- Text Generation
MiniMax M2 is an open‑weight Mixture‑of‑Experts large language model from MiniMax, designed to deliver high coding and agentic workflow performance with low latency and cost. It…
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Claude Sonnet 4.6
- Text Generation
Claude Sonnet 4.6 is Anthropic’s most capable Sonnet‑tier large language model, offering Opus‑class performance in coding, computer use, and long‑context reasoning with a 1 million token context window in beta.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Claude Sonnet 4.6?
Claude Sonnet 4.6 is a multimodal large language model from Anthropic designed to balance high intelligence with speed and cost efficiency. It is used for software development and debugging, long‑horizon knowledge work and planning, and interacting with real computer environments by navigating applications and documents. It also supports design, analysis, and other general assistant tasks over very long contexts. Claude Sonnet 4.6 belongs to the Claude Sonnet family in Anthropic’s Claude model series, succeeding earlier Sonnet 4.x generations such as Sonnet 4.5.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, following complex instructions, maintaining context, and adapting tone for assistance, analysis, and brainstorming.
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Image Understanding
Interprets images by identifying objects, text, layout, and visual relationships to support descriptions, analysis, and reasoning tasks.
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Text Translation
Translates between major languages, preserving meaning and style for instructions, explanations, and general-purpose multilingual communication.
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Document OCR
Extracts machine-readable text from images or document photos, enabling downstream search, summarization, and editing workflows.
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System Monitoring Aid
Helps interpret logs, metrics, and alerts conceptually, supporting troubleshooting and analysis of technical systems when given textual telemetry.
Use cases
6 Most Valuable Use Cases
- Enterprise knowledge assistant
- Invoice and receipt parsing
- Legal research support
- Regulatory change monitoring
- Customer service automation
- Code generation and review
Transparent pricing
Cost Comparison
Save up to ~55% vs. standard Claude Sonnet 4.6 API pricing.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | 99.99% | $0.80 | $4.00 | 200K |
| Anthropic | US East | ~220ms | ~40 tps | 99.9% | ~$1.80 | ~$9.00 | 200K |
| AWS Bedrock | US West | ~260ms | ~35 tps | 99.9% | ~$2.00 | ~$10.00 | 200K |
| Google Cloud Vertex AI | Global | ~250ms | ~30 tps | 99.9% | ~$2.10 | ~$10.50 | 200K |
Performance benchmarks
Technical Specifications
| Metric | Claude Sonnet 4.6 | GPT-4.1 Mini | Gemini 1.5 Flash |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 200K | 128K | 1M |
| Input Price ($/1M) | $0.20 | $0.15 | $0.20 |
| Output Price ($/1M) | $0.80 | $0.60 | $0.60 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 80 tps | 100 tps | 90 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 92.0B
- Prompt tokens processed (30 days)
- 31.5B
- Completion tokens generated (30 days)
- 11.8M
- API requests served (30 days)
- 98.9%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or client code.
One endpoint. Any model. -
Cost-Aware Orchestration
Control spend with price caps, smart model selection, and usage controls so you can experiment freely while keeping production costs predictable and optimized.
Optimize quality per dollar. -
Resilient Fallback Logic
Define automatic failover chains so requests recover from provider outages, rate limits, or timeouts—without shipping new code or impacting end users.
Stay up, even when they’re down. -
End-to-End Observability
Get full visibility into every call—latency, errors, cost, and provider breakdowns—so you can debug faster, tune prompts, and prove performance to stakeholders.
See every token’s journey. -
Task-Native Abstractions
Use high-level task APIs for chat, tools, RAG, and structured outputs instead of wiring raw providers, cutting boilerplate while keeping full config control.
Think in tasks, not providers. -
High-Throughput Batch Jobs
Run large-scale generations, evaluations, and enrichments via optimized batch execution with concurrency controls, retries, and cost tracking built in.
Scale from one to millions.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose assistant for coding, analysis, and explanation tasks.
- You need solid reasoning and writing quality without paying frontier model premiums.
- You need a safety-conscious model for enterprise or regulated-industry applications.
- Your use case involves multi-step problem solving that benefits from iterative deliberation.
- Your use case involves building chat-style assistants that require helpful, polite conversation.
- You need good performance across programming languages, including code review and refactoring support.
Avoid if...
- You need the absolute best open-ended reasoning available, regardless of higher inference cost.
- Your workload requires ultra-low-latency responses for high-frequency, real-time user interactions.
- You need tight integration with proprietary provider tooling available only in other ecosystems.
- You need guaranteed top-tier performance on highly specialized domain knowledge benchmarks.
- Your workload requires running fully on-prem with models you can self-host and tune.
- You need an open-weights model that can be deeply modified beyond API-level configuration.
FAQ
Frequently Asked Questions
-
What is Claude Sonnet 4.6?
Claude Sonnet 4.6 is an Anthropic large language model optimized for balanced cost, quality, and speed across coding, chat, and analysis tasks.
-
What is Claude Sonnet 4.6 best suited for?
Claude Sonnet 4.6 excels at multi-step reasoning, code generation and refactoring, data analysis, and high-quality conversational agents with moderate latency and cost.
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What context window does Claude Sonnet 4.6 support?
Claude Sonnet 4.6 supports context windows up to 200,000 tokens, enabling long documents, multi-file codebases, and complex workflows in a single request.
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What modalities does Claude Sonnet 4.6 support through LLM.API?
Through LLM.API, Claude Sonnet 4.6 supports text input and output, and image inputs for vision-language tasks where enabled by your LLM.API plan.
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How fast is Claude Sonnet 4.6 when called via LLM.API?
Claude Sonnet 4.6 generally returns first tokens within a few hundred milliseconds to a couple seconds, depending on prompt size and LLM.API region.
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How is Claude Sonnet 4.6 priced on LLM.API?
Claude Sonnet 4.6 uses a per-token billing model on LLM.API, with separate input and output token rates defined in LLM.API’s pricing schedule.
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How do I call Claude Sonnet 4.6 from my application using LLM.API?
You select the model identifier for Claude Sonnet 4.6 in your LLM.API completion or chat endpoint request and send prompts using the standard JSON schema.
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How does Claude Sonnet 4.6 compare to larger Claude models?
Claude Sonnet 4.6 typically offers lower cost and latency than flagship Claude models, with slightly reduced peak reasoning depth and creativity.
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What limitations should I be aware of with Claude Sonnet 4.6?
Claude Sonnet 4.6 can still hallucinate facts, mishandle very domain-specific edge cases, and should not be used without human review for high-stakes decisions.
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Does Claude Sonnet 4.6 support streaming responses on LLM.API?
Yes, Claude Sonnet 4.6 can stream tokens incrementally through LLM.API by enabling the streaming option on your request.
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