Claude Opus 4 API200K context

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

What is Claude Opus 4?

Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in...

Claude Opus 4 on the release timeline

Claude Opus 4 pricing

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

On output price, Claude Opus 4 is cheaper than 2% of the 309 models in the catalog.

What Claude Opus 4 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.

Claude Opus 4 vs alternatives

When not to use Claude Opus 4

• It is premium-priced (output cheaper than only 2% of the catalog) — for routine work a cheaper model likely does the job.
• It is served by a single provider, so there is less failover headroom during an outage.

Specs

How to call Claude Opus 4

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="anthropic/claude-opus-4",
    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":"anthropic/claude-opus-4","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: "anthropic/claude-opus-4",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Claude Opus 4 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Claude Opus 4

• State the goal, not every step. Claude Opus 4 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.
• Huge 200K context — but anchor the ask. You can paste whole documents or codebases; models attend most to the start and end, so put the key instruction at the top and restate it after long inputs.
• It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Claude Opus 4 emit tool calls, execute them, and feed results back for the next turn.
• It's premium — spend where it counts. Route easy calls to a cheaper model and reserve Claude Opus 4 for the hard reasoning; trim system prompts and few-shot examples that don't move the result.

Tips are derived from Claude Opus 4's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Claude Opus 4

Popular open-source frameworks and agents to build with Claude Opus 4 over the API — star counts pulled from GitHub (July 2026).

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

Migrating to Claude Opus 4

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → anthropic/claude-opus-4. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

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. See the error-code guide and failover setup.

Related reading

Frequently asked questions