DeepSeek R1-0528: Reasoning, Access, and Coding Use

R1-0528 vs Original R1: What Changed

The update focused on reasoning after the base-model training stage. It used more post-training compute and revised optimization methods, while retaining the original base-model lineage. The open checkpoint is listed at 685 billion parameters, including its multi-token prediction component, and remains under the MIT License.

Several practical behaviors changed as well. The update added system-prompt support, removed the old recommendation to force a thinking prefix, and improved JSON output and function calling. It can also produce longer reasoning traces on difficult problems, so a higher score may come with more generated tokens, latency, and review work.

Keep the hosted and open-weight surfaces separate. The hosted release used a 64K context limit, while the open checkpoint supports 128K. A third-party endpoint may apply another limit, quantization, or serving configuration.

How to Compare R1-0528 with o3

The release positioned the update near o3 on selected reasoning evaluations. That is a benchmark claim, not a general statement that the models are interchangeable. Scores depend on the exact model variant, prompt, sampling settings, tool access, answer budget, and scoring rules.

For a useful comparison, hold these conditions constant:

• Use the same problem set and remove examples seen during prompt development.
• Give both systems the same tools, time limit, retry policy, and output format.
• Score final answers and patches, not the fluency or length of visible reasoning.
• Track token use, latency, and human review time alongside task success.

This approach avoids turning a narrow math or coding result into a universal winner claim.

Math and Coding Benchmarks

DeepSeek reported gains over the original R1 on AIME 2025, LiveCodeBench, Aider Polyglot, and SWE Verified under its stated evaluation settings. For example, its reported SWE Verified result rose from 49.2 to 57.6, while AIME 2025 moved from 70.0 to 87.5. DeepSeek used the Agentless framework for SWE Verified.

Treat those numbers as a starting point for a pilot. Repository work adds dependency setup, tool failures, incomplete tests, and project conventions that a benchmark may not cover. A compact coding evaluation can include:

1. Fix one reproducible bug and require failing and passing test output.
2. Refactor one module without changing its public interface.
3. Ask for an algorithm, then test edge cases and complexity independently.
4. Run the same task twice to measure consistency, not only peak quality.

API Access and Cost

The original hosted R1 route is no longer the current DeepSeek API model. The deepseek-reasoner name later pointed to newer releases and was retired on July 24, 2026. Current official API work uses the V4 family, so historical R1 token prices should not be treated as an available R1-0528 rate.

You can still evaluate the open checkpoint through self-hosting or an independent provider. Compare total operating cost rather than token price alone. Include GPU memory, quantization, prompt caching, concurrency, idle capacity, observability, and the engineering time needed to maintain the serving stack. Confirm the exact checkpoint and context limit before comparing quotes.

Checking DeepSeek R1-0528 Access in Verdent

Verdent's current built-in model catalog lists DeepSeek V4 Pro, not this R1 checkpoint. Start with Verdent model pricing to confirm the enabled lineup. If you plan to bring a provider connection, review Verdent BYOK configuration and check the live model selector. A provider connection does not guarantee that this exact checkpoint is exposed.

Related Guides

• GPT-5.2
• Llama 4 Scout
• DeepSeek R1

Reproduce One Reasoning Task Before You Commit

Choose a task with a deterministic answer, record the checkpoint and serving settings, and compare correctness, latency, token use, and review effort. Then check Verdent's current model lineup before mapping the result to a production workflow.