Claude Haiku 5.5 vs GPT-6 Luna: Pricing, Benchmarks, and Which AI Model Is Better?

Updated: October 10, 2026

Claude Haiku 5.5 and GPT-6 Luna are two of the most affordable AI models from Anthropic and OpenAI.

Both are designed for fast, cost-efficient AI workloads, including coding assistance, document processing, customer support, and AI agents.

At first glance, they look remarkably similar. Both start at just $0.10 per million input tokens and $0.50 per million output tokens. Both support approximately one million tokens of context.

But there are important differences in performance, pricing thresholds, and real-world operating costs.

So, which model should you choose?

In this comparison, we'll examine their specifications, API pricing, independent benchmarks, coding performance, and practical use cases.

Quick Verdict: Claude Haiku 5.5 vs GPT-6 Luna

Claude Haiku 5.5 is the stronger choice on several independent intelligence and coding benchmarks, while GPT-6 Luna can be more economical for long prompts and certain automated workflows.

Here's the quick breakdown:

• Overall intelligence: Claude Haiku 5.5 leads on the Artificial Analysis Intelligence Index at matched medium reasoning effort.
• API pricing: Both models have identical base input and output prices.
• Long-context pricing: GPT-6 Luna has a significant pricing advantage for prompts between 100K and 272K tokens.
• Coding: Claude Haiku 5.5 performs better on some agentic coding benchmarks, while results are mixed on other coding evaluations.
• Automation: GPT-6 Luna leads on the independent AutomationBench-AA evaluation at medium effort.
• Cost per completed task: GPT-6 Luna uses fewer output tokens on average in Artificial Analysis's evaluation, resulting in lower measured costs per task.

For short, high-volume requests, both models deserve consideration.

For complex coding and agentic tasks, Claude Haiku 5.5 is a promising starting point.

For long-document processing and cost-sensitive automation, GPT-6 Luna may provide better economics.

These recommendations are based on available benchmark evidence, not universal performance guarantees. Source: Artificial Analysis.

Claude Haiku 5.5 vs GPT-6 Luna: At a Glance

Before examining performance, let's compare their technical specifications.

Sources: Anthropic Model Documentation and OpenAI GPT-6 Luna Documentation.

Both models target similar workloads.

Claude Haiku 5.5 is Anthropic's latest small model, optimized for classification, extraction, routing, and other latency-sensitive tasks.

GPT-6 Luna is OpenAI's efficient GPT-6 model, designed for focused, high-volume applications.

The most important differences emerge when we look beyond the specifications.

Pricing Comparison: Which Model Is Cheaper?

Both models advertise the same starting price.

However, their pricing structures differ significantly for longer prompts.

API Pricing Per Million Tokens

Prices are in USD per million tokens using the standard API pricing tier.

The distinction is important.

Claude Haiku 5.5 switches to a higher pricing tier when the prompt exceeds 100,000 tokens.

GPT-6 Luna maintains its base pricing until the input exceeds 272,000 tokens.

Above their respective thresholds, the higher rates apply to the request, not just the tokens beyond the threshold.

These rules are documented in the official Anthropic pricing information and OpenAI pricing documentation.

Real-World API Cost Examples

To understand the difference, let's calculate several example workloads.

The calculations below use the same assumed billable token counts for both models. They exclude caching, additional reasoning tokens, tool usage, and other processing charges.

The first scenario represents a short request, such as a customer-support response.

Both models cost exactly the same under these assumptions.

The second scenario represents a long-document task.

At 150,000 input tokens, Claude Haiku 5.5 has already entered its higher pricing tier, while GPT-6 Luna remains in its standard tier.

As a result, GPT-6 Luna costs approximately one-fifth as much for the example request.

The third scenario shows that GPT-6 Luna retains a pricing advantage even after both models enter their higher pricing tiers.

Why Token Usage Matters

Identical API rates do not necessarily mean identical bills.

Two models can consume different numbers of tokens when completing the same task.

Reasoning tokens can also contribute substantially to the final cost.

For example, Artificial Analysis measured approximately $0.05 per task for Claude Haiku 5.5 and $0.02 for GPT-6 Luna at medium reasoning effort in its Intelligence Index evaluation.

This is specific to that benchmark workload, but it demonstrates why developers should measure cost per successful task, not just the advertised price per token. Source: Artificial Analysis.

Benchmark Comparison: Which Model Performs Better?

Benchmarks help us understand how AI models perform across different types of tasks.

However, benchmark results should be interpreted carefully.

Scores may vary depending on reasoning effort, evaluation tools, test versions, and execution environments.

For a more consistent comparison, we'll begin with independent results from Artificial Analysis using medium reasoning effort for both models.

Independent Benchmark Results

Source: Artificial Analysis — Claude Haiku 5.5 vs GPT-6 Luna, Medium Effort.

These results reveal several useful patterns.

First, Claude Haiku 5.5 has the stronger overall intelligence score.

Its Intelligence Index score of 34 exceeds GPT-6 Luna's 30.

It also performs better on several knowledge-work and agentic coding evaluations.

Second, GPT-6 Luna remains competitive on specialized tasks.

It outperforms Haiku 5.5 on AutomationBench-AA and slightly leads on SciCode and AA-LCR.

Third, no single benchmark determines which model is best for every application.

A model that performs better on terminal-based coding tasks may not be the best choice for automating business software or processing large document collections.

Your workload remains the deciding factor.

Coding Performance: Claude Haiku 5.5 vs GPT-6 Luna

Coding is an important use case for both models.

Developers increasingly rely on smaller AI models for code generation, debugging, test creation, and routine software maintenance.

Claude Haiku 5.5 shows promising results in agentic coding evaluations.

Terminal-Bench Performance

In Artificial Analysis's medium-effort testing:

• Claude Haiku 5.5 scored 15%.
• GPT-6 Luna scored 3%.

Terminal-Bench evaluates coding agents working through terminal-based tasks that require planning, tool usage, and multi-step execution.

These results suggest Claude Haiku 5.5 may be better suited to certain agentic coding workflows at this effort level.

FrontierCode Results

Anthropic's published comparison also reports results from FrontierCode 1.1 Main.

FrontierCode focuses on software engineering tasks where generated changes must satisfy real codebase requirements.

Haiku 5.5 leads in these published results, although the comparison depends on the evaluation configurations and coding harnesses used.

Source: Introducing Claude Haiku 5.5.

Which Should Developers Choose?

For code editing, debugging, and terminal-based agents, Claude Haiku 5.5 is worth testing first.

For focused code generation and workflows where cost matters more than complex reasoning, GPT-6 Luna remains a viable alternative.

However, neither model should be selected based on a single coding benchmark.

Developers should compare both models using their actual repositories, tests, and development environments.

Speed and Latency: Which Model Responds Faster?

Speed is another important factor when choosing an AI model.

However, there are several different ways to measure speed.

Time to First Token (TTFT) measures how long users wait before receiving the first generated token.

Output Speed measures how quickly a model produces tokens once generation begins.

End-to-End Latency measures the total time needed to complete a request.

These measurements are not interchangeable.

Anthropic describes Claude Haiku 5.5 as its fastest model to date, making it suitable for customer support, browser interactions, and other latency-sensitive applications.

GPT-6 Luna is also designed for efficient responses.

But their actual performance depends on the reasoning effort, prompt length, output length, API provider, and execution environment.

One third-party experiment from Kingy AI reported median completion times of 6.83 seconds for Claude Haiku 5.5 and 2.92 seconds for GPT-6 Luna across its bounded test tasks.

These results are specific to its test configuration and should not be treated as universal API latency measurements.

For production applications, developers should measure both response time and answer quality using representative requests.

Source: Kingy AI's 160-response comparison.

Real-World Use Cases: Which Model Should You Choose?

Different workloads emphasize different combinations of quality, speed, and cost.

Here are several practical scenarios.

1. Customer Support Chatbots

Customer support systems often process thousands of short requests.

Common tasks include answering product questions, summarizing support tickets, and identifying the correct knowledge-base articles.

Both models are suitable candidates.

Because short requests have identical base token prices, the better choice depends primarily on response quality, latency, and actual token consumption.

Recommendation: Test both models using real support conversations and measure accuracy, cost, and response time.

2. Coding Assistants

Coding assistants must understand instructions, modify code, explain errors, and sometimes interact with development tools.

Claude Haiku 5.5 has stronger results on some agentic coding benchmarks.

This makes it a reasonable candidate for coding agents and repository maintenance.

Recommendation: Start by evaluating Claude Haiku 5.5, but test GPT-6 Luna for focused tasks where lower operating costs may be more important.

3. Long Document Summarization

Applications that analyze legal contracts, financial reports, transcripts, or large document collections often rely on long prompts.

This is where the pricing difference becomes particularly important.

Claude Haiku 5.5 enters its higher pricing tier above 100K prompt tokens.

GPT-6 Luna retains its base pricing until 272K input tokens.

Recommendation: GPT-6 Luna deserves serious consideration for long-document workloads, especially when prompts regularly exceed 100K tokens.

4. Data Extraction and Classification

Data extraction applications convert unstructured text into structured information.

Examples include:

• Extracting invoice details.
• Categorizing customer feedback.
• Identifying entities in documents.
• Producing structured JSON responses.

Both models are designed for high-volume tasks.

At relatively short input lengths, their base pricing is identical.

Recommendation: Evaluate both using labeled examples and compare extraction accuracy, schema validity, and cost per successful result.

5. AI Agents and Workflow Automation

AI agents often combine reasoning with external tools.

Typical tasks include searching documents, updating records, interacting with business software, and executing multi-step workflows.

Claude Haiku 5.5 performs well on several agentic coding and computer-use benchmarks.

GPT-6 Luna performs better on AutomationBench-AA at medium effort.

The distinction suggests that different types of agents may favor different models.

Recommendation: Test Claude Haiku 5.5 for coding-oriented agents and GPT-6 Luna for business workflow automation, then compare them on your actual tasks.

How to Test Both Models for Your Application

Public benchmarks are useful, but real-world performance varies considerably.

A practical evaluation should use the same representative task set for both models.

Start by collecting 20–50 requests from the workflow you want to automate.

Then evaluate:

Run the same requests multiple times.

Where possible, use comparable reasoning settings and identical input data.

Keep the model versions, prompts, outputs, and evaluation criteria recorded.

Most importantly, avoid choosing a model solely because it performed well on a benchmark unrelated to your application.

Final Verdict: Claude Haiku 5.5 or GPT-6 Luna?

Claude Haiku 5.5 and GPT-6 Luna are both compelling options for developers seeking affordable AI capabilities.

They share similar technical specifications and identical starting API prices.

But their strengths differ.

Choose Claude Haiku 5.5 if:

• You prioritize stronger performance on several coding and reasoning benchmarks.
• You're building agentic coding workflows.
• Your requests generally remain below 100K tokens.
• You want to integrate a smaller model into the Anthropic ecosystem.

Choose GPT-6 Luna if:

• Your applications regularly process prompts over 100K tokens.
• You want lower measured costs per task for certain workloads.
• You're building business workflow automation.
• Your infrastructure already relies on OpenAI APIs and tools.

Overall, Claude Haiku 5.5 has the edge on the independent aggregate benchmark at matched medium effort, while GPT-6 Luna offers significant advantages in long-prompt pricing and certain automation tasks.

There is no universal winner.

The best AI model is the one that delivers the accuracy your application needs at an acceptable cost and latency.

Frequently Asked Questions

Is Claude Haiku 5.5 better than GPT-6 Luna?

Claude Haiku 5.5 performs better on several independent intelligence and coding benchmarks. However, GPT-6 Luna leads on some automation-focused evaluations and can be more cost-effective for certain workloads.

Is Claude Haiku 5.5 cheaper than GPT-6 Luna?

Both models start at $0.10 per million input tokens and $0.50 per million output tokens. Claude Haiku 5.5 has a higher pricing tier above 100K prompt tokens, while GPT-6 Luna maintains its base pricing until 272K input tokens.

Which is better for coding, Claude Haiku 5.5 or GPT-6 Luna?

Claude Haiku 5.5 performs better on some agentic coding benchmarks, including Terminal-Bench 4.0 at medium effort. However, performance varies across coding tasks and evaluation configurations.

Does GPT-6 Luna support a larger context window?

Yes. GPT-6 Luna supports a 1,050,000-token context window, compared with Claude Haiku 5.5's 1,000,000 tokens.

Both models also support a maximum standard output of 128,000 tokens.

Which model is faster?

There is no universal answer. Performance varies with reasoning settings, request size, output length, and provider infrastructure.

Developers should measure end-to-end response time using representative application workloads.

Is Claude Haiku 5.5 suitable for AI agents?

Yes. Anthropic positions Claude Haiku 5.5 for tasks such as classification, extraction, routing, and coding subagents.

It also supports adjustable reasoning effort, making it suitable for applications that need to balance intelligence and cost.

Can I use Claude Haiku 5.5 and GPT-6 Luna through an API?

Yes.

Claude Haiku 5.5 is available through the Anthropic API using claude-haiku-5-5.

GPT-6 Luna is available through the OpenAI API using gpt-6-luna.

Sources and Methodology

This article uses publicly available documentation and independent evaluation results.

Primary references:

1. Anthropic — Introducing Claude Haiku 5.5
2. Anthropic — Claude Haiku 5.5 Model Documentation
3. OpenAI — Introducing GPT-6 Sol and Luna
4. OpenAI — GPT-6 Luna Model Documentation
5. Artificial Analysis — Model Comparison
6. Kingy AI — Independent Task Comparison

Methodology note: The article does not claim original hands-on API testing. Benchmark data is attributed to the organization that conducted or published the evaluation. Illustrative pricing calculations exclude additional reasoning tokens, caching behavior, tool fees, and special processing rates.

Pricing and model specifications were checked on October 10, 2026.