Chat With Claude Opus 5.5 Online : Frontier Coding at a Lower Cost
Try Claude Opus 5.5 on Lorka AI to plan, code, use tools, verify results, and complete demanding engineering or research workflows efficiently.
Anthropic’s latest Opus model introducing next-generation agentic coding and knowledge work capabilities, adaptive thinking, long-context reasoning, and more efficient complex task execution.
Access Claude Opus 5.5 on Lorka and Move From Difficult Prompts to Implementation-Ready Work
The Anthropic model combines high-end coding and knowledge-work performance with lower token prices and more efficient execution.
Agentic coding that completes the workflow
Repository-scale execution
Professional analysis with stronger evidence discipline
Lower typical workload cost
Clearer long-session collaboration
Claude Opus 5.5 Benchmark Scores
Official results highlight the Claude Opus 5.5 model’s strengths in agentic coding, knowledge work, computer use, scientific research, and visual analysis.
| Benchmark | Anthropic-reported result |
|---|---|
| Terminal-Bench 4.0 | 66.4% at xhigh effort |
| FrontierCode v1.1 Main | 54.4% |
| CursorBench 4.0 | 57.8% |
| GDPval-AA v2.1 | 1,846 Elo |
| AutomationBench | 40.0% |
| Humanity’s Last Exam | 67.7% with tools |
| Terminal-Bench-Science 0.1 | 58.7% |
| OSWorld 2.0 | 81.8% partial score |
| Chartography | 89.0% with tools |
Anthropic reports most Claude Opus 5.5 results with adaptive thinking at max effort. Terminal-Bench 4.0 was evaluated at xhigh effort. Results should be considered alongside safeguards, model configuration, effort level, tool access, task design, and testing on real workflows.
Opus 5.5 vs. Opus 5: What’s New
Claude Opus 5.5 improves on Opus 5 with stronger agentic coding, better efficiency, and lower operating costs for complex workflows.
Higher coding performance
Terminal-Bench 4.0 increased from 52.3% to 66.4%, with gains across repository-level coding and agent workflows.
Lower cost
Input and output pricing dropped from $5/$25 to $4/$20 per million tokens, with cheaper cache reads.
Faster execution
Generates outputs more than 30% faster and typically uses fewer tokens and tool calls.
Stronger knowledge work
Reaches 1,846 Elo on GDPval-AA v2.1 for professional tasks across multiple industries.
Improved agent workflows
Opus 5.5 uses fewer turns and tokens on complex tasks while supporting large codebases, multi-step agents, and sustained context.
Claude Opus 5.5 Coding Workflow Example
See how Anthropic Opus 5.5 can take your coding further at a lower price.
Audit this monorepo for slow API responses. Identify the highest-impact bottleneck, implement the safest fix, add regression coverage, and explain how to validate the change before rollout.
Output: Root cause The product-search endpoint requests inventory data one SKU at a time inside the response loop. A page containing 50 products creates 50 sequential inventory lookups, increasing P95 latency and making the endpoint sensitive to downstream inventory delays.
1def get_inventory_by_sku(skus):2 rows = inventory_client.get_many(skus)3 return {row.sku: row.available for row in rows}456def build_product_response(products):7 inventory = get_inventory_by_sku(8 [product.sku for product in products]9 )1011 return [12 {13 "id": product.id,14 "name": product.name,15 "available": inventory.get(product.sku, False),16 }17 for product in products18 ]1920# Regression test21def test_product_search_batches_inventory_requests(mocker):22 get_many = mocker.patch(23 "inventory_client.get_many",24 return_value=[]25 )2627 build_product_response(products_with_50_items())2829 get_many.assert_called_once()Next step: Run the endpoint against production-like traffic, compare P50 and P95 latency, error rate, and inventory-service request volume, then deploy behind a feature flag.
Try Claude Opus 5.5 With Leading Models in One AI Workspace
Lorka AI keeps model comparison and project context together in a single platform so teams can match capability, speed, and cost to each workload.
Compare leading models
Match capability to difficulty
Keep project context centralized
Test before standardizing
Control model spend
Claude Opus 5.5 Context Window, Capabilities, Pricing, and Availability
MODEL TYPE
- Released September 22, 2026
- First model in Anthropic’s Claude 5.5 family
- Built for agentic coding, computer use, research, and professional knowledge work
- Positioned near Claude Fable 5.1 performance on most work at a lower typical cost than Opus 5
CONTEXT AND OUTPUT
- Context window: 1,000,000 tokens by default
- Maximum standard output: 128,000 tokens
- Maximum Batch API beta output: 300,000 tokens
- Supports up to 600 images or PDF pages in a 1M-context request, subject to request-size limits
- Designed for repositories, multi-file projects, research collections, technical logs, and extended project histories
MODALITIES
- Inputs: Text and images
- Output: Text
- Supports PDF and document analysis through compatible platform features
- Suitable for coding, tool use, computer use, research, and structured professional workflows
REASONING AND DEVELOPMENT
- Adaptive thinking is always on and cannot be disabled
- Supported effort levels: low, medium, high, xhigh, and max
- Default effort level: medium
- Supports tool use, prompt caching, batch processing, Files API workflows, and model-specific computer-use tools
- Maximum output includes thinking tokens and response text, so higher effort may require a larger max_tokens setting
KNOWLEDGE AND DEPLOYMENT
- Reliable knowledge cutoff: June 2026
- Training-data cutoff: June 2026
- Model ID: claude-opus-5-5
- Available through the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS
- Anthropic lists retirement as no sooner than September 22, 2027
PRICING
- Input: $4 per 1 million tokens
- Output: $20 per 1 million tokens
- Five-minute cache write: $5 per 1 million tokens
- One-hour cache write: $8 per 1 million tokens
- Cache read: $0.20 per 1 million tokens
- Batch API: 50% discount on input and output tokens
- Fast mode: $8 per 1 million input tokens and $40 per 1 million output tokens, with up to 2.5 times the standard speed
- Anthropic estimates approximately 40% lower typical workload cost than Opus 5
Claude Opus 5.5 vs. Fable 5.1, Opus 5, GPT-6 Astra, and Gemini 3.8 Flash
Use this comparison table to see where Opus 5.5 has the advantage compared with other frontier AI models like Gemini 3.8 Flash, Muse Spark 1.3, and other models available in Lorka’s AI chat.
| Models | Reasoning | Speed | Multimodality | Context | Ideal use cases |
|---|---|---|---|---|---|
Claude Opus 5.5 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Frontier coding, large codebase changes, difficult debugging, extended research, financial analysis, business processes, and demanding professional work at lower operating cost. |
Claude Fable 5.1 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Top-tier software engineering, persistent agents, difficult research, precision-focused analysis, system architecture, and the most demanding publicly available model workloads. |
Gemini 3.8 Flash | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Fast long-horizon coding, multimodal investigation, autonomous agents, document analysis, reasoning-heavy tasks, and high-volume professional workflows. |
GPT-6 Astra | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Complex programming, computer-use workflows, million-token project reviews, independent agents, technical investigations, and consequential professional analysis. |
Qwen 3.8 Max | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Large multimodal development projects, agent-led workflows, visual production, technical research, and long-context product or engineering work. |
Grok 4.6 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Persistent agents, technical investigations, interactive product development, repository assessment, and multistage research workflows. |
Kimi K3 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Large software projects, multimodal product development, interface creation, system assessment, and structured agents working across substantial project contexts. |
Muse Spark 1.3 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Repository-wide engineering, advanced debugging, persistent technical agents, tool-assisted development, and intricate instruction-following tasks. |
Claude Sonnet 5 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Efficient business analysis, application development, automated processes, project planning, and routine technical work |
GLM-5.3 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | End-to-end software delivery, process automation, major codebase refactoring, technical agents, and budget-conscious development workflows. |
Claude Opus 5.5
Frontier coding, large codebase changes, difficult debugging, extended research, financial analysis, business processes, and demanding professional work at lower operating cost.
Claude Fable 5.1
Top-tier software engineering, persistent agents, difficult research, precision-focused analysis, system architecture, and the most demanding publicly available model workloads.
Gemini 3.8 Flash
Fast long-horizon coding, multimodal investigation, autonomous agents, document analysis, reasoning-heavy tasks, and high-volume professional workflows.
GPT-6 Astra
Complex programming, computer-use workflows, million-token project reviews, independent agents, technical investigations, and consequential professional analysis.
Qwen 3.8 Max
Large multimodal development projects, agent-led workflows, visual production, technical research, and long-context product or engineering work.
Grok 4.6
Persistent agents, technical investigations, interactive product development, repository assessment, and multistage research workflows.
Kimi K3
Large software projects, multimodal product development, interface creation, system assessment, and structured agents working across substantial project contexts.
Muse Spark 1.3
Repository-wide engineering, advanced debugging, persistent technical agents, tool-assisted development, and intricate instruction-following tasks.
Claude Sonnet 5
Efficient business analysis, application development, automated processes, project planning, and routine technical work
GLM-5.3
End-to-end software delivery, process automation, major codebase refactoring, technical agents, and budget-conscious development workflows.
How to Try Claude Opus 5.5 on Lorka AI
Chat with Opus 5.5 on Lorka AI and use it with GPT-6, Fable 5.1, and other top models in the same chat.
1. Select Opus 5.5
2. Write a prompt
3. Start your workflow
Chat With Claude Opus 5.5 Today
Access Claude Opus 5.5 on Lorka now. Create an account and start a multi-LLM workflow with Opus, GPT, and more.
Claude Opus 5.5 Model FAQs
You can access Claude Opus 5.5 through Anthropic’s Claude apps and API, Amazon Web Services, Google Cloud, or Microsoft Azure. You can also select it from Lorka AI's chat along with DeepSeek models, OpenAI models, and more.