Claude Haiku 5.5 is Anthropic's small, fast model for high-volume, cost-sensitive work such as summarization, subagents, and browser use. It succeeds Claude Haiku 4.5 with stronger coding, computer use, and knowledge work, and accepts text and image input with a 1M-token context window.
It is the first Haiku model with adjustable effort. Thinking is adaptive and on by default, with effort as the main lever for trading off depth, latency, and cost; it can be turned off at low, medium, and high effort.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).
| $0.05 | $0.25 | $0.005 | -- | -- |
Input $0.05 / Output $0.25-- tok/s-- uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
Claude Haiku 5.5 (batch) — Price history
| $0.00 | $0.00 | 73.8% | 100.0% |
- Effective in $0.00 / Effective out $0.00Token share100.0%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Not enough uptime data to display yet.
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) Intelligence Index | 34.6 |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) HLE | 42.7% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) AA-LCR | 78.3% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) GDPval-AA | 51.2% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) CritPt | 22.6% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) SciCode | 51.7% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) Terminal-Bench 4.0 | 29.3% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) AA-Omniscience Accuracy | 33.8% |
| Artificial Analysis | Claude Haiku 5.5 (Xhigh) AA-Omniscience Non-Hallucination Rate | 60.8% |
| Artificial Analysis | Claude Haiku 5.5 (Low) Intelligence Index | 18.6 |
| Artificial Analysis | Claude Haiku 5.5 (Low) HLE | 27.0% |
| Artificial Analysis | Claude Haiku 5.5 (Low) AA-LCR | 72.0% |
| Artificial Analysis | Claude Haiku 5.5 (Low) GDPval-AA | 31.5% |
| Artificial Analysis | Claude Haiku 5.5 (Low) CritPt | 9.1% |
| Artificial Analysis | Claude Haiku 5.5 (Low) SciCode | 49.2% |
| Artificial Analysis | Claude Haiku 5.5 (Low) Terminal-Bench 4.0 | 12.6% |
| Artificial Analysis | Claude Haiku 5.5 (Low) AA-Omniscience Accuracy | 30.4% |
| Artificial Analysis | Claude Haiku 5.5 (Low) AA-Omniscience Non-Hallucination Rate | 59.5% |
| Artificial Analysis | Claude Haiku 5.5 (High) Intelligence Index | 29.9 |
| Artificial Analysis | Claude Haiku 5.5 (High) HLE | 37.3% |
| Artificial Analysis | Claude Haiku 5.5 (High) AA-LCR | 77.3% |
| Artificial Analysis | Claude Haiku 5.5 (High) GDPval-AA | 46.2% |
| Artificial Analysis | Claude Haiku 5.5 (High) CritPt | 18.6% |
| Artificial Analysis | Claude Haiku 5.5 (High) SciCode | 48.7% |
| Artificial Analysis | Claude Haiku 5.5 (High) Terminal-Bench 4.0 | 21.7% |
| Artificial Analysis | Claude Haiku 5.5 (High) AA-Omniscience Accuracy | 31.8% |
| Artificial Analysis | Claude Haiku 5.5 (High) AA-Omniscience Non-Hallucination Rate | 61.1% |
| Artificial Analysis | Claude Haiku 5.5 (Max) Intelligence Index | 36.5 |
| Artificial Analysis | Claude Haiku 5.5 (Max) HLE | 44.4% |
| Artificial Analysis | Claude Haiku 5.5 (Max) AA-LCR | 82.7% |
| Artificial Analysis | Claude Haiku 5.5 (Max) GDPval-AA | 56.4% |
| Artificial Analysis | Claude Haiku 5.5 (Max) CritPt | 18.9% |
| Artificial Analysis | Claude Haiku 5.5 (Max) SciCode | 55.0% |
| Artificial Analysis | Claude Haiku 5.5 (Max) Terminal-Bench 4.0 | 32.8% |
| Artificial Analysis | Claude Haiku 5.5 (Max) AA-Omniscience Accuracy | 34.7% |
| Artificial Analysis | Claude Haiku 5.5 (Max) AA-Omniscience Non-Hallucination Rate | 65.8% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) Intelligence Index | 24.6 |
| Artificial Analysis | Claude Haiku 5.5 (Medium) HLE | 33.8% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) AA-LCR | 77.3% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) GDPval-AA | 39.1% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) CritPt | 12.9% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) SciCode | 49.0% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) Terminal-Bench 4.0 | 15.2% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) AA-Omniscience Accuracy | 31.7% |
| Artificial Analysis | Claude Haiku 5.5 (Medium) AA-Omniscience Non-Hallucination Rate | 59.6% |
| Design Arena | Claude Haiku 5.5 Models Arena 3D Elo | 1321 |
| Design Arena | Claude Haiku 5.5 Models Arena Asciiart Elo | 1210 |
| Design Arena | Claude Haiku 5.5 Models Arena Code Categories Elo | 1278 |
| Design Arena | Claude Haiku 5.5 Models Arena Data Visualization Elo | 1278 |
| Design Arena | Claude Haiku 5.5 Models Arena Game Development Elo | 1316 |
| Design Arena | Claude Haiku 5.5 Models Arena SVG Elo | 1195 |
| Design Arena | Claude Haiku 5.5 Models Arena UI Component Elo | 1271 |
| Design Arena | Claude Haiku 5.5 Models Arena Website Elo | 1253 |
| Vals AI | Claude Haiku 5.5 BioMysteryBench | 70.4% |
| Vals AI | Claude Haiku 5.5 ClaimBench | 100.0% |
| Vals AI | Claude Haiku 5.5 Code Migration v1.1 | 52.4% |
| Vals AI | Claude Haiku 5.5 CyberBench v1.1 | 75.5% |
| Vals AI | Claude Haiku 5.5 EMB v1.1 | 65.5% |
| Vals AI | Claude Haiku 5.5 Finance Agent v2.1 | 65.4% |
| Vals AI | Claude Haiku 5.5 Vals Finance Index | 57.4% |
| Vals AI | Claude Haiku 5.5 HLAB-Vals-1.1 | 4.2% |
| Vals AI | Claude Haiku 5.5 Immigration | 61.8% |
| Vals AI | Claude Haiku 5.5 IOI | 47.3% |
| Vals AI | Claude Haiku 5.5 Vals Legal Index | 70.3% |
| Vals AI | Claude Haiku 5.5 Legal Research Bench | 43.8% |
| Vals AI | Claude Haiku 5.5 MedCode | 51.5% |
| Vals AI | Claude Haiku 5.5 MedScribe | 89.5% |
| Vals AI | Claude Haiku 5.5 MysteryMechanism | 35.1% |
| Vals AI | Claude Haiku 5.5 ProgramBench | 2.5% |
| Vals AI | Claude Haiku 5.5 ProofBench v1.1 | 89.0% |
| Vals AI | Claude Haiku 5.5 Public Benefits Bench v1.1 | 63.2% |
| Vals AI | Claude Haiku 5.5 SAGE | 89.8% |
| Vals AI | Claude Haiku 5.5 Vals Science Index | 38.5% |
| Vals AI | Claude Haiku 5.5 SRE Bench | 5.7% |
| Vals AI | Claude Haiku 5.5 SWE-bench Multimodal | 44.0% |
| Vals AI | Claude Haiku 5.5 Tax Agent Bench | 31.9% |
| Vals AI | Claude Haiku 5.5 Terminal-Bench 4.0 | 36.4% |
| Vals AI | Claude Haiku 5.5 Terminal-Bench-Science | 10.0% |
| Vals AI | Claude Haiku 5.5 Vals Index | 58.7% |
| Vals AI | Claude Haiku 5.5 Vibe Code Bench 1-100 | 20.4% |
| Vals AI | Claude Haiku 5.5 Vibe Code Bench v1.1 | 90.4% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Not enough data for apps using this model yet.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Anthropic: Claude Haiku versions
Not currently available through EU in-region routing. In-region routing docs
Documentation
Guides, cookbooks, blog posts, and tools for building with Anthropic: Claude Haiku 5.5 (batch) on OpenRouter.
- Claude 5.5 Migration GuideCookbook
- Reranking in RAG: How to Fix Bad Retrieval with a RerankerBlog post
- How to Benchmark LLMsBlog post
- How to Stop an AI Agent Stuck in an Infinite LoopBlog post
- MCP vs CLI vs Skills: What Each Costs in ContextBlog post
- LLM API Rate Limits: How to Fix OpenAI and Claude 429 ErrorsBlog post
- How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision ModelBlog post
- Best OCR Model for Scanned Documents, MeasuredBlog post
- Build an AI Code Reviewer with OpenRouterBlog post
- How to Build Your Own Always-On AI AgentBlog post
- Claude Opus vs Sonnet vs Haiku: Which Should You Use?Blog post
- How Much Does Claude Code Cost? Real Session Costs by ModelBlog post
- How to Extract Tables from a PDF with an LLMBlog post
- Hit Your Claude Code Usage Limit? How to Keep CodingBlog post
- Claude 529 Overloaded: What It Means and How to Fix ItBlog post
Frequently asked questions
Claude Haiku 5.5 is Anthropic's small, fast model for high-volume, cost-sensitive work such as summarization, subagents, and browser use. It succeeds Claude Haiku 4.5 with stronger coding, computer use, and knowledge work, and accepts text and image input with a 1M-token context window. It is the first Haiku model with adjustable effort.
The Claude Haiku 5.5 (batch) documentation on OpenRouter includes the Claude 5.5 Migration Guide cookbook, the Reranking in RAG: How to Fix Bad Retrieval with a Reranker blog post, the How to Benchmark LLMs blog post, the How to Stop an AI Agent Stuck in an Infinite Loop blog post, the MCP vs CLI vs Skills: What Each Costs in Context blog post, the LLM API Rate Limits: How to Fix OpenAI and Claude 429 Errors blog post, the How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision Model blog post, the Best OCR Model for Scanned Documents, Measured blog post, the Build an AI Code Reviewer with OpenRouter blog post, the How to Build Your Own Always-On AI Agent blog post, the Claude Opus vs Sonnet vs Haiku: Which Should You Use? blog post, the How Much Does Claude Code Cost? Real Session Costs by Model blog post, the How to Extract Tables from a PDF with an LLM blog post, the Hit Your Claude Code Usage Limit? How to Keep Coding blog post, and the Claude 529 Overloaded: What It Means and How to Fix It blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.
Claude Haiku 5.5 (batch) costs $0.05/M input tokens and $0.25/M output tokens, with separate rates for Cache Read at $0.005/M tokens, Cache Write at $0.0625/M tokens, Cache Write (1h) at $0.10/M tokens, and Web Search at $10.00/1K calls.
Claude Haiku 5.5 (batch) has a 1,000,000 token context window. It supports up to 128,000 completion tokens.
Yes. Claude Haiku 5.5 (batch) accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
Claude Haiku 5.5 (batch) accepts text, images, and files such as PDFs as input and returns text.
Claude Haiku 5.5 (batch) is served by 1 provider on OpenRouter: Anthropic.
Claude Fable 5.2, Claude Sonnet 5.5, Claude Opus 5.5 and 12 more are other text models from Anthropic.
Claude Haiku 5.5 (batch) was released on October 7, 2026.