Claude Haiku 5.5 — Ranked #26 in Coding on HuaLinYuan Index
Not yet on this site
Overview
Claude Haiku 5.5 from Anthropic sits at #26 on the HuaLinYuan Coding leaderboard with an Index score of 1585, placing it among capable mid-tier coding assistants. It balances speed and accuracy for everyday programming tasks, from writing functions to debugging snippets.
Priced at $0.10 per million input tokens and $0.50 per million output tokens, it targets developers, students, and small teams who want dependable code generation without premium costs. Its positioning makes it a practical choice for routine development workflows and learning scenarios.
Strengths
- Solid coding rank at #26
- Low per-token pricing
- Fast response for routine tasks
- Reliable for common languages
Best for
- Write boilerplate code quickly
- Debug simple logic errors
- Learn programming concepts
- Generate test cases fast
Things to know
- Not yet available on HuaLinYuan
- Mid-tier rank, not top-tier
Claude Haiku 5.5 vs other models
FAQ
What does Claude Haiku 5.5 cost — is it free?
It is not free. Official pricing is $0.10 per 1M input tokens and $0.50 per 1M output tokens. You pay only for what you use via Anthropic's API or partnered platforms.
What is this model best at?
It excels at everyday coding tasks: generating functions, explaining snippets, writing tests, and helping with syntax in popular languages. Its #26 Coding rank reflects solid, practical performance for routine development.
How do I choose between this and comparable models?
Compare based on price, speed, and your specific language needs. Kimi K3, GLM-5.3, and DeepSeek V4.1 Flash are available on HuaLinYuan now — try them side by side for your workflow.
Can I use Claude Haiku 5.5 on HuaLinYuan or LongHua AI today?
Not yet — it is not available on this site. For immediate access, use the comparable models listed above: Kimi K3, GLM-5.3, or DeepSeek V4.1 Flash, all usable right now on HuaLinYuan.
Similar models you can use here
Tell LongHua Tree ASI what you want — it splits the task and assigns the strongest model to each part, then delivers the finished result.