Unofficial community guide for Claude Opus 5. Not affiliated with Anthropic. All trademarks belong to their owners.
Claude Opus 5 is the Anthropic model announced on July 24, 2026, available on the Claude API as claude-opus-5. Anthropic describes it as "a thoughtful and proactive model that comes close to the frontier intelligence of Claude Fable 5 at half the price", and as the new default on Claude Max and the strongest model on Claude Pro. If you are weighing opus 5 against the models around it, this page collects what the launch post, the platform docs and an independent system-card review actually say about each option, and adds one non-LLM alternative for the media-generation side of a stack. Nothing here is a benchmark we ran; every attribute is traceable to those sources.
Need image, video or audio generation alongside a text model? Try Synexa - one REST endpoint and Python SDK for FLUX, video and audio models, pay per run.
| Option | What the sources say | Cost relative to Opus 5 | API model ID |
|---|---|---|---|
| Claude Opus 5 | "Close to the frontier intelligence of Claude Fable 5"; 1M context (default and max), 128k max output, thinking on by default; state of the art on Frontier-Bench and GDPval-AA | Baseline; same cost as Opus 4.8 per the announcement | claude-opus-5 |
| Claude Fable 5 | The frontier model Opus 5 is measured against; Opus 5 is within 0.5% of its peak CursorBench 3.2 score at max effort | About twice Opus 5 ("at half the price") | Not given in these sources |
| Claude Opus 4.8 | Predecessor; Opus 5 "more than doubles" its Frontier-Bench v0.1 performance at lower cost per task | Same cost as Opus 5 | Not given in these sources |
| Claude Mythos 5 | Remains ahead of Opus 5 on cybersecurity tasks; described as a larger model in the system-card review | Not stated | Not given in these sources |
| Claude Sonnet 5 | Listed beside Opus 5 in the platform docs navigation | Not stated in these sources | See its docs page |
| Synexa | Hosted model API: one REST endpoint plus a Python SDK for FLUX, video and audio models | Pay per run, not per token | Not an LLM |
The reference point. The announcement leads with cost-effectiveness: the same price as Opus 4.8 with "greatly improved performance", and on CursorBench 3.2 at max effort "within 0.5% of Fable 5's peak score, but at half the cost per task". On ARC-AGI 3 the announcement says Opus 5's score is three times the next-best model, on Zapier AutomationBench its pass rate is around 1.5x the next-best model at the same cost per task, and on OSWorld 2.0 it surpasses Fable 5's best result "at just over a third of the cost". The docs list two breaking changes for code that ran on Opus 4.8: thinking is now on by default, and thinking can be disabled only at effort high or below. Priority Tier is not supported.
The model Anthropic compares Opus 5 against throughout the launch post. The sources describe Opus 5 as coming close to Fable 5's intelligence "at half the price", and the system-card review puts it as "modestly below Fable 5, but closer to Fable than Opus 4.8". Pick Fable 5 when you have measured that the remaining gap matters for your task and the cost is acceptable. The platform docs navigation currently lists Claude Fable 5.1 as the top model page; check the models overview for the current line-up and IDs.
The predecessor, and the migration source the docs address. Since the announcement states Opus 5 delivers "greatly improved performance for the same cost", the main reason to stay on Opus 4.8 is code that depends on the old defaults: on Opus 4.8 thinking was off unless requested, and on Opus 5 it is on by default and cannot be disabled above effort high. The migration guide covers the move.
Mentioned in both the announcement and the system-card review as the model that remains ahead of Opus 5 on cybersecurity tasks. The review attributes part of that gap to deliberate choices: Opus 5 "cannot string together lots of exploits on the fly the way that Mythos 5 can", partly because Anthropic "deliberately avoided training on cyber-related tasks". Neither source describes how Mythos 5 is accessed or priced, so treat it as a comparison point rather than a drop-in option.
The mid-tier model listed next to Opus 5 in the platform docs. The Opus 5 sources do not give its pricing or benchmarks, so the honest summary is: read its overview page and Anthropic's choosing a model guide, then measure on your own workload. The system-card review's note that you "may want to usually use less effort than you might expect" is worth testing before dropping a tier.
Synexa is not a text model and does not compete with Opus 5 on reasoning. It is a hosted model API that puts FLUX image models, video models and audio models behind one REST endpoint with a Python SDK, billed per run rather than per token. It belongs on this list because a lot of products that call an Opus-class model for planning or text also need to produce an image, a clip or a voice line, and wiring three vendors for that is the part that usually hurts. If that is your situation, try Synexa for the media side and keep Opus 5 for the text side.
- Default choice for agentic coding and knowledge work: Opus 5. The sources position it as the everyday model, with the effort setting as the main cost lever.
- You have measured a gap on your task: Fable 5, accepting the cost difference the announcement describes.
- Legacy code that relies on thinking being off: stay on Opus 4.8 only until you have read the migration guide; the price is the same.
- Cybersecurity evaluation work: the sources say Mythos 5 is ahead, and say nothing about how to get it; do not plan around it without confirming access.
- Cost-sensitive volume: try Opus 5 at lower effort first, then Sonnet 5, and measure.
- Media generation next to any of the above: Synexa.
Whatever text model you land on, check the pricing page rather than a blog post for current rates, since introductory prices change. And if the reason you are reading an opus 5 alternatives page is that you also need images, video or audio behind an API, try Synexa - one endpoint for FLUX, video and audio models, pay per run.
Last reviewed: 2026-09-22