anthropic
Claude Opus 4.7
Claude Opus 4.7 is Anthropic's high-context, multimodal AI optimized for tool-enabled workflows and long-document reasoning. It sits in the Opus line alongside Claude Opus 4.6 and Claude Sonnet 4.6 and competes with other top-tier models in the same bracket. Opus 4.7 is aimed at developers building agentic systems, retrieval + synthesis pipelines, and teams that need faithful, multi-step outputs rather than the lowest-cost option.
Performance
On our 12-test suite Opus 4.7 shines in multi-step, agentic, and faithfulness metrics. Top strengths (all 5/5 in our testing): tool calling (5/5; tied for 1st with 17 other models out of 55), agentic planning (5/5; tied for 1st with 15 others out of 55), and faithfulness (5/5; tied for 1st with 33 others out of 56). It also scores 5/5 on creative problem solving, persona consistency, strategic analysis, and long context. Overall it ranks 8 of 53 models on our leaderboard. Notable weaknesses: classification (3/5; rank 31 of 54) is middling, and multilingual ranks relatively low (4/5 but rank 36 of 56), so routing-heavy or broad-language production may not be the best fit. Structured output is competent (4/5; midrank), and safety calibration is moderate (3/5; rank 10 of 56). The model’s enormous context window (1,000,000 tokens) and max output token cap (128,000) amplify its long-context advantages in document-level tasks.
Pricing
Opus 4.7 charges $5 per million input tokens and $25 per million output tokens. In practice that means: 100k output tokens ≈ $2.50; 1M output tokens = $25; 10M output tokens = $250. Example: 1,000 requests that each send 200 input tokens and return 1,000 output tokens cost about $26 total (0.2M input → $1; 1.0M output → $25). Compared with bracket peers, Opus 4.7 is on the expensive end: Claude Sonnet 4.6 lists $15 per million output and GPT-5.2 lists $14 per million output in the same peer set. Lower-cost alternatives in the bracket include Gemma 4 31B at $0.38 per million output for much cheaper bulk usage, while Opus 4.7’s pricing favors high-value, high-accuracy workloads where its strengths pay off.
anthropic
Claude Opus 4.7
Benchmark Scores
External Benchmarks
Pricing
Input
$5.00/MTok
Output
$25.00/MTok
modelpicker.net
Real-World Costs
Pricing vs Performance
Output cost per million tokens (log scale) vs average score across our 12 internal benchmarks
Try It
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_OPENROUTER_KEY",
)
response = client.chat.completions.create(
model="anthropic/claude-opus-4.7",
messages=[
{"role": "user", "content": "Hello, Claude Opus 4.7!"}
],
)
print(response.choices[0].message.content)Recommendation
Choose Claude Opus 4.7 if you build: (1) agentic systems that need precise function selection and argument sequencing (tool calling 5/5); (2) long-context retrieval, summarization, or synthesis where 30K+ and up to 1,000,000-token context matters (long context 5/5); (3) high-stakes content that must remain faithful to sources (faithfulness 5/5) or needs creative, feasible ideas (creative problem solving 5/5). Avoid Opus 4.7 for high-volume, low-margin chat or classification-at-scale use cases: its output cost is $25 per million tokens (higher than Sonnet 4.6 at $15 and GPT-5.2 at $14), and classification scores are only 3/5 in our testing. If you need the cheapest per-token option for bulk conversational volume, consider lower-cost bracket peers; if you need the best classification or top-ranked multilingual performance, evaluate models that rank higher on those specific tests.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.