The short answer

GPT-5.5 vs Claude Opus 4.7 for finance in 2026 is closer than the rates suggest. Both are ~1M-context frontier models at $5/1M input. They split on output ($30 vs $25) and on levers: Opus offers up to 90% caching savings but a new tokenizer that can emit up to 35% more tokens, while both offer 50% batch. Measure your real workload, do not trust the sticker.

For finance workloads in 2026, GPT-5.5 vs Claude Opus 4.7 is closer than the headline rates suggest. Both are frontier models with roughly 1M-token context and identical $5 per million input pricing. They split on output (GPT-5.5 $30 vs Opus 4.7 $25 per million) and on two cost levers that matter more than the sticker: Opus 4.7 offers up to 90% savings via prompt caching but ships a new tokenizer that can emit up to 35% more tokens per request, while GPT-5.5 offers a flat 50% batch discount. On output-heavy finance tasks Opus is nominally cheaper per token; the tokenizer change can erase that. Price your real workload in the Token-Cost Optimizer.

TL;DR

Dimension GPT-5.5 Claude Opus 4.7
Input ($/1M) $5 $5
Output ($/1M) $30 $25
Context window ~1.05M tokens 1M tokens (no long-context surcharge)
Batch discount 50% (under 24h) 50%
Prompt caching yes up to 90% savings (cache reads ~$0.50/1M)
Tokenizer caveat none noted new tokenizer, up to 35% more tokens per request

Prices verified against official pages on 2026-05-26. GPT-5.5 launched at a 2x increase over GPT-5.4 (input $2.50 to $5, output $15 to $30).

Why finance picks between these two

Frontier-tier finance work, like multi-step research, complex extraction with reasoning, and agentic loops over filings and market data, is where the most capable models earn their cost. GPT-5.5 and Claude Opus 4.7 are the two flagship reasoning models you would reach for when a Flash-tier model is not enough. They are close on capability and identical on input price, so the decision turns on output cost, the cost levers each exposes, and one tokenizer subtlety that quietly moves the real bill.

Where they are identical, and where they split

Both list at $5 per million input tokens and both carry roughly a 1M-token context window. Opus 4.7 explicitly has no surcharge for long-context requests, which matters for a finance agent that loads large documents. Both offer a 50% batch discount for non-interactive jobs.

The headline split is output: GPT-5.5 is $30 per million output tokens, Opus 4.7 is $25. For output-heavy finance tasks like long structured extractions and verbose reasoning traces, that 17% output gap favors Opus on paper. GPT-5.5's launch was itself a 2x price increase over GPT-5.4, so the gap is a recent development, not a longstanding one.

The two levers that beat the sticker price

Per-token rates are the starting point, not the answer, because both models expose cost levers that swamp the 17% output difference:

  • Prompt caching (Opus 4.7): cache reads cost about 10% of the standard input rate (roughly $0.50 per million instead of $5) for cached system prompts, tool definitions, and reference documents. For a finance agent that reuses a long instruction block and reference filings across many calls, this is the single biggest lever, and it can dominate the comparison.
  • Batch (both): non-interactive jobs run at 50% of standard pricing. For overnight extraction or research batches that tolerate latency, this halves the bill on either model.

The tokenizer caveat that bites

Opus 4.7's nominal output advantage carries an asterisk: it ships a new tokenizer that can produce up to 35% more tokens for the same input text than Opus 4.6. On identical prompts, the per-request cost can rise 0% to 35% purely from tokenization, independent of the per-token rate. That effect can wipe out, or even invert, the 17% output-price advantage versus GPT-5.5, depending on your text. You cannot compare these two on rate cards alone; you have to measure token counts on your actual finance prompts.

The decision

  • Reuse a long system prompt and reference docs across many calls: Opus 4.7, where 90% caching savings dominate.
  • Output-heavy task, no heavy reuse, comparing on rate alone: Opus 4.7's $25 output edges GPT-5.5's $30 — but verify token counts given the tokenizer change.
  • Overnight or batch workloads on either: take the 50% batch discount.
  • You already standardize on the OpenAI ecosystem: GPT-5.5's tooling and 50% batch may net out comparably once you price your real prompts.

The honest answer is that on these two, you measure rather than assume. The 17% output gap, the 90% caching lever, and the up-to-35% tokenizer effect can each be larger than the others depending on your workload shape.

Price your real workload

Headline rates mislead here more than usual, because caching reuse and tokenization swing the effective cost by more than the sticker difference. Run your actual prompt — its reuse pattern, output length, and batch tolerance — through the Token-Cost Optimizer to get an effective cost per task for each model, and validate output accuracy on a labeled sample before committing a pipeline.

Connects to

Sources

  • OpenAI API Pricing, openai.com/api/pricing (accessed 2026-05-26).
  • Claude API Pricing, platform.claude.com/docs/en/about-claude/pricing (accessed 2026-05-26).
  • "Claude Opus 4.7 Pricing 2026," finout.io (tokenizer note, accessed 2026-05-26).

Frequently asked questions

Is Claude Opus 4.7 cheaper than GPT-5.5 for finance?
On rate cards, slightly: same $5 input, but Opus 4.7 is $25 output against GPT-5.5's $30, a 17% edge. The catch is Opus 4.7's new tokenizer, which can emit up to 35% more tokens for the same text than Opus 4.6, raising per-request cost 0% to 35% and potentially erasing that edge. Opus also offers up to 90% caching savings. Measure token counts on your real prompts first.
What is the tokenizer caveat with Opus 4.7?
It ships a new tokenizer that can produce up to 35% more tokens for the same input text than Opus 4.6. Since you pay per token, identical prompts cost 0% to 35% more, independent of the rate. So the nominal $25-vs-$30 output advantage can shrink or invert once applied to your text. Count tokens on real prompts rather than trusting the rate card.
Should I use batch mode for finance LLM jobs?
Yes, whenever latency allows. Both models run non-interactive jobs at 50% of standard pricing, typically under 24-hour turnaround. For overnight extraction or research sweeps that need no live response, batch halves the bill with no quality change. Combine batch with Opus prompt caching on reused prompts and effective cost falls well below the headline rate.