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Claude Opus 4.8 API
Claude Opus 4.8 is an Anthropic model for long-running agentic coding and complex enterprise work. It is designed for tasks where sustained judgment matters: investigating before acting, using the right tools, identifying weak assumptions, and carrying work through to a defensible result.
On iCreat, developers can call claude-opus-4-8 through our Anthropic-compatible Messages API. The model accepts text and image input, supports streaming and Adaptive Thinking, and returns typed content blocks that applications can handle as thinking or text.
Model Positioning
Claude Opus 4.8 is the direct successor to Claude Opus 4.7. The published context window and maximum output are unchanged; the upgrade focuses on long-horizon execution, tool triggering, context continuity, and more reliable reasoning behavior at each effort level.
Within Anthropic's current lineup, Opus 4.8 is the recommended starting point for complex agentic coding and enterprise work. Claude Fable 5 occupies the higher-capability tier, while Sonnet and Haiku models are better candidates when throughput or latency matters more than Opus-level judgment.
Key Capabilities
Judgment for Long-Running Work
Claude Opus 4.8 is more likely to question an unsound plan, flag uncertainty, and catch problems in its own work before presenting a result. This behavior is useful when an agent must coordinate changes across services, repositories, or professional evidence sets.
More Reliable Tool Triggering
The model reduces cases where a task requires a tool but the call is skipped. When connected to application-provided tools and permissions, this supports cleaner execution across search, coding, retrieval, browser, or business-system workflows.
Long-Context Continuity
A 1M-token context window and 128K maximum output support large repositories, extensive document collections, and substantial deliverables. Opus 4.8 also targets fewer context compactions and better recovery when an agent framework compacts a long task history.
Adaptive Reasoning
Adaptive Thinking lets the model decide whether a turn needs reasoning and how much to use. Effort levels low, medium, high, xhigh, and max give applications a consistent way to trade latency and token use against capability; the default effort is high.
Instructions That Can Evolve Mid-Task
Opus 4.8 accepts system messages at supported points inside a conversation. Long-running applications can update permissions, budgets, or environment instructions without restating the entire original system prompt.
Pricing
| Token Type | Price |
|---|---|
| Input | $1 per 1M tokens |
| Output | $5 per 1M tokens |
| Cache Read | $0.10 per 1M tokens |
| Cache Write | $1.25 per 1M tokens |
Use Cases
- Repository-scale engineering agents: investigate dependencies, implement coordinated changes, run application-provided tools, and verify outcomes across long task trajectories.
- Complex debugging and incident analysis: connect logs, code, architecture, and operational evidence before recommending or applying a fix.
- Enterprise research and document workflows: analyze long financial, legal, technical, or policy materials and produce reviewable deliverables.
- Browser and computer-use agents: reason over visual state and select actions inside a controlled environment supplied by the application.
- Long-running orchestration: maintain goals and updated instructions across multi-stage workflows that use retrieval, tools, and context management.
These workloads still require scoped permissions, executable checks, stopping rules, audit logs, and human approval for consequential actions.
Model Comparison
Claude Opus 4.8 vs Claude Opus 4.7
| Factor | Claude Opus 4.8 | Claude Opus 4.7 |
|---|---|---|
| Context Window | 1M tokens | 1M tokens |
| Maximum Output | 128K tokens | 128K tokens |
| Adaptive Thinking | Optional; low to max, including xhigh |
Optional; low to max, including xhigh |
| Long-Task Behavior | Better tool triggering, context handling, and recovery after compaction | Previous Opus behavior |
| Mid-Conversation System Messages | Supported | Not supported |
| Minimum Cacheable Prompt | 1,024 tokens | 2,048 tokens |
| Best Fit | New long-running agents that need stronger judgment and continuity | Existing evaluated workloads pinned to Opus 4.7 behavior |
Claude Opus 4.8 vs GPT-5.5 and Gemini 3.1 Pro
| Factor | Claude Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|---|
| Positioning | Complex agentic coding and enterprise work | Frontier coding and complex professional work | Complex problem-solving and multimodal agentic work |
| Context Window | 1M tokens | 1,050,000 tokens | 1,048,576 tokens |
| Maximum Output | 128K tokens | 128K tokens | 65,536 tokens |
| Official Input Modalities | Text and image | Text and image | Text, image, video, audio, and PDF |
| Reasoning Control | Adaptive Thinking; low to max, including xhigh |
Reasoning effort; none to xhigh |
Thinking supported |
| Best Fit | Long-running Claude agents that prioritize judgment and tool discipline | OpenAI-based coding and professional agent workflows | Broad multimodal intake and Google-centered agent workflows |
Why Choose Claude Opus 4.8?
Choose Claude Opus 4.8 when a task can run for many steps and the quality of intermediate decisions matters as much as the final answer. Its differentiator is the combination of stronger judgment, more dependable tool triggering, long-context continuity, and effort-controlled Adaptive Thinking.
This makes the model a strong candidate for engineering agents, professional research, and enterprise orchestration where missed tool calls, weak assumptions, or context drift can invalidate an otherwise fluent result. Teams already using Opus 4.7 can keep the same core scale while evaluating a focused behavioral upgrade.
Specifications
| Category | Description |
|---|---|
| Model Name | Claude Opus 4.8 |
| Provider | Anthropic |
| Model ID | claude-opus-4-8 |
| Release Date | May 28, 2026 |
| Model Type | Hybrid-reasoning LLM |
| Context Window | 1M tokens |
| Maximum Output | 128K tokens |
| Reliable Knowledge Cutoff | January 2026 |
| Training Data Cutoff | January 2026 |
| Official Input Modalities | Text and image |
| Output Modalities | Text |
| Official Reasoning Control | Optional Adaptive Thinking; low, medium, high, xhigh, max effort |
| Default Effort | high |
| Minimum Cacheable Prompt | 1,024 tokens |
| iCreat Input Modalities | Text and image |
| iCreat API Features | Anthropic-compatible Messages API, streaming, Adaptive Thinking, effort control, thinking/text content-block handling |
| Best Suited For | Long-running coding agents, enterprise knowledge work, tool-oriented professional workflows |
Architecture
Anthropic does not publish Claude Opus 4.8's parameter count or underlying network architecture. Its documented behavior is that of a hybrid-reasoning model: standard requests can answer directly, while Adaptive Thinking can allocate reasoning based on the task and selected effort level.
Adaptive Thinking returns signed thinking blocks for multi-turn continuity rather than raw chain-of-thought. Applications can request a readable reasoning summary, but that summary should not be treated as a complete internal reasoning trace.
Production Notes
Re-baseline effort before moving Opus 4.7 traffic to Opus 4.8. Anthropic recalibrated the token allocation behind medium, high, and xhigh, so the same effort label can produce different latency and token use even when the request shape is unchanged.
Leave enough max_tokens headroom for both reasoning and final text at high effort. Monitor truncation, tool-call completion, retry rates, and the quality of the final artifact rather than measuring only the first response.
Preserve returned content blocks unchanged when continuing tool-use conversations. Evaluate complete task trajectories with production tools, permissions, compaction settings, and acceptance checks before switching live traffic.
FAQ
Does effort: "high" automatically enable Adaptive Thinking?
No. high is the default effort level, but thinking remains off until the request explicitly sets thinking: {"type": "adaptive"}. Effort guides how much reasoning the model may use after Adaptive Thinking is enabled.
Why does a request with budget_tokens return a 400 error?
Claude Opus 4.8 does not support manual Extended Thinking. Replace thinking: {"type": "enabled", "budget_tokens": N} with Adaptive Thinking and control reasoning through an effort level.
Why is the thinking field empty after Adaptive Thinking is enabled?
Claude Opus 4.8 defaults thinking.display to omitted. Set display: "summarized" when the application needs a readable reasoning summary; the signed block should still be preserved unchanged for multi-turn continuity.
Can system instructions change during a long conversation without rebuilding the prompt?
Yes. Opus 4.8 supports mid-conversation system messages at documented placement points, including immediately after a user turn. This can update operating instructions while preserving cache hits on earlier conversation content.