## Create a Text Completion `Completion completions().create(CompletionCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())` **post** `/v1/complete` [Legacy] Create a Text Completion. The Text Completions API is a legacy API. We recommend using the [Messages API](https://docs.claude.com/en/api/messages) going forward. Future models and features will not be compatible with Text Completions. See our [migration guide](https://docs.claude.com/en/api/migrating-from-text-completions-to-messages) for guidance in migrating from Text Completions to Messages. ### Parameters - `CompletionCreateParams params` - `Optional> betas` Optional header to specify the beta version(s) you want to use. - `MESSAGE_BATCHES_2024_09_24("message-batches-2024-09-24")` - `PROMPT_CACHING_2024_07_31("prompt-caching-2024-07-31")` - `COMPUTER_USE_2024_10_22("computer-use-2024-10-22")` - `COMPUTER_USE_2025_01_24("computer-use-2025-01-24")` - `PDFS_2024_09_25("pdfs-2024-09-25")` - `TOKEN_COUNTING_2024_11_01("token-counting-2024-11-01")` - `TOKEN_EFFICIENT_TOOLS_2025_02_19("token-efficient-tools-2025-02-19")` - `OUTPUT_128K_2025_02_19("output-128k-2025-02-19")` - `FILES_API_2025_04_14("files-api-2025-04-14")` - `MCP_CLIENT_2025_04_04("mcp-client-2025-04-04")` - `MCP_CLIENT_2025_11_20("mcp-client-2025-11-20")` - `DEV_FULL_THINKING_2025_05_14("dev-full-thinking-2025-05-14")` - `INTERLEAVED_THINKING_2025_05_14("interleaved-thinking-2025-05-14")` - `CODE_EXECUTION_2025_05_22("code-execution-2025-05-22")` - `EXTENDED_CACHE_TTL_2025_04_11("extended-cache-ttl-2025-04-11")` - `CONTEXT_1M_2025_08_07("context-1m-2025-08-07")` - `CONTEXT_MANAGEMENT_2025_06_27("context-management-2025-06-27")` - `MODEL_CONTEXT_WINDOW_EXCEEDED_2025_08_26("model-context-window-exceeded-2025-08-26")` - `SKILLS_2025_10_02("skills-2025-10-02")` - `FAST_MODE_2026_02_01("fast-mode-2026-02-01")` - `OUTPUT_300K_2026_03_24("output-300k-2026-03-24")` - `USER_PROFILES_2026_03_24("user-profiles-2026-03-24")` - `ADVISOR_TOOL_2026_03_01("advisor-tool-2026-03-01")` - `MANAGED_AGENTS_2026_04_01("managed-agents-2026-04-01")` - `CACHE_DIAGNOSIS_2026_04_07("cache-diagnosis-2026-04-07")` - `THINKING_TOKEN_COUNT_2026_05_13("thinking-token-count-2026-05-13")` - `long maxTokensToSample` The maximum number of tokens to generate before stopping. Note that our models may stop _before_ reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate. - `Model model` The model that will complete your prompt. See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options. - `String prompt` The prompt that you want Claude to complete. For proper response generation you will need to format your prompt using alternating ` Human:`and` Assistant:` conversational turns. For example: ``` " Human: {userQuestion} Assistant:" ``` See [prompt validation](https://docs.claude.com/en/api/prompt-validation) and our guide to [prompt design](https://docs.claude.com/en/docs/intro-to-prompting) for more details. - `Optional metadata` An object describing metadata about the request. - `Optional> stopSequences` Sequences that will cause the model to stop generating. Our models stop on `" Human:"`, and may include additional built-in stop sequences in the future. By providing the stop_sequences parameter, you may include additional strings that will cause the model to stop generating. - `Optional temperature` Amount of randomness injected into the response. Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks. Note that even with `temperature` of `0.0`, the results will not be fully deterministic. - `Optional topK` Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. [Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277). Recommended for advanced use cases only. - `Optional topP` Use nucleus sampling. In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. Recommended for advanced use cases only. ### Returns - `class Completion:` - `String id` Unique object identifier. The format and length of IDs may change over time. - `String completion` The resulting completion up to and excluding the stop sequences. - `Model model` The model that will complete your prompt. See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options. - `CLAUDE_OPUS_4_8("claude-opus-4-8")` Frontier intelligence for long-running agents and coding - `CLAUDE_OPUS_4_7("claude-opus-4-7")` Frontier intelligence for long-running agents and coding - `CLAUDE_MYTHOS_PREVIEW("claude-mythos-preview")` New class of intelligence, strongest in coding and cybersecurity - `CLAUDE_OPUS_4_6("claude-opus-4-6")` Frontier intelligence for long-running agents and coding - `CLAUDE_SONNET_4_6("claude-sonnet-4-6")` Best combination of speed and intelligence - `CLAUDE_HAIKU_4_5("claude-haiku-4-5")` Fastest model with near-frontier intelligence - `CLAUDE_HAIKU_4_5_20251001("claude-haiku-4-5-20251001")` Fastest model with near-frontier intelligence - `CLAUDE_OPUS_4_5("claude-opus-4-5")` Premium model combining maximum intelligence with practical performance - `CLAUDE_OPUS_4_5_20251101("claude-opus-4-5-20251101")` Premium model combining maximum intelligence with practical performance - `CLAUDE_SONNET_4_5("claude-sonnet-4-5")` High-performance model for agents and coding - `CLAUDE_SONNET_4_5_20250929("claude-sonnet-4-5-20250929")` High-performance model for agents and coding - `CLAUDE_OPUS_4_1("claude-opus-4-1")` Exceptional model for specialized complex tasks - `CLAUDE_OPUS_4_1_20250805("claude-opus-4-1-20250805")` Exceptional model for specialized complex tasks - `CLAUDE_OPUS_4_0("claude-opus-4-0")` Powerful model for complex tasks - `CLAUDE_OPUS_4_20250514("claude-opus-4-20250514")` Powerful model for complex tasks - `CLAUDE_SONNET_4_0("claude-sonnet-4-0")` High-performance model with extended thinking - `CLAUDE_SONNET_4_20250514("claude-sonnet-4-20250514")` High-performance model with extended thinking - `CLAUDE_3_HAIKU_20240307("claude-3-haiku-20240307")` Fast and cost-effective model - `Optional stopReason` The reason that we stopped. This may be one the following values: * `"stop_sequence"`: we reached a stop sequence — either provided by you via the `stop_sequences` parameter, or a stop sequence built into the model * `"max_tokens"`: we exceeded `max_tokens_to_sample` or the model's maximum - `JsonValue; type "completion"constant` Object type. For Text Completions, this is always `"completion"`. - `COMPLETION("completion")` ### Example ```java package com.anthropic.example; import com.anthropic.client.AnthropicClient; import com.anthropic.client.okhttp.AnthropicOkHttpClient; import com.anthropic.models.completions.Completion; import com.anthropic.models.completions.CompletionCreateParams; import com.anthropic.models.messages.Model; public final class Main { private Main() {} public static void main(String[] args) { AnthropicClient client = AnthropicOkHttpClient.fromEnv(); CompletionCreateParams params = CompletionCreateParams.builder() .maxTokensToSample(256L) .model(Model.CLAUDE_OPUS_4_8) .prompt("\n\nHuman: Hello, world!\n\nAssistant:") .build(); Completion completion = client.completions().create(params); } } ``` #### Response ```json { "id": "compl_018CKm6gsux7P8yMcwZbeCPw", "completion": " Hello! My name is Claude.", "model": "claude-2.1", "stop_reason": "stop_sequence", "type": "completion" } ```