What modalities does Claude Sonnet 4.5 (2025-09-29) support?

Claude Sonnet 4.5 (2025-09-29) accepts text and produces text according to its architecture metadata on LLM.API.

How is Claude Sonnet 4.5 (2025-09-29) priced on LLM.API?

Listed pricing metadata shows: In $3.00 / 1M tokens · Out $15.00 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

Can I use tools or structured outputs with Claude Sonnet 4.5 (2025-09-29)?

Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

Where is the canonical page for Claude Sonnet 4.5 (2025-09-29)?

https://llmapi.ai/models/anthropic-claude-sonnet-4-5-20250929/

Does Claude Sonnet 4.5 (2025-09-29) support streaming?

Yes—at least one listed provider advertises streaming.

Is Claude Sonnet 4.5 (2025-09-29) a chat model?

Yes—Claude Sonnet 4.5 (2025-09-29) is exposed as a chat/completions-style endpoint on LLM.API.

What are limitations of Claude Sonnet 4.5 (2025-09-29)?

Like other API models, Claude Sonnet 4.5 (2025-09-29) can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

What is the context length for Claude Sonnet 4.5 (2025-09-29)?

Reported context for Claude Sonnet 4.5 (2025-09-29) is 200K tokens. Always verify the active provider row if multiple providers are listed.

How do I call Claude Sonnet 4.5 (2025-09-29) via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "claude-sonnet-4-5-20250929" and your LLM.API key. See the code snippet on this page.

When should I choose Claude Sonnet 4.5 (2025-09-29)?

You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need Claude Sonnet 4.5 (2025-09-29).

Claude Sonnet 4.5 (2025-09-29)

Up to 30%

Claude Sonnet 4.5 (2025-09-29) is available on LLM.API as an OpenAI-compatible chat endpoint—route Anthropic quality through one key with transparent token pricing.

Start Using API

What is Claude Sonnet 4.5 (2025-09-29)?

Claude Sonnet 4.5 (2025-09-29) is an Anthropic chat model exposed on LLM.API under id `claude-sonnet-4-5-20250929`. claude-sonnet-4-5-20250929 provided by anthropic, aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text inputs and text outputs over an OpenAI-compatible API.

Providers

LLM.API routes Claude Sonnet 4.5 (2025-09-29) to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput

anthropic$3 in

$15 out

aws-bedrock$3 in

$15 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
anthropic30% off	in $3000; out — per 1M tokens	200K tokens	vision, tools, streaming, reasoning, web search, JSON, structured
aws-bedrock30% off	in $3000; out — per 1M tokens	200K tokens	vision, tools, streaming, reasoning, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Claude Sonnet 4.5 (2025-09-29) right here — free to start.

Claude Sonnet 4.5 (2025-09-29)

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Claude Sonnet 4.5 (2025-09-29) through the OpenAI-compatible API — POST /v1/chat/completions.

python

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

{
  "model": "claude-sonnet-4-5-20250929",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

Multilingual drafting
Drafts and translates professional content across major business languages. Tuned to how teams typically call Claude Sonnet 4.5 (2025-09-29).
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Tuned to how teams typically call Claude Sonnet 4.5 (2025-09-29).
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces. Tuned to how teams typically call Claude Sonnet 4.5 (2025-09-29).
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Reflects Anthropic positioning for this endpoint.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Tuned to how teams typically call Claude Sonnet 4.5 (2025-09-29).

6 Most Valuable Use Cases

Policy Q&A bots with careful refusal behavior with Claude Sonnet 4.5 (2025-09-29)
Customer support copilots that draft accurate, on-brand replies
Product analytics narration and anomaly explanations with Claude Sonnet 4.5 (2025-09-29)
Multilingual localization drafts for UX copy
Meeting-note cleanup and action-item generation with Claude Sonnet 4.5 (2025-09-29)
Internal knowledge assistants grounded with your retrieval layer

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

Intelligent AI Routing

Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model.
Cost-Aware Execution

Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend.
Resilient Fallback Flows

Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users.
Deep LLM Observability

Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere.
Task-Level Orchestration

Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models.
High-Throughput Batch APIs

Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.

Why run Claude Sonnet 4.5 (2025-09-29) on LLM.API?

Unified AI Routing
Production: Reach Claude Sonnet 4.5 (2025-09-29) and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Claude Sonnet 4.5 (2025-09-29).
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Claude Sonnet 4.5 (2025-09-29) alongside the rest of your stack.
Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Practical: Swap Claude Sonnet 4.5 (2025-09-29) for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Claude Sonnet 4.5 (2025-09-29))
You want OpenAI-compatible chat completions through a single LLM.API key (Claude Sonnet 4.5 (2025-09-29))
You need provider failover options exposed for this model id (Claude Sonnet 4.5 (2025-09-29))
You need a general-purpose text model for assistants, agents, or content workflows (Claude Sonnet 4.5 (2025-09-29))

Avoid if...

You require on-prem only deployment with no cloud inference
Your use case depends on unpublished proprietary benchmarks not listed here
You need pure embedding, OCR, or media generation instead of chat
You need guaranteed real-time hard latency SLAs without benchmarking the provider

RELIABILITY