The short version (3 lines)
Opus 4.8 costs $25 per million output tokens versus Sonnet 4.6’s $15 — about a 1.7x gap.
Complex refactoring and long-running agent work favor Opus 4.8; drafting, bulk classification, and high-volume production are more cost-efficient on Sonnet 4.6.
For context that repeats, applying prompt caching can cut the cost of that portion by up to 90%.
If you automate a blog with AI and run tools day to day, you eventually hit one question: “Should I use the expensive model for this task, or is the cheap one enough?” Cling to a single model and one side leaks money while the other collapses on quality. This is a decision guide, not a news post. Working only from figures in Anthropic’s official docs, and from a solo-developer, solopreneur point of view, it lays out when and how to split work between Claude Opus 4.8 and Sonnet 4.6.
The Three Models’ Pricing and Specs at a Glance
The difference between the three models is not simply “expensive versus cheap.” Their per-token price, context window, maximum output length, and where each is strong all differ. The tool comparison table below reproduces Anthropic’s official specs as-is.
| Model | Input / Output (per million tokens) | Context | Max output | Recommended use |
|---|---|---|---|---|
Opus 4.8 (claude-opus-4-8) |
$5 / $25 | 1M | 128K | Autonomous agents, long-running coding, complex knowledge work |
Sonnet 4.6 (claude-sonnet-4-6) |
$3 / $15 | 1M | 64K | Balanced speed and intelligence, high-volume production |
Haiku 4.5 (claude-haiku-4-5) |
$1 / $5 | 200K | — | Simple, speed-focused work |
The output price is what most often becomes a burden here. For the same volume of writing, Opus 4.8 costs about 1.7x more in output than Sonnet 4.6. The per-piece difference may look small, but for an operator auto-publishing dozens of pieces a day and hundreds a month, the cumulative gap is large. The context window is the same 1M on both Opus 4.8 and Sonnet 4.6, so on the raw ability to handle long inputs there is no difference between them. Where they diverge is reasoning depth and output price.
When to Use Opus, When Sonnet, When Haiku
The right answer for model selection is not “use the smartest one” but “match the difficulty of the task.” Let’s split work into three tracks.
When to use Opus 4.8. Refactoring that spans multiple files, long-running agent loops that must run to the end without human intervention, and code changes where a single mistake is costly. Opus 4.8 supports effort levels from low, medium, and high up to xhigh and max, with adaptive thinking on by default, so on hard problems it plans more deeply on its own. For coding and agent work, xhigh fits best, and max is exclusive to the Opus tier. Because it does the deeper reasoning up front, in some cases it actually reduces the number of steps and the total token count.
When to use Sonnet 4.6. High-volume, uniformly difficult work such as generating blog drafts, bulk text classification, summarization, and structured extraction. Sonnet 4.6 also supports adaptive thinking and effort, but you cannot use max (max is Opus-only). Its balance of speed and intelligence makes it a good workhorse for high-volume production.
When to use Haiku 4.5. Work where speed and price matter more than intelligence — sentiment classification, short labeling, simple conversions. At $1 input and $5 output, it is the cheapest, so there is no reason to hand simple, repetitive tasks to a pricier model.
Running NABERAL as a solo business, I applied this split in practice. I routed drafting and bulk classification to Sonnet 4.6 and complex refactoring and long-running work to Opus 4.8, and got the same deliverables while noticeably lowering my monthly token cost. The cost curve is far gentler than when I handled everything with one model.
A Practical Tip — Cutting Token Cost with Prompt Caching
As important as splitting the work is the second axis of token-cost optimization: prompt caching. If you cache context that repeats — for example, a system prompt that goes in identically every time, or a large reference document — you can cut the cost of that portion by up to 90%.
The mechanism is simple. Tokens read from the cache are billed at about 0.1x the price, while tokens written to the cache are billed at about 1.25x (with a 5-minute TTL). In other words, reuse the same prefix twice or more and you come out ahead even after the write premium. The key is to put the unchanging content in front and the content that changes each request behind it. Slip the current time or a random ID into the system prompt and the prefix changes every time, invalidating the whole cache.
This is exactly where I saw the biggest effect running my blog-automation pipeline. Bundling the fixed parts — the writing instructions and tone guide — into a cached prefix dropped the input-token cost sharply when producing multiple pieces from the same template. You can confirm whether the cache is actually hitting by checking the response’s cache_read_input_tokens value; if that value is 0, it is a signal that something changing every time is hidden somewhere in the prefix.
The Cost-and-Quality Verdict for Solo Operators
To sum up, trying to handle every task with a single model is the least efficient approach. Just holding to two principles — split work by difficulty, and bundle repeating context with caching — changes your cost structure.
Opus 4.8 earns its keep with quality on hard, long tasks. Sonnet 4.6 earns it with balance on high-volume work. Haiku 4.5 earns it with price on simple work. In a solo-business setup, the most realistic structure is to keep all three models in one pipeline and route each task to the right one.
One last thing. It is safest to treat Anthropic’s official docs as your single source for pricing and specs. Model IDs, per-token prices, context windows, effort levels, and caching multipliers are all values you can confirm in the official docs; rely on figures of unclear origin or unverified claims like “the price changes starting this month,” and your cost math breaks from the start. Splitting the work on top of verified numbers is the simplest, surest way for a solo operator to keep token cost under control.
Sources: Claude docs — Models overview, Claude docs — Pricing