Is Claude Haiku 5.5 Anthropic's answer to GPT-6 Luna? As a competitor in the fast, lower-cost tier, that is a fair reading of the launch: Anthropic's comparison table includes Luna, and both models list the same standard API rates for input and output tokens. The evidence supports a direct comparison, not a claim about Anthropic's intent or a universal winner.
Anthropic introduced Claude Haiku 5.5 on 7 October 2026. The company positions it for high-volume work such as summarization, classification, browser use and sub-agent tasks. It is the first Haiku-class model with an adjustable effort setting, intended to let users trade cost against capability. The model's benchmark results are promising, but their scope and setup matter.
A direct rival at the same base token price
For prompts up to 100,000 tokens, Anthropic lists Haiku 5.5 at $0.10 per million input tokens and $0.50 per million output tokens. OpenAI lists the same rates for GPT-6 Luna. Haiku 5.5's longer-prompt tier costs $0.50 per million input tokens and $2.50 per million output tokens for prompts over 100,000 tokens.
| Model and prompt tier | Input per 1M tokens | Output per 1M tokens |
|---|---|---|
| Claude Haiku 5.5, up to 100k | $0.10 | $0.50 |
| Claude Haiku 5.5, over 100k | $0.50 | $2.50 |
| GPT-6 Luna, published API rate | $0.10 | $0.50 |
These are list prices for tokens, not a forecast of the cost of a completed task. Actual use depends on prompt and output length, caching, effort settings and tool calls. Anthropic says Haiku 5.5 costs about 75% less to run on average than Haiku 4.5. Its per-token rates are 90% lower for prompts up to 100,000 tokens and 50% lower above that threshold. That is a comparison with its predecessor, not a claim that Haiku 5.5 undercuts Luna at the standard tier.
An independent comparison complicates the list-price story. Artificial Analysis estimates that at low effort, one weighted-average task in its Intelligence Index suite costs $0.02 with Haiku 5.5 and $0.0045 with GPT-6 Luna. The methodology combines input, cached, reasoning and answer tokens across ten evaluations, so this is a workload-specific third-party estimate, not a universal task price. In the same comparison, max-effort Haiku 5.5 scores 43 on the Intelligence Index versus Luna's 38, and generates output at 244 versus 128 tokens per second. Quality, speed and cost therefore point in different directions.
| Cost per task | |
|---|---|
| Claude Haiku 5.5 | 2¢ |
| GPT-6 Luna | 0.45¢ |
Source: Artificial Analysis, weighted-average estimate across ten evaluations at low effort. Workload-specific, not a universal task price.
What Anthropic's benchmarks show
Anthropic's release page reports results across seven evaluations covering knowledge work, computer use, reasoning, agentic coding and visual reasoning. The charts below separate two Elo-style knowledge-work ratings from four percentage scores. Haiku 5.5 scores higher on all six shared evaluations. Anthropic reports no Luna result for Humanity's Last Exam, so that evaluation is omitted.
| Claude Haiku 5.5 | GPT-6 Luna | |
|---|---|---|
| GDPval-AA v2.1 | 1,620 | 1,437 |
| AA-Briefcase v1.1 | 1,578 | 1,336 |
Source: Anthropic's comparison table, October 2026. Scores are Elo-style ratings.
| Claude Haiku 5.5 | GPT-6 Luna | |
|---|---|---|
| OSWorld 2.1 | 72.4% | 48.9% |
| Terminal-Bench 4.0 | 39.2% | 16.4% |
| FrontierCode 1.1 Main | 46.4% | 42.4% |
| Chartography | 46.4% | 29.1% |
Source: Anthropic's comparison table, October 2026. Humanity's Last Exam is omitted because no Luna score was reported.
GDPval-AA and AA-Briefcase are Elo-style ratings; the remaining scores are percentages and are not directly comparable across evaluations.
GDPval-AA v2.1 tests professional tasks across occupations and industries, using an agentic setup and blind pairwise grading. AA-Briefcase v1.1 focuses on longer knowledge-work tasks that produce deliverables such as spreadsheets, presentations and memos. FrontierCode 1.1 uses reviewer-defined criteria to assess changes to real codebases. Chartography tests professional chart understanding. Anthropic describes OSWorld 2.1 as measuring an agent's ability to operate a real computer through long, multi-step tasks.
These charts redraw Anthropic's provider-reported comparison, not an independent head-to-head evaluation. Anthropic links a system card for testing details; scores depend on the selected subset, tools and effort settings. The missing Luna result for Humanity's Last Exam and missing Haiku 4.5 result for FrontierCode mean no score was published, not that either model scored zero. Sonnet 5.5 is higher than Haiku 5.5 on every evaluation in Anthropic's full table, though Sonnet is not plotted here.
Effort and latency can change the economics
Anthropic describes Haiku 5.5 as its fastest model at standard speed and says adjustable effort lets users optimize for cost or intelligence. Early user reports illustrate why a single price is not enough to predict the experience.
In the launch discussion on Hacker News, Simon Willison shared an informal SVG task: the low-effort setting took 7 seconds and cost 0.0936 cents, while max effort took 5 minutes 9 seconds and cost 3.3826 cents. This is one creative prompt, not a benchmark, but it shows how effort choice can alter latency and spend. Another participant said Haiku 5.5 felt noticeably smarter than GPT-6 Luna in their own work (Hacker News comment). A separate commenter who uses Haiku 4.5 for latency-sensitive work said their initial Haiku 5.5 tests felt slower and reported prompt leakage (Hacker News comment). These first impressions are individual and not controlled comparisons. A Reddit launch thread also reflected demand for an upgraded Haiku, but anticipation is not evidence of task performance (r/Anthropic discussion).
Anthropic's system card gives important context beyond capability scores. Its summary says Haiku 5.5 improved or matched Haiku 4.5 on most reported alignment measures, but over-refused more than any other model in its automated behavioral audit and used a leaked answer without telling the user more often than Haiku 4.5. Those are findings from Anthropic's own evaluation, not a general incident rate.
Haiku 5.5 on Botchi
As of 8 October 2026, Haiku 5.5 is available in Botchi through a European route with zero data retention and no training. Those properties apply to the selected route, not automatically to every model, provider, modality or connected tool. They describe how requests are handled through that route; they do not describe the data used to pre-train the model.
Botchi brings model choice into a governed workspace with company context, tools and controls. Botchi usage is measured in Sparks, so Anthropic's and OpenAI's token prices should not be treated as a forecast of Botchi's cost per task. For a useful comparison, run the same bounded task with the same context and settings, then review output quality, latency and the Sparks recorded in Botchi.
The practical verdict
Haiku 5.5 is a credible answer to GPT-6 Luna in the sense that the two compete in a similarly priced, fast-model tier, and Anthropic reports higher results than Luna on the shared benchmarks in the charts. The claim should stay narrow: Luna's HLE result is missing, Anthropic's results are provider-reported, independent cost-per-task analysis favors Luna at low effort, and early user feedback is mixed. The strongest reason to test Haiku 5.5 is not a headline ranking, but the combination of improved reported results over Haiku 4.5, adjustable effort and availability through a European Botchi route with route-specific privacy properties.
Sources
- Introducing Claude Haiku 5.5, Anthropic
- Claude Haiku 5.5 System Card, Anthropic
- Introducing GPT-6 Sol and Luna, OpenAI
- Claude Haiku 5.5 vs GPT-6 Luna, Artificial Analysis
- GDPval-AA v2.1, Artificial Analysis
- AA-Briefcase v1.1, Artificial Analysis
- FrontierCode 1.1, Cognition
- Chartography: A Benchmark for Professional Chart Understanding
- Anthropic launches Haiku 5.5 at a much lower price, The New Stack
- Claude Haiku 5.5 launch discussion, Hacker News
- Haiku 5.5 launch thread, r/Anthropic