Guide
Claude opus 5AnthropicClaudeAi model releaseClaude Opus 5: Features, Pricing & What's New (2026)
Claude Opus 5 is Anthropic's new flagship model, launched in July 2026. The headline is the price-to-capability jump: it lands close to Claude Fable 5, Anthropic's most capable model, at roughly half the cost, while pricing stays flat at the same $5 and $25 per million tokens Opus 4.8 charged. It is now the default on Claude Max and the strongest model available to Claude Pro subscribers. Here is what it does and how it compares.
By the FluxNote Editorial Team · Last updated: July 25, 2026

What is Claude Opus 5?
Claude Opus 5 is the newest flagship in Anthropic's Opus line, released in July 2026. Anthropic positions it as coming close to the frontier intelligence of Claude Fable 5 across coding, knowledge work and complex reasoning, at about half the price.
Notably the price did not move from the previous generation, so it is a capability jump with no cost increase. Here is the spec at a glance.
| Attribute | Claude Opus 5 |
|---|---|
| Maker | Anthropic |
| Released | July 2026 |
| Input price | $5 per million tokens |
| Output price | $25 per million tokens |
| Context window | 1M tokens |
| Positioning | Near Fable 5 capability at half the price |
| Availability | Default on Claude Max, available on Claude Pro |
| Extras | Fast mode, cost-versus-capability control |
What is new in Claude Opus 5?
Three things stand out.
First, the capability-to-price ratio: Opus 5 performs close to Claude Fable 5 while costing about half as much, and it holds the same pricing as Opus 4.8, so the upgrade is effectively free if you were already on Opus.
Second, Anthropic reports substantial gains in self-verification and deliberate iteration, meaning the model checks its own work and refines rather than committing to a first answer, which matters most on long agentic tasks.
Third, it ships with a fast mode and a control that lets you trade cost against capability per task, so you are not paying frontier prices for routine work.
Anthropic also describes it as its most aligned model to date.
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Claude Opus 5 vs Opus 4.8 vs Fable 5
The useful comparison is capability against cost, because that is the axis Opus 5 moves. Fable 5 remains Anthropic's most capable model, but Opus 5 closes most of the gap for half the money, and Opus 4.8 is now superseded at the same price.
| Model | Capability | Price per 1M (in / out) | Best for |
|---|---|---|---|
| Claude Fable 5 | Highest | $10 / $50 | The hardest long-horizon reasoning |
| Claude Opus 5 | Near Fable 5 | $5 / $25 | Best capability per dollar |
| Claude Opus 4.8 | Strong | $5 / $25 | Superseded by Opus 5 at same price |
For most teams Opus 5 is now the default choice: you give up a little at the very top end and halve the bill. Reach for Fable 5 only when a task genuinely needs the maximum.
What is Claude Opus 5 good for?
Opus 5 suits work where quality matters but frontier pricing is hard to justify: production coding and refactors, long agentic runs that need the model to verify its own output, research and analysis, and knowledge work with a lot of context, helped by the 1M token window.
The self-verification improvements make it a better fit than earlier models for tasks that run unattended, because it is more likely to catch its own mistakes rather than confidently finish something wrong.
The cost-versus-capability control also makes it practical to run at scale, dialing effort down for routine calls and up for the hard ones.
How to use Claude Opus 5
Opus 5 is available in the Claude apps (the default on Claude Max, and the strongest model for Claude Pro) and through Anthropic's API.
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Pro Tips
- If you are already on Opus 4.8, Opus 5 is a straight upgrade: same $5 and $25 per million tokens, meaningfully more capability.
- Use the cost-versus-capability control to run routine calls cheaply and save maximum effort for genuinely hard tasks.
- Reach for Fable 5 only when a task needs the absolute ceiling; Opus 5 covers most work at half the price.
- The self-verification gains matter most on long unattended runs, where the model catching its own errors saves the whole task.
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