Skip to content
Dashboard

DeepSeek R1 0528

DeepSeek R1 0528 is DeepSeek's open-source reasoning model, released January 20, 2025. It scores 79.8% Pass@1 on AIME 2024 and 97.3% on MATH-500. Weights ship under the MIT License for commercial use. Your use is subject to DeepSeek's Terms & Privacy Policies.

Implicit CachingReasoningTool Use
import { streamText } from 'ai'
const result = streamText({
model: 'deepseek/deepseek-r1',
prompt: 'Why is the sky blue?'
})
Read docs

Copy link to headingPlayground

Try out DeepSeek R1 0528 by DeepSeek. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

deepseek logo
deepseek logo

DeepSeek R1 0528

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider
Context
Max Output
Latency
Throughput
Input
Output
Cache
Web Search
Capabilities
ZDR
No Training
Release Date
160K16K
0.5s
23tps
$0.50/M
$2.15/M
Read:$0.35/M
Write:
01/20/2025
128K8K
0.7s
95tps
$1.35/M
$5.40/M
01/20/2025

Copy link to headingThroughput

P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.

Copy link to headingLatency

P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.

Copy link to headingUptime

Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.

Copy link to headingMore models by DeepSeek

Model
Context
Latency
Throughput
Input
Output
Cache
Web Search
Capabilities
Providers
ZDR
No Training
Release Date
1M
0.9s
345tps
$0.09/M
$0.18/M
Read:$0.0/M
Write:
alibaba logo
baseten logo
deepinfra logo
+5
07/31/2026
1M
0.5s
308tps
$0.09/M
$0.18/M
Read:$0.0/M
Write:
alibaba logo
azure logo
baseten logo
+7
04/23/2026
1M
0.4s
143tps
$1.74/M$0.43/M
$3.48/M$0.87/M
Read:$0.0/M
Write:
alibaba logo
azure logo
baseten logo
+5
04/23/2026
164K
1.0s
101tps
$0.28/M
$0.42/M
Read:$0.03/M
Write:
bedrock logo
deepinfra logo
deepseek logo
+1
12/01/2025
164K
0.6s
58tps
$0.26/M
$0.38/M
Read:$0.13/M
Write:
bedrock logo
deepinfra logo
novita logo
12/01/2025
164K
1.0s
36tps
$0.25/M
$0.95/M
Read:$0.13/M
Write:
deepinfra logo
novita logo
sambanova logo
08/21/2025

Copy link to headingAbout DeepSeek R1 0528

DeepSeek R1 0528 was released January 20, 2025 and breaks from conventional reasoning model training. Instead of relying on human-written reasoning traces, DeepSeek applied reinforcement learning directly to the base DeepSeek-V3 weights. Unconstrained RL let emergent behaviors like self-verification, self-reflection, and long chain-of-thought generation develop organically.

The architecture is a 671B Mixture-of-Experts (MoE) model that activates 37B parameters per forward pass. On AIME 2024, DeepSeek R1 0528 achieves 79.8% Pass@1, on par with OpenAI o1. On MATH-500 it reaches 97.3%. The release documentation also highlights strong code and general reasoning performance.

The MIT License is permissive: many proprietary reasoning models impose stricter restrictions. DeepSeek released six smaller derivatives alongside the full model. The 32B and 70B versions match OpenAI o1-mini performance, giving teams cost-efficient alternatives to the full 671B model.

Copy link to headingWhat To Consider When Choosing a Provider

  • Configuration: DeepSeek R1 0528 generates verbose reasoning traces before final answers. Budget output tokens generously and account for variable response length when estimating costs.
  • Zero Data Retention: AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.
  • Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use DeepSeek R1 0528

Best for

  • Competitive mathematics: Formal proof construction and quantitative reasoning where AIME 2024 and MATH-500 benchmark results match your task
  • Code generation and debugging: Algorithm design where RL-derived problem-solving patterns produce self-correcting chains before final output
  • Complex analytical reasoning: Multi-step reasoning in finance, science, and engineering where showing work and self-verification build trust

Consider alternatives when

  • Conversation or summarization: Extended reasoning traces add unnecessary output token cost for content generation workloads
  • Hybrid thinking modes: DeepSeek-V3.1 or later supports both thinking and non-thinking modes through the same endpoint
  • Strict latency requirements: Variable response times from long reasoning chains are not acceptable when latency is a hard constraint
  • Pure creative writing: Structured reasoning adds no quality benefit for open-ended generation tasks

DeepSeek R1 0528 matches closed-source models on published benchmarks while shipping weights under the MIT License. For math, code, and formal reasoning workloads, it fits teams that need open weights.