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Claude Sonnet 4.5 vs DeepSeek R1

Claude Sonnet 4.5 (2025) and DeepSeek R1 (2025) are frontier-tier reasoning models from Anthropic and DeepSeek. Claude Sonnet 4.5 ships a 200K-token context window, while DeepSeek R1 ships a 128K-token context window. On pricing, DeepSeek R1 costs $0.1/1M input tokens versus $3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

DeepSeek R1 is ~2900% cheaper at $0.1/1M; pay for Claude Sonnet 4.5 only for long-context analysis.

Specs

Released2025-09-292025-01-20
Context window200K128K
Parameters671B, 37B Active
Architecturedecoder onlydecoder only
LicenseProprietaryOpen Source
Knowledge cutoff2025-12-

Pricing and availability

Claude Sonnet 4.5DeepSeek R1
Input price$3/1M tokens$0.1/1M tokens
Output price$15/1M tokens$0.3/1M tokens
Providers

Capabilities

Claude Sonnet 4.5DeepSeek R1
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: Claude Sonnet 4.5, multimodal input: Claude Sonnet 4.5, function calling: Claude Sonnet 4.5, tool use: Claude Sonnet 4.5, and code execution: DeepSeek R1. Both models share reasoning mode and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Claude Sonnet 4.5 lists $3/1M input and $15/1M output tokens, while DeepSeek R1 lists $0.1/1M input and $0.3/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 lower by about $6.44 per million blended tokens. Availability is 8 providers versus 13, so concentration risk also matters.

Choose Claude Sonnet 4.5 when long-context analysis and larger context windows are central to the workload. Choose DeepSeek R1 when coding workflow support, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions.

FAQ

Which has a larger context window, Claude Sonnet 4.5 or DeepSeek R1?

Claude Sonnet 4.5 supports 200K tokens, while DeepSeek R1 supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, Claude Sonnet 4.5 or DeepSeek R1?

DeepSeek R1 is cheaper on tracked token pricing. Claude Sonnet 4.5 costs $3/1M input and $15/1M output tokens. DeepSeek R1 costs $0.1/1M input and $0.3/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Claude Sonnet 4.5 or DeepSeek R1 open source?

Claude Sonnet 4.5 is listed under Proprietary. DeepSeek R1 is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, Claude Sonnet 4.5 or DeepSeek R1?

Claude Sonnet 4.5 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for multimodal input, Claude Sonnet 4.5 or DeepSeek R1?

Claude Sonnet 4.5 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Claude Sonnet 4.5 and DeepSeek R1?

Claude Sonnet 4.5 is available on Microsoft Foundry, Anthropic, Snowflake Cortex, GCP Vertex AI, and AWS Bedrock. DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.