Claude Opus 5 for Data Analysis: What's New, Pricing, and Alternatives (2026)
Claude Opus 5 launched on 24 July 2026 at $5 per million input tokens and $25 per million output tokens, with a 1M-token context window and a low/medium/high effort control. For data work specifically, Box reported it beating Claude Opus 4.8 by 11% on data analysis and 17% on due diligence. It costs half what Claude Fable 5 does at a comparable score.
That combination — same context window, better measured accuracy on analysis tasks, half the price of the tier above it — is why this release matters more to people working with spreadsheets and reports than the average model launch does.
Here is what actually changed, what Claude Opus 5 for data analysis really costs, and where a better model stops being the thing that helps you.
What's new in Claude Opus 5
Four changes are worth knowing.
Price. Opus 5 sits at $5 input / $25 output per million tokens. Claude Fable 5, which scores close to it on public leaderboards, is $10 / $50. Same family, roughly the same score, half the bill.
Effort control. Opus 5 takes a low, medium, or high effort setting per request. You trade compute for accuracy on a per-call basis rather than switching models. For a batch of 500 routine categorisation calls, low effort is the right answer. For one reconciliation you will present to a board, high effort is.
Context window. The full 1M-token window is billed at standard rates. A 900k-token request costs the same per token as a 9k-token one. For anyone feeding in long financial filings or multi-sheet exports, that removes the old cliff where long context cost a premium.
Fast mode. A research-preview speed tier for Opus 5 and Opus 4.8 runs at $10 / $50, double standard, and applies across the full context window. It is not available through the Batch API.
All figures above are from Anthropic's published pricing documentation, checked 31 July 2026.
What the numbers say about data work specifically
Most launch coverage reports a general intelligence score. That is the least useful number if your job is analysis, because a model can be strong at code and mediocre at reading a messy financial table.
The figure worth quoting is the one from Box: 11% better on data analysis and 17% better on due diligence than Opus 4.8. Those are task-shaped measurements on document-heavy work, not a leaderboard position.
It is worth being precise about what an 11% improvement is and is not. It is a meaningful reduction in the rate at which the model misreads a table, drops a footnote, or misattributes a figure. It is not a guarantee that the number it hands you is right. On any analysis someone will audit, you still check the method.
Two public leaderboards also disagree about where Opus 5 lands overall, which is a useful reminder not to buy on a single score. LLM Stats has it tied with GPT-5.6 Sol at 58.0; Artificial Analysis, running at maximum effort, puts Opus 5 at 61 ahead of Sol at 59.
What it costs, in context
| Model | Input / 1M | Output / 1M | Notes |
|---|---|---|---|
| Claude Fable 5 | $10 | $50 | Top of the family |
| Claude Opus 5 | $5 | $25 | 1M context at standard rates |
| Claude Sonnet 5 | $2 | $10 | Introductory rate through 31 Aug 2026, then $3 / $15 |
| Claude Haiku 4.5 | $1 | $5 | Cheapest of the current line |
Three modifiers change the maths more than the model choice does:
- Batch API: halves both input and output. Opus 5 becomes $2.50 / $12.50.
- Prompt caching: a cache hit costs 10% of the input price. If you are asking twenty questions against the same 200-page report, the document is paid for roughly once.
- Data residency: pinning inference to the US applies a 1.1× multiplier on every token category.
For a workload that reads a lot and writes a little — long documents in, short answers out — output price barely matters and caching matters enormously. That is the shape of most analysis work, and it is the opposite of the shape most pricing comparisons assume.
Which model for which data task
The useful question is not "which model is best" but "which model is worth it for this call".
Haiku 4.5 or Sonnet 5 — cleaning, deduplication, column-type inference, categorising thousands of rows, first-pass summaries. High volume, low judgement. Sonnet 5's introductory $2 / $10 makes this bracket unusually cheap until the end of August.
Opus 5 at low or medium effort — standard analysis: read the export, find the trend, explain the outlier, draft the commentary.
Opus 5 at high effort — reconciliation across sources, due diligence reading, anything with a number in it that a board or an auditor will see. This is where the Box figures point.
Fable 5 — only when you have tested it against Opus 5 on your own task and measured a difference worth twice the price. On published scores that difference is not obvious.
Where a better model stops helping
A stronger model improves one link in the chain: how accurately the machine reads your data and reasons about it. It does not touch the links on either side.
It does not clean the four inconsistent exports sitting in your downloads folder. It does not decide which chart makes the finding legible. It does not build the deck your director opens on Monday. Those are workflow problems, and they are unchanged by a price cut on Opus 5.
This is where a data agent sits rather than a raw model. Powerdrill Bloom takes the uploaded files, cleans them, runs the analysis, recommends the charts, and converts the resulting canvas into a presentation-ready deck you can export to PowerPoint or Notion. Its paid plans support Claude Skills for research, analysis, automation, and execution workflows, so the model-layer improvements described above are a shared tailwind rather than anyone's proprietary advantage.
The honest framing: Opus 5 makes the reasoning step better for everyone who calls it. What still varies is how much of the surrounding work you have to do by hand.
Alternatives worth knowing
GPT-5.6, generally available since 9 July 2026, ships in three tiers: Sol at $5 / $30, Terra at $2.50 / $15, Luna at $1 / $6. Sol is the closest direct comparison to Opus 5, priced slightly higher on output.
Gemini 3.6 Flash, released at the end of July, is $1.50 / $7.50 with claimed token-efficiency gains over 3.5 Flash. Gemini 3.5 Flash-Lite sits at $0.30 / $2.50. For high-volume, low-judgement passes over large datasets, the Flash line is priced for exactly that job.
Kimi K3 published open weights on 27 July 2026, which matters if self-hosting is on the table. Running your own weights means no per-token bill and no built-in memory, retrieval, or reporting layer — you build those yourself.
The practical answer for most teams is not one model. It is routing: cheap models for volume, Opus 5 for judgement, and caching turned on for anything you will ask more than once.
Conclusion
If your work involves reading long documents and reasoning over tables, Opus 5 is the most consequential release of July 2026 — not because it tops a leaderboard, but because it halves the price of the previous top tier while measuring better on exactly those tasks. Turn on prompt caching, use the effort control instead of switching models, and reserve high effort for numbers someone will audit.
And keep the scope of the improvement honest. A better model reads your data better; it does not turn four messy exports into a deck. If that last mile is what actually costs you the afternoon, try Powerdrill Bloom free — the free plan includes 1,000 daily refreshed credits, and the pricing page has the full plan detail.
Frequently asked questions
How much does Claude Opus 5 cost?
Claude Opus 5 is $5 per million input tokens and $25 per million output tokens on standard pricing, as of 31 July 2026. The Batch API halves both to $2.50 and $12.50, cache hits cost 10% of the input price, and the research-preview fast mode runs at $10 / $50.
Is Claude Opus 5 good for data analysis?
The case for Claude Opus 5 for data analysis rests on task-shaped evidence rather than a leaderboard position: Box measured it as 11% better than Opus 4.8 on data analysis and 17% better on due diligence, both on document-heavy work. It also carries a 1M-token context window at standard rates, so long filings and multi-sheet exports fit in one pass.
Claude Opus 5 vs GPT-5.6: which is better for spreadsheets?
They are close and the public leaderboards disagree on the ordering. Opus 5 is $5 / $25 against GPT-5.6 Sol at $5 / $30, so Opus 5 is slightly cheaper on output-heavy work. For a decision that matters, test both on one of your own real files rather than choosing on a score.
What is the effort setting in Claude Opus 5?
Opus 5 accepts a low, medium, or high effort value per request, trading compute for accuracy without switching models. Use low for high-volume routine passes such as categorisation, and high for reconciliation or any figure that will be audited.
Do I still need a data tool if I have access to Claude Opus 5?
It depends on where your time goes. A model improves how accurately your data is read and reasoned about. It does not clean multiple inconsistent exports, choose the right chart, or assemble the deck. If those steps are the bottleneck, a data agent that handles upload through export is solving a different problem from the one a model upgrade solves.