Anthropic
Claude Opus 4.7
model
claude-opus-4-7PremiumStableVisionStreamingVisionToolsBatchLong contextUnited States
Context
1.0M
Input / 1M tokens
$5.00
Output / 1M tokens
$25.00
Cached input / 1M tokens
$1.18
Cache write / 1M tokens
$6.25
About this model
Claude Opus 4.7
Anthropic's flagship model — Claude Opus 4.7 leads on coding, reasoning, and agentic workflows. Best-in-class for complex multi-step tasks where you need the model to plan, write code, and verify its own work.
Key facts
- Maker
- Anthropic
- Context window
- 1.0M tokens
- Max output
- 128K tokens
- Lane
- Guarded: the provider has confirmed it does not train on your data
- Modality
- Text and image input
- Input price
- $5.00 per 1M tokens
- Output price
- $25.00 per 1M tokens
- Processing region(s)
- us
- Route
- Via Anthropic's API
- Cheapest plan
- Available from the Pro plan
- Price comparison
- Priced below 15 of 255 pro-band guarded models on Anoman (blended price: 1 part input to 3 parts output).
Released
2025-04
Training cutoff
2024-11
Parameters
Not disclosed
Ways to call
Ways to call Claude Opus 4.7
Each model ID below is callable through the Anoman AI API. Price and processing region depend on the route.
| Model ID | Route | Input / output per 1M tokens | Region(s) | Lane | Context |
|---|---|---|---|---|---|
| claude-opus-4-7 | Via Anthropic's API | $5.00 / $25.00 | us | Guarded | 1.0M |
| claude-opus-4-7-openrouter | Via OpenRouter | $5.00 / $25.00 | global | Guarded | 1.0M |
Best for
Use cases
- Autonomous coding agents (Claude Code, Cline, Cursor)
- Complex research + literature analysis
- Multi-step planning with tool use
- High-stakes content where quality > cost
- Very long context (1M token window)
Strengths
What it does well
- Industry-leading on SWE-bench and coding evals
- Strong refusal calibration — minimal over-refusal
- 1M-token context window
- Prompt caching cuts repeat-prompt cost by 90%
- Vision + tool calling + structured output all native
Limitations
Know the trade-offs
- Premium pricing — best for high-value workloads only
- Higher latency than Sonnet/Haiku — plan for 3-8 s typical
- Output tokens charged at premium rate — set max_tokens carefully
Code example
Chat completion with Claude Opus 4.7
from openai import OpenAI
client = OpenAI(
base_url="https://api.anoman.io/v1",
api_key="anm-sk-..."
)
response = client.chat.completions.create(
model="claude-opus-4-7",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)Alternatives
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