Anthropic: Claude Opus 5.5
2 providers live- 2 live providers
- 100% uptime
- 2.98 s first token
- 334 tok/s output
- 25% off
Claude Opus 5.5 is Anthropic's flagship model for demanding reasoning, coding, and long-horizon agentic work, succeeding Claude Opus 5. It is particularly strong at multi-step changes in large codebases, code review and bug finding, financial and scientific analysis, and reading dense charts, diagrams, and screenshots, and it is more careful than its predecessor about only stating figures and citing sources it can back up. The model completes comparable tasks in fewer steps and with fewer tokens than Opus 5, and reports on its work in plainer language, with clear updates on what it did, what it found, and what it needs from the user. Thinking is always adaptive, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings remain effective for latency-sensitive workloads.
- Context
- 1M
- tokens
- Max output
- 128K
- tokens
- Input
$4.00$3.00- per million tokens, 25% off
- Output
$20.00$15.00- per million tokens, 25% off
Providers
Ranked by price and speed. Requests go to the best healthy provider, then fail over down the list.BenchmarksPrice history
No price changes since Oct 22, 2026. The history shows prices before discounts.
- Anthropic$4.00 in, $20.00 out
Performance
Last 24 hours, from llm.buzz's test requestsThroughput (tok/s)
- llm.buzz134 tok/s
- Anthropic334 tok/s
Data table
| Provider | From (UTC) | p50 | p90 | p99 |
|---|---|---|---|---|
| llm.buzz | Oct 26, 06:00 | 134 tok/s | 134 tok/s | 134 tok/s |
| llm.buzz | Oct 26, 10:00 | 193 tok/s | 193 tok/s | 193 tok/s |
| llm.buzz | Oct 26, 14:00 | 112 tok/s | 112 tok/s | 112 tok/s |
| llm.buzz | Oct 26, 19:00 | 134 tok/s | 134 tok/s | 134 tok/s |
| llm.buzz | Oct 26, 23:00 | 112 tok/s | 112 tok/s | 112 tok/s |
| llm.buzz | Last 24 hours | 134 tok/s | 112 tok/s | 112 tok/s |
| Anthropic | Oct 26, 05:00 | 278 tok/s | 278 tok/s | 278 tok/s |
| Anthropic | Oct 26, 09:00 | 577 tok/s | 577 tok/s | 577 tok/s |
| Anthropic | Oct 26, 13:00 | 334 tok/s | 334 tok/s | 334 tok/s |
| Anthropic | Oct 26, 18:00 | 334 tok/s | 334 tok/s | 334 tok/s |
| Anthropic | Oct 26, 22:00 | 278 tok/s | 278 tok/s | 278 tok/s |
| Anthropic | Oct 27, 03:00 | 401 tok/s | 401 tok/s | 401 tok/s |
| Anthropic | Last 24 hours | 334 tok/s | 278 tok/s | 278 tok/s |
Latency (time to first token)
- llm.buzz4.29 s
- Anthropic2.98 s
Data table
| Provider | From (UTC) | p50 | p90 | p99 |
|---|---|---|---|---|
| llm.buzz | Oct 26, 06:00 | 4.29 s | 4.29 s | 4.29 s |
| llm.buzz | Oct 26, 10:00 | 8.89 s | 8.89 s | 8.89 s |
| llm.buzz | Oct 26, 14:00 | 38.2 s | 38.2 s | 38.2 s |
| llm.buzz | Oct 26, 19:00 | 3.57 s | 3.57 s | 3.57 s |
| llm.buzz | Oct 26, 23:00 | 3.57 s | 3.57 s | 3.57 s |
| llm.buzz | Last 24 hours | 4.29 s | 38.2 s | 38.2 s |
| Anthropic | Oct 26, 05:00 | 2.98 s | 2.98 s | 2.98 s |
| Anthropic | Oct 26, 09:00 | 2.98 s | 2.98 s | 2.98 s |
| Anthropic | Oct 26, 13:00 | 2.98 s | 2.98 s | 2.98 s |
| Anthropic | Oct 26, 18:00 | 3.57 s | 3.57 s | 3.57 s |
| Anthropic | Oct 26, 22:00 | 2.98 s | 2.98 s | 2.98 s |
| Anthropic | Oct 27, 03:00 | 2.98 s | 2.98 s | 2.98 s |
| Anthropic | Last 24 hours | 2.98 s | 3.57 s | 3.57 s |
Uptime
Last 72 hours, by hour- Up: 95% or more served
- Degraded: 85% to 95%
- Down: below 85%
- No data
- From llm.buzz's test requests
Questions
How much does Anthropic: Claude Opus 5.5 cost?
Anthropic: Claude Opus 5.5 costs $3.00 per million input tokens and $15.00 per million output tokens at llm.buzz, the provider requests go to first, with cache reads at $0.15 per million tokens. These prices are 25% off until 2026-11-06 00:00 UTC. Each provider sets its own price, and you pay the price of the provider that answered.
Do providers train on my prompts?
Each provider declares it, and the table above shows every declaration. You can limit a request to providers that keep nothing, or to providers in the EU.
Can I serve Anthropic: Claude Opus 5.5 myself?
Yes. Apply as a provider, add your endpoint and list this model at your price. It goes live once the automated check passes.
Similar models
Call it
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.llm.buzz/api/v1",
api_key=os.environ["LLM_BUZZ_KEY"],
)
reply = client.chat.completions.create(
model="anthropic/claude-opus-5.5",
messages=[{"role": "user", "content": "Hello"}],
)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.llm.buzz/api/v1",
apiKey: process.env.LLM_BUZZ_KEY,
});
const reply = await client.chat.completions.create({
model: "anthropic/claude-opus-5.5",
messages: [{ role: "user", content: "Hello" }],
});curl https://api.llm.buzz/api/v1/chat/completions \
-H "Authorization: Bearer $LLM_BUZZ_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "anthropic/claude-opus-5.5",
"messages": [{"role": "user", "content": "Hello"}]}'Endpoints
POST /api/v1/chat/completionsPOST /api/v1/responsesPOST /api/v1/messagesAuthentication
Send your llm.buzz key as a Bearer token: Authorization: Bearer $LLM_BUZZ_KEY. Create keys in the console. The same key calls every model; each request pays the price of the provider that answered it.
More in Authentication and the API reference.
README
Claude Opus 5.5 is Anthropic's flagship model for demanding reasoning, coding, and long-horizon agentic work, succeeding Claude Opus 5. It is particularly strong at multi-step changes in large codebases, code review and bug finding, financial and scientific analysis, and reading dense charts, diagrams, and screenshots, and it is more careful than its predecessor about only stating figures and citing sources it can back up. The model completes comparable tasks in fewer steps and with fewer tokens than Opus 5, and reports on its work in plainer language, with clear updates on what it did, what it found, and what it needs from the user. Thinking is always adaptive, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings remain effective for latency-sensitive workloads.
- Model ID
- anthropic/claude-opus-5.5
- Takes
- Text, image
- Answers
- Text
- Context
- 1M tokens
- Max output
- 128K tokens
- Capabilities
- Tools, Caching, Structured outputs
- Parameters
- max_tokens, response_format, structured_outputs, temperature, tool_choice, tools
- Weights
- Closed
- On llm.buzz since
- Oct 22, 2026