Anthropic • LLM
Fast-mode variant of [Opus 4.6](/anthropic/claude-opus-4.6) - identical capabilities with higher output speed at premium 6x pricing. Learn more in Anthropic's docs: https://platform.claude.com/docs/en/build-with-claude/fast-mode
Context Window
1.0M tokens
Input Price/1M
$30.00
Output Price/1M
$150.00
Parameters
—
Max Output
128K tokens
Claude Opus 4.6 (Fast) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Coding Index | 48.1 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LMArena Elo | 1497.0 | 2000.0 | Crowdsourced blind pairwise comparisons |
| AA Intelligence Index | 52.9 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 89.6 | 100.0 | Artificial Analysis official API |
Claude Opus 4.6 (Fast) is an AI model developed by Anthropic, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As a proprietary model, it is available via Anthropic's cloud API. With a context window of 1.0M tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Claude Opus 4.6 (Fast) is usage-based, priced at $30/1M input tokens and $150/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The premium pricing reflects the model's frontier capabilities, recommended for complex reasoning, analysis, and generation tasks where quality takes priority over cost.
Claude Opus 4.6 (Fast) was evaluated on 4 different benchmarks, covering categories like Coding, overall, Reasoning. Results show exceptional performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Claude Opus 4.6 (Fast) is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), image and visual document analysis (OCR, diagrams, screenshots), multimodal processing combining text and images, complex reasoning, math problem solving, and logical analysis, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, Claude Opus 4.6 (Fast) competes directly with similarly capable models. Key competitors include GPT (OpenAI), Gemini (Google), and open source models like Llama (Meta) and DeepSeek. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Fast-mode variant of [Opus 4.6](/anthropic/claude-opus-4.6) - identical capabilities with higher output speed at premium 6x pricing. Learn more in Anthropic's docs: https://platform.claude.com/docs/en/build-with-claude/fast-mode
Claude Opus 4.6 (Fast) costs $30/1M input tokens and $150/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Claude Opus 4.6 (Fast) scored: AA Coding Index: 48.1/100, LMArena Elo: 1497/2000, AA Intelligence Index: 52.9/100. See the full table above for a detailed comparison.
No, Claude Opus 4.6 (Fast) is a proprietary model from Anthropic. It is available via cloud API. For open source alternatives, check our open source model ranking.
Claude Opus 4.6 (Fast) excels at complex reasoning and problem solving. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →