R1 0528 APIOPEN WEIGHTS164K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is R1 0528?

May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...

R1 0528 on the release timeline

R1 0528 pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, R1 0528 is cheaper than 41% of the 309 models in the catalog. It is served by 4 providers; the best combined rate at our last snapshot was ≈ $0.50 / $2.15 per 1M via DeepInfra, with upstream quantizations fp4, fp8.

What R1 0528 costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

R1 0528 vs alternatives

Specs

How to call R1 0528

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="deepseek/deepseek-r1-0528",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek/deepseek-r1-0528","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "deepseek/deepseek-r1-0528",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes R1 0528 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for R1 0528

• State the goal, not every step. R1 0528 reasons internally — give it the objective, constraints and success criteria and let it plan the approach; over-scripting each step tends to lower quality. Turn effort up for hard problems.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let R1 0528 emit tool calls, execute them, and feed results back for the next turn.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.

Tips are derived from R1 0528's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for R1 0528

Popular open-source projects for running and building with R1 0528 — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to R1 0528

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → deepseek/deepseek-r1-0528. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

R1 0528 ships open weights (deepseek-ai/DeepSeek-R1-0528, ~3.0M downloads and 2.5K likes in the last 30 days), with community quantizations (fp4, fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 4 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because R1 0528 is served by 4 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions