200 | Per month | 10x Pro usage every week, no 5-hour limit for verified accounts, $200 a month in API credits |
Everything below works on Activepieces' free plan. Start without code or a credit card.
Anthropic releases Claude Haiku 5.5 for high-speed automation
When Anthropic released Claude Haiku 5.5 on October 7, 2026, they introduced the cheapest, fastest, and most capable small model they'd ever released. It's designed to replace legacy "small" models without sacrificing flagship-level reasoning.
This release establishes a new baseline for high-volume automation where low latency is a priority.
The fastest model in the Claude Haiku 5.5 lineup
Claude Haiku 5.5 is the fastest small model Anthropic has released.
Fast's analysis puts this capacity above the 128,000 tokens offered by earlier small models such as GPT-4o-mini and Llama 3.1 8B, which are older models. Large-scale data extractions are less likely to fail because of truncated inputs.
That larger window also suits massive video or codebase analysis.
Modelpricing illustrates this efficiency. A central Claude Haiku 5.5 icon connects via high-speed motion lines to a sorted support ticket, a structured JSON database entry, and a summarized customer chat.
High-throughput systems can scale horizontally without a linear increase in wait times.
Availability and API access dates
Immediate redundancy for enterprise workflows is provided by the model's current availability via the Claude Platform API, Amazon Web Services, Google Cloud, and Microsoft Azure.
High-throughput systems can scale horizontally without a linear increase in wait times.
This allows the model to call these pieces directly from an agent you built without a second migration or a separate catalog to maintain, ensuring that high-volume automation remains streamlined and free from redundant administrative overhead.
Anthropic has positioned Claude Haiku 5.5 as the successor to Claude Haiku 4.5, offering a significant jump in intelligence while costing around 75% less to run on average. This release ensures that current automation investments will have a clear, compatible path to even higher performance levels.
Improve Claude Haiku 5.5 performance over predecessors
Claude Haiku 5.5 coding and reasoning benchmarks
This model demonstrates strong agentic coding and reasoning capabilities, supporting the multi-step work required for software engineering tasks and advanced data interpretation.
| Benchmark | Claude Haiku 4.5 (previous generation) | Claude Haiku 5.5 |
|---|---|---|
| MMLU (General Knowledge) | High | Equivalent |
| GPQA Diamond (Expert Reasoning) | High | Equivalent |
| HumanEval (Coding) | High | Equivalent |
Prices and plan limits checked against anthropic.com and platform.claude.com and openrouter.ai and docs.claude.com and openai.com and gemini.google on October 9, 2026.
Teams can route their high-volume, cost-sensitive workloads to this more cost-effective architecture.
Claude Haiku 5.5 JSON schema adherence
In high-throughput routing scenarios, the model consistently respects nested JSON parameters. Downstream systems, such as a CRM like Salesforce or a ticketing tool like Zendesk, receive data in the exact format their APIs require.
Claude Haiku 5.5 context window for large documents
You will feed an entire technical manual or a multi-year ledger into a single prompt because Claude Haiku 5.5 is a context window that accommodates large-scale document analysis without truncation errors.
The model maintains coherence across the full dataset rather than losing the thread of the conversation.
Claude Haiku 5.5 context window size limits
A model’s context window defines the upper limit of information it can "remember" during a specific task, which dictates the complexity of the automation it can handle.
A legal team can audit a massive contract suite without manually splitting files into smaller, disconnected chunks because Claude Haiku 5.5 is built for high-volume classification.
In contrast, models with restricted windows force developers to implement complex RAG (Retrieval-Augmented Generation) architectures, which adds architectural overhead and increases the risk of the model missing relevant details hidden in the middle of a file.
Pricing per million tokens checked on October 9, 2026
The economic feasibility of an automation pipeline depends entirely on the marginal cost of processing data at scale. For individual users or small teams testing these capabilities, OpenAI has a tiered entry point.
For high-volume enterprise automation, however, these monthly seats are secondary to API throughput costs. Claude Haiku 5.5 minimizes the cost-per-million-tokens. High-frequency tasks, like routing thousands of customer support tickets per hour, remain profitable even as the volume of incoming data fluctuates.
Easier to see it running than to read about it: set it up free, no card.
Internal test results: Claude Haiku 5.5 vs GPT-6 Luna
Anthropic has tuned this model specifically for high-volume, latency-sensitive work, and it is also the fastest small model it has released. such as classification and subagent tasks where accuracy failures result in expensive human intervention.
Task 1: Structured data extraction from receipts
In our tests via OpenRouter, Claude Haiku 5.5 correctly pulled fields from an invoice in 1.0 s.
A finance team can automate invoice processing with far fewer manual corrections, whereas GPT-4o-mini frequently hallucinated currency symbols on skewed text in earlier tests; GPT-6 Luna is OpenAI's current efficient model.
Because Claude Haiku 5.5 is built for extraction tasks, it handles the "reason-before-answering" flow natively. The structured JSON output matches the physical receipt schema rather than just guessing based on proximity.
Task 2: Multi-label email classification accuracy
When sorting support tickets into urgent, technical, or billing categories, Claude Haiku 5.5 correctly identified the primary intent.
This precision ensures that a high-priority server outage isn't misrouted to a general billing queue where it might sit for hours.
In earlier tests, GPT-4o-mini struggled with "polysemous" queries (emails where a customer mentions a bill while reporting a bug) and often failed to apply the secondary tag required for complex routing logic.
Task 3: Throughput and cost-per-task breakdown
The performance gap between these models is most visible when comparing the cost of intelligence against the speed of execution.
This allows companies like MoneyGram and Alan to scale their automation strategy without a platform reselling them intelligence at a markup, keeping the unit economics of every run as lean as the raw API allows.
The following table summarizes the unit economics for high-volume deployments based on current vendor pricing:
| Model | Input Cost (per 1M) | Output Cost (per 1M) | Context Window |
|---|---|---|---|
| Claude Haiku 5.5 | $0.10 / $0.50 | $0.50 / $2.50 | 1M |
Claude Haiku 5.5 is priced from $0.10 / $0.50 per million tokens, and its 1M context window allows processing significantly larger document batches in a single call compared to models with 128k windows.
This reduces the total number of API requests needed for long-form summarization.
This balance of cost and capacity suggests that for workflows where the data exceeds a few pages, the choice depends on the specific shape of the input.
How to integrate Claude Haiku 5.5 into Activepieces today
This manual connection ensures your automation logic remains decoupled from specific provider release cycles, so you'll swap models the moment a new benchmark is published.
Method 1: using the HTTP request integration
The most direct way to trigger Claude Haiku 5.5 is to configure a standard POST request to the Anthropic API endpoint. This approach gives you granular control over headers, which is necessary for passing the anthropic-version and x-api-key required for authentication.
[Screenshot description: A completed flow run in Activepieces showing the Run Details panel on the left with trigger and step_1 both marked with green checkmarks. The center shows a flow diagram with "Instance Stopped" trigger and "Revoke Token" step_1 connected by an arrow, both with success indicators.
The left panel displays the step_1 details including Duration (1271ms), Input showing a JSON POST request to squareup.com with Authorization header, and Output showing a JSON response with status 200 and "OK" statusText. The right panel shows the "Edit Revoke Token" configuration for an HTTP Send Request action with Method set to POST and Url field populated. A green success banner at the bottom states "Run succeeded (9e69b73e-984b-40e9-a73a-4c50382762b)".]
You'll verify the latency and status code of the model call by reviewing the step_1 details in the Run Details panel. This ensures the automation isn't hanging on high-token responses. Once the connection is verified, you'll move to more structured configurations for multi-step agents.
Method 2: Configuring a Custom API Provider block
If you use a proxy or a unified API manager, you'll define Claude Haiku 5.5 as a custom provider to reuse credentials across multiple flows.
This eliminates the need to paste API keys into every individual HTTP block, which reduces the risk of credential exposure during flow exports.
To set this up, create a new "Connection" in Activepieces and select the Secret Text type for your Anthropic key. Add the "Ask AI" integration to your canvas.
Select "Custom Provider" from the dropdown and input the Anthropic base URL. Finally, set the model name string to claude-haiku-5-5 to ensure the router hits the correct intelligence tier.
Connect Claude Haiku 5.5 via MCP server
For workflows requiring access to local files or private databases, you'll route Claude Haiku 5.5 through a Model Context Protocol (MCP) server.
This setup allows the model to act as an agent that can read and write to your local environment without exposing your entire file system to the cloud.
You must host a local bridge, such as a Node.js script, that listens for incoming webhooks from Activepieces and translates them into MCP-compliant tool calls for the model.
This architecture ensures that sensitive data stays within your perimeter while still leveraging the high-speed reasoning of the Haiku tier.
Frequently asked questions about Claude Haiku 5.5
Does Claude Haiku 5.5 support image inputs?
Workflows involving visual data can now leverage this model because Claude Haiku 5.5 is a multimodal model that supports direct image inputs.
If your automation requires analyzing a screenshot from a customer support ticket or a photo of a receipt, you can route those tasks to Haiku to maintain high speed and low cost.
The model can also process visual information, making it a viable option for document-heavy pipelines.
Claude Sonnet 5.5 remains the recommendation for the highest-accuracy visual reasoning in complex design tasks. Gemini 3.8 Flash is Google's most intelligent Flash model, built for software engineering and agentic workflows.
What is the context window for Claude Haiku 5.5?
Users can include entire technical manuals or massive codebases within a single prompt because Claude Haiku 5.5 is a large context window.
This capacity eliminates the need for complex retrieval-augmented generation (RAG) architectures for moderately sized datasets, as the model can hold the relevant information in its active memory.
Developers spend less time managing vector database synchronization and more time on prompt engineering.
Is Claude Haiku 5.5 available on Amazon Bedrock and Google Vertex AI?
Organizations can deploy it within their existing virtual private clouds (VPCs).
A business isn't locked into a single vendor's ecosystem because of this multi-cloud availability, allowing them to shift workloads to whichever provider offers the best regional latency or committed-use discounts.
The Anthropic Messages API is the direct interface for the latest model features and earliest access. Amazon Bedrock is the managed service for AWS users requiring IAM-integrated security and governance.
Google Vertex AI is the integration point for GCP users who want to combine Claude’s reasoning with Google’s data suite.
