Everything about Claude Opus 5

Claude Opus 5 at a glance

Anthropic released Claude Opus 5 on July 24, 2026. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. Its weights are not published, so it only runs remotely, through the Anthropic API or partner hosts (43 listed on models.dev). That makes it 3 months old as of October 22, 2026.

We file it under frontier models (24 models in this category on the site).

Benchmark results

According to results published by Epoch AI (checked on October 22, 2026), Claude Opus 5 has a capability index of 92.7 out of 100, computed on 5 benchmarks from our basket.

That puts it at #5 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 93.9% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 98.9% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 85.6% — the best tracked model, GPT-6 Astra, reaches 93.7%
• FrontierCode (advanced programming tasks): 53.4% — the best tracked model, Claude Opus 5.5, reaches 54.6%

How much does Claude Opus 5 cost?

Claude Opus 5 costs $5 per million input tokens and $25 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.15, or $150 for 1,000 such tasks.

That is 1.7 times the median output price of frontier models ($14.5).

Tokens read back from cache cost $0.50 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

What it can do

Its context window reaches 1,000,000 tokens, roughly 1,500 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 128,000 tokens.

It accepts as input: text, image and PDF. Its knowledge stops at May 2026: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

What is it good for?

Given its category, Claude Opus 5 is mainly aimed at hard tasks where quality matters more than cost: deep reasoning, complex code, long analyses.

Its FrontierCode result (53.4%, above the median of tracked models) makes it a serious candidate for coding agents.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Alternatives

In the same category, the models closest to Claude Opus 5 are:

• Claude Fable 5.1 (Anthropic, index 92.6) — see the comparison
• GPT-6.1 Sol (OpenAI, index 92.1) — see the comparison
• Claude Fable 5 (Anthropic, index 90.8) — see the comparison
• GPT-6 Astra (OpenAI, index 94.7) — see the comparison