Anthropic: Claude Sonnet 5 (batch)
Anthropic • LLM
Anthropic: Claude Sonnet 5 (batch) by Anthropic
Specifications
Context Window
1.0M tokens
Input Price/1M
$1.00
Output Price/1M
$5.00
Parameters
—
Max Output
128K tokens
Benchmarks
Anthropic: Claude Sonnet 5 (batch) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Coding Index | 66.4 | 100.0 | Artificial Analysis official API |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 42.6 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 80.0 | 100.0 | Artificial Analysis official API |
Information
- Release date
- June 30, 2026
- Tool Calling
- ❌ Not supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: Anthropic: Claude Sonnet 5 (batch)
What is Anthropic: Claude Sonnet 5 (batch)?
Anthropic: Claude Sonnet 5 (batch) 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.
Pricing & Costs in 2026
Anthropic: Claude Sonnet 5 (batch) is usage-based, priced at $1/1M input tokens and $5/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The mid-range pricing balances quality and cost for most professional applications.
Benchmarks & Performance
Anthropic: Claude Sonnet 5 (batch) was evaluated on 3 different benchmarks, covering categories like Coding, overall, Reasoning. Results show solid 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.
Recommended Use Cases
Anthropic: Claude Sonnet 5 (batch) is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), multimodal processing combining text and images, text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, Anthropic: Claude Sonnet 5 (batch) 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.
Frequently Asked Questions
What is Anthropic: Claude Sonnet 5 (batch)?
Anthropic: Claude Sonnet 5 (batch) is an AI model developed by Anthropic. It is a language model (LLM), with multimodal support (text, image and more).
How much does Anthropic: Claude Sonnet 5 (batch) cost?
Anthropic: Claude Sonnet 5 (batch) costs $1/1M input tokens and $5/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does Anthropic: Claude Sonnet 5 (batch) compare with other models?
In available benchmarks, Anthropic: Claude Sonnet 5 (batch) scored: AA Coding Index: 66.4/100, AA Intelligence Index: 42.6/100, GPQA Diamond: 80/100. See the full table above for a detailed comparison.
Is Anthropic: Claude Sonnet 5 (batch) open source?
No, Anthropic: Claude Sonnet 5 (batch) is a proprietary model from Anthropic. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is Anthropic: Claude Sonnet 5 (batch) best for?
Anthropic: Claude Sonnet 5 (batch) excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations.
Last updated: August 10, 2026 • View methodology →