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Haiku 5.5 Benchmark
Compare Claude Haiku 5.5 benchmark scores, price tiers and context limits — each figure dated and sourced, with no hands-on test claimed.
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Haiku 5.5 Benchmark Results at a Glance
Launch-day scores, token pricing tiers and cost-per-effort figures, each stored beside the run configuration and the date it was retrieved.
- What the Launch Scores Really TrackElo, partial-credit, coding and accuracy values sit on different scales, so averaging them produces a meaningless number.
- Cost Maths for High-Volume RepetitionDesigned for extraction, tagging and summarisation jobs that run thousands of times a day.
- Two Footnotes That Rewrite the TablesDiscounted rates end at 100K tokens, and the 72.4% computer-use figure counts partial credit rather than completed runs.
Using the Haiku 5.5 Benchmark in Three Steps
A three-step method for converting published figures into an actual workload decision.
Haiku 5.5 Benchmark Findings in Detail
Specifications, pricing tiers, coding outcomes, computer-use scores and third-party index data for Claude Haiku 5.5, all drawn from published sources.
Context Window and Output Ceiling
Text and images go in, text comes out, and adaptive thinking stays enabled. A 1,000,000-token context and 128,000-token output ceiling make Haiku 4.5's 200,000 and 64,000 look small.
Pricing Split by Prompt Length
Pass 100,000 prompt tokens and input, output and caching all move to a tier priced five times higher. OpenAI's base Standard tier, by contrast, extends to 272,000 input tokens.
Terminal-Bench 4.0 Finished Tasks
At 39.2% on launch, Haiku clears Luna by a wide margin yet still trails the published Sol entries. Each task run gets an eight-hour agent timeout, and any 2.1 or 3.0 figures come from an earlier experiment.
FrontierCode Merge Quality and Effort Spend
Scoring weighs correctness, tests, scope, style and repository conventions. Haiku reaches 46.4% at max effort and 41.6% at medium for roughly $0.13 per rollout; stepping from xhigh to max doubles the cost for half a point.
OSWorld 2.1: Partial Credit vs. Strict Completion
The run spans 82 offline tasks, five attempts each, 1080p screenshots and a 500-step cap. If a workflow has to complete, quote the 37.1% strict figure.
Third-Party Index Scores and Tool Gains
Artificial Analysis lists Haiku at all five effort levels on Intelligence Index v4.3.2, with task costs rounded. On HLE it moves from 45.9% unaided to 57.4% with tools — a lift that comes from the harness, not the model alone.
Haiku 5.5 Benchmark Questions Answered
Answers on Claude Haiku 5.5 scores, token pricing, context limits and how the published tables differ.
Where does Haiku 5.5 stand next to GPT-6 Luna and GPT-6 Sol?
Across knowledge tasks, screenshot work and small coding jobs, Haiku and Luna trade blows. Sol and Sol 6.1 charge more and justify it on coding, and Sonnet 5.5 still tops most of the rows.
Why should the 72.4% computer-use figure not be read as a success rate?
Require every checkpoint and the same run drops to 37.1%. An agent may find and edit the correct file, then save it in the wrong format or halt before the final action. Always add an end-state check.
At what point does Haiku 5.5 move into the higher price tier?
The lowest rates apply only up to 100,000 prompt tokens; beyond that every category costs five times as much. Anthropic's 90%, 50% and roughly 75% savings claims each measure against a different baseline.
Is local hosting possible for Haiku 5.5, and how much VRAM would that take?
The published specifications give no parameter count, active-parameter figure, layer count or training compute. 'Small' describes a product tier rather than an architecture, so any VRAM estimate would be guesswork.
Does raising reasoning effort always buy better results?
No. Sol 6.1 scores higher at medium than at max, and Haiku's 0.2-point lead over Sonnet at max is too narrow to call a win. More effort also means more tokens, more latency and more room to drift.
Why do identical models post different figures from table to table?
Sonnet shows 70.6% in Anthropic's launch table and 61.8% in the public leaderboard snapshot, where Haiku had no visible row at all. Artificial Analysis removed its 'Default Fallback' tag, while Anthropic's migration guide still states that no server-side fallback exists.
Put the Haiku 5.5 Benchmark to Work on Your Own Tasks
Choose a single workload, fix the effort setting, and measure Haiku 5.5 against the quality bar you actually need.