Claude Haiku 5.5: Real-World Coding and Agentic Test Results
Claude Haiku 5.5 debugs a real dashboard bug, builds a 3D Blender game, and handles multilingual tests. Here's how it actually performs.
What is Claude Haiku 5.5 and why does it matter?
Claude Haiku 5.5 is Anthropic’s cheapest and fastest model in its current lineup, priced at 10 cents per million input tokens and 50 cents per million output tokens for prompts up to 100,000 tokens. That’s roughly a tenth of what Haiku 4.5 cost ($1 and $5 respectively for comparable tiers), and Anthropic says it costs about 75% less to run than its predecessor. Since prompts under 100k tokens make up roughly 90% of Haiku’s real-world traffic, most users will see that full discount in practice. The pitch is simple: a model good enough for everyday coding and agentic work, cheap enough to run constantly without worrying about the bill.
TL;DR
- Haiku 5.5 is dramatically cheaper than Haiku 4.5, dropping from $15 per million tokens (at higher tiers) down to 10 cents input / 50 cents output for sub-100k prompts, a roughly 75% cost reduction overall.
- It found and fixed a real, subtle production bug in a three-tier dairy co-op dashboard (Postgres, Flask, Nginx, Docker, Redis), correcting a milk-per-producer calculation that silently divided by the wrong denominator.
- Benchmark patterns show Haiku 5.5 beating GPT 5.2 Mini and its own predecessor on most tasks, with the biggest single jump on computer-use tasks, though Sonnet 5.5 still leads clearly on agentic coding.
- It built and ran a playable 3D Blender/Godot trampoline park game from a single prompt in about 25 minutes for roughly $2, including an unplanned ninja warrior course it added on its own.
- Multilingual output was functional but shallow, producing grammatically correct text across several languages without much depth or poetic nuance.
- Visual and scientific reasoning results were mixed: it correctly diagnosed a sloshing-liquid physics diagram and read an AI-generated comedic image well, but gave an overly cautious non-answer on an ambiguous sitting-versus-standing image.
- It’s positioned as a model for fast, everyday work rather than long coding marathons, with the gap to Sonnet 5.5 widest specifically on sustained agentic coding tasks.
How did Claude Haiku 5.5 perform on a real debugging task?
The test case was a “Yak Herd Manager,” a dashboard built for a fictional dairy co-op on the Tibetan plateau, running as a genuine three-tier application: Postgres backend, a Flask-based KPI service, an Nginx front end, Redis caching, all containerized in Docker. The dashboard displayed an “average milk per producer” figure of 2.09 liters. That number looked plausible on screen and the API returned a normal 200 response, but it was wrong. Only 8 of the 15 yaks in the herd actually produce milk; the app was dividing total milk output by all 15 animals, including males and calves. The correct figure was 3.91 liters, nearly double the displayed value.
This is the kind of bug that doesn’t throw errors or crash anything. It just quietly understates performance, which in a real cooperative could lead to bad feeding or culling decisions based on yaks that look underproductive but aren’t. Given a single prompt with no hints about where the bug lived, Haiku 5.5 located the exact calculation error, fixed it, and in the process also caught a connection leak and a divide-by-zero edge case, cross-checking its fix against the raw data. The fix held up after a hard refresh of the dashboard.
What do the benchmark numbers actually show?
Across the board, Haiku 5.5 scores above Haiku 4.5 on every measured category, and in most categories it also edges out GPT 5.2 Mini. The most dramatic improvement is in computer-use tasks, where it jumps from handling a small fraction of tasks successfully to around three-quarters. Sonnet 5.5 remains the stronger model across every category, but the size of that gap varies a lot by task type. On knowledge work and general reasoning, Haiku trails Sonnet by a relatively small margin. On agentic coding specifically, the gap nearly doubles, meaning Sonnet pulls far ahead on long, multi-step coding workflows.
The practical read: Haiku 5.5 is built for fast, everyday tasks (quick bug fixes, scripting, computer-use automation, general Q&A) rather than extended autonomous coding sessions that require sustained planning across many steps. That distinction matters when deciding which model to route a given job to.
Can Haiku 5.5 handle a long agentic build, like a 3D game?
To stress-test its agentic range beyond a single bug fix, Haiku 5.5 was given an open-ended goal: build a full 3D trampoline park game from scratch, modeling assets in Blender and assembling the playable result in Godot. This requires chaining many steps together (asset creation, scene assembly, game logic, testing) without a human correcting course along the way.
The model delivered a working, playable build in about 25 minutes for roughly $2 in API costs. The game included multiple trampoline-style courses and, notably, a ninja warrior obstacle course that wasn’t explicitly requested. Not everything worked perfectly: interaction with the main trampoline surface itself was inconsistent, and some physics behavior (flipping, jumping) worked better in some zones than others. But for a model explicitly positioned as the cheap, fast option rather than the top-tier coding model, producing a functional multi-scene 3D game autonomously is a meaningfully strong result, and it lines up with the benchmark pattern of Haiku being reasonably capable at agentic tasks even if it doesn’t match Sonnet on coding marathons.
How good is Haiku 5.5 at multilingual and visual reasoning tasks?
Asked to write short pieces of text across several languages with specific formatting instructions, Haiku 5.5 followed the structural instructions correctly but produced results that read as functional rather than polished. Wording was grammatically correct in each language tested, but the content lacked depth or poetic nuance, even in high-resource languages where larger models tend to do noticeably better. This suggests Haiku handles multilingual tasks competently at a surface level but isn’t the model to reach for when the output needs genuine literary quality.
Visual reasoning results were mixed. Given a diagram of a truck with liquid sloshing inside its tank and asked which truck was braking and why, Haiku correctly identified the braking truck and explained the physics based on the direction the liquid was sloshing, including reasoning about the internal baffles. On an AI-generated comedic image of a man “mowing” dirt, it correctly picked up on the visual joke and the absurdity of the scenario. But on an ambiguous image asking whether a man was sitting down or standing up, the model hedged and declined to commit to an answer even when context suggested a reasonable guess was possible, showing a conservative, sometimes overly rigid response style on genuinely ambiguous visual input.
Is Claude Haiku 5.5 worth using over Sonnet or GPT competitors?
For cost-sensitive, high-volume use, it’s a strong candidate. The price drop from Haiku 4.5 is substantial (roughly 75% cheaper), and the benchmark gap versus Sonnet 5.5 is smallest exactly where most everyday work happens: knowledge tasks, general reasoning, and computer use. If a workflow involves large volumes of debugging, scripting, or agent-driven computer tasks, Haiku 5.5 is cheap enough to run as a default rather than reserving the expensive model for simple jobs. It also makes a reasonable fallback when Sonnet or Opus usage hits rate limits, since switching to Haiku can extend usable throughput within Anthropic’s rate-limit windows.
Where it’s less convincing is on long, autonomous coding projects requiring many sequential decisions without supervision. That’s where Sonnet’s lead nearly doubles compared to other task categories. For teams building coding agents meant to run extended, unsupervised sessions, Haiku 5.5 is a cost-efficient option for the easy parts of the pipeline, not necessarily the whole job.
Frequently Asked Questions
How much does Claude Haiku 5.5 cost compared to Haiku 4.5?
Haiku 5.5 costs 10 cents per million input tokens and 50 cents per million output tokens for prompts up to 100,000 tokens. Haiku 4.5 cost around $1 and $5 per million tokens at comparable tiers, making Haiku 5.5 roughly a tenth of the price and about 75% cheaper overall according to Anthropic.
Can Claude Haiku 5.5 actually debug real production code?
Yes, in testing it identified and fixed a subtle calculation bug in a live three-tier web application (Postgres, Flask, Nginx, Docker) that was silently producing incorrect business metrics, without being given any hints about where the bug was located.
Everyone else built a construction worker.
We built the contractor.
One file at a time.
UI, API, database, deploy.
Is Claude Haiku 5.5 good at agentic coding tasks?
It performs reasonably well on agentic tasks like computer use and short automation jobs, where benchmark scores show a big jump over Haiku 4.5. However, on long multi-step agentic coding projects, Sonnet 5.5 maintains a much larger lead, suggesting Haiku is better suited to shorter, well-scoped coding tasks than extended autonomous builds.
How does Claude Haiku 5.5 compare to GPT models?
In the benchmarks referenced, Haiku 5.5 beat GPT 5.2 Mini on most measured categories, while still trailing Anthropic’s own Sonnet 5.5 across the board, most noticeably in agentic coding.
Is Claude Haiku 5.5 reliable for multilingual writing tasks?
It produces grammatically correct, instruction-following output across multiple languages, but the writing tends to be plain rather than nuanced or literary, even in widely spoken languages.