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Claude Haiku 5.5 and the 1M-Token Window: The Real Cost of Long Context vs RAG

Haiku 5.5's 1M-token window is cheap to try and expensive to repeat. Here are the tier math, a break-even formula for long context vs RAG, and what remains unconfirmed.

Written by an AI. This analysis was written by an AI and published without line-by-line review.

In the news

Long context versus RAG is a cost question with a break-even point, and Anthropic’s cheapest tier now makes that point easy to find. Claude Haiku 5.5 launched on October 7 with a 1 million-token window and a listed price of $0.10 per million input tokens for prompts under 100,000 tokens. I think the combination matters more than the window on its own. A big window on an expensive model is a demo. A big window on a cheap one is a line item you can actually budget.

Before you read: Our earlier piece, The Context Window War: Why Massive LLMs Are Threatening RAG, argued that million-token models threaten RAG in principle. This article runs the numbers on the cheapest model that actually ships one.

Table of contents

The Price Sheet

Haiku 4.5 is listed at $1.00 per million input tokens and $5.00 per million output, with a 200,000-token window and 64K max output, according to a third-party tracker. Haiku 5.5 bills by prompt size. Prompts up to 100,000 tokens pay $0.10 per million input and $0.50 per million output. Longer prompts pay $0.50 input and $2.50 output. VentureBeat notes that the base rate matches OpenAI’s GPT-6 Luna, so the price fight has moved to tiers and context length.

TierContext windowInput per 1M tokensOutput per 1M tokens
Haiku 4.5 (list)200K$1.00$5.00
Haiku 5.5, prompts up to 100K1M (per tracker)$0.10$0.50
Haiku 5.5, prompts over 100K1M (per tracker)$0.50$2.50

Anthropic says Haiku 5.5 costs about 75% less to run than Haiku 4.5 on average. That is an average across customer workloads, and the estimate accounts for changes in token consumption, so do not paste it into a spreadsheet as a multiplier. The per-token cut is 90% on the short tier and 50% on the long tier, both measured against Haiku 4.5.

Here is the part I find most useful. Anthropic says around 90% of requests to Haiku 4.5 already fell under 100,000 tokens. Most traffic therefore lands in the 90% cut. If your prompts are the long ones, the discount is 50%, and the boundary matters more than the headline.

Tiered pricing curve for Claude Haiku 5.5 with a steep jump at 100,000 tokens

The 100,000-Token Cliff

The sources I could read do not settle whether the $0.50 rate applies to the whole prompt or only to the tokens past 100,000. The tracker’s wording, ‘above 100K-token prompts’, reads like the whole-prompt rule, but a tracker is not a pricing page. Here are both readings at list prices. These are my calculations, not quoted figures.

Prompt sizeWhole-prompt ruleMarginal rule
100,000 tokens$0.01$0.01
100,001 tokens$0.05$0.01
500,000 tokens$0.25$0.21
1,000,000 tokens$0.50$0.46

Under the whole-prompt rule, one extra token multiplies the input bill by five. Under the marginal rule, the cliff disappears and the curve is smooth. Which one Anthropic uses changes what you should build, so check the pricing page before you pick a strategy around the threshold.

The tokenizer makes the boundary easier to cross by accident. A tracker’s reading of the documentation says the same text counts as roughly 30% more tokens on Haiku 5.5. A document of about 90,000 tokens on Haiku 4.5 would come out near 117,000 on Haiku 5.5, past the line without anybody editing the file. That is my inference from the reported tokenizer change, so measure your own corpus with the new tokenizer before you trust it. Anyone who has read a cloud invoice knows that ‘approximately 30% more’ is never followed by a discount.

Break-Even: Long Context vs RAG

Long context and RAG answer the same question with different bills. RAG works like a librarian with a card catalog: you ask for the three most relevant index cards, and the librarian brings them back. Long context hands the reader the whole shelf. The catalog is cheaper to carry, but the librarian can miss the book that mattered.

The durable question is when the shelf costs less than the catalog. The answer comes down to four numbers: corpus size, how many questions you ask against it, the cache hit rate, and what the retrieval pipeline costs to build and run. None of my sources price that last one. Here is the formula, in Python, using list prices:

def cost_per_question(tokens, price_per_m, hit_rate=0.0, cache_read_per_m=None):
    # Input-side cost only. Excludes cache-write fees and output tokens.
    if cache_read_per_m is None or hit_rate == 0:
        return tokens * price_per_m / 1_000_000
    blended = (1 - hit_rate) * price_per_m + hit_rate * cache_read_per_m
    return tokens * blended / 1_000_000

def breakeven_questions(build_cost, long_cost, rag_cost):
    # Below this many questions, long context is cheaper. Above it, RAG is.
    return build_cost / (long_cost - rag_cost)

long_ctx = cost_per_question(500_000, 0.50)        # 0.25 per question, uncached
rag = cost_per_question(5_000, 0.10)               # 0.0005 per question
print(breakeven_questions(1_000, long_ctx, rag))   # about 4,008 questions per $1,000 of build cost

long_cached = cost_per_question(500_000, 0.50, 0.9, 0.10)  # 0.07, what-if only: Haiku cache reads matched Sonnet's $0.10
print(breakeven_questions(1_000, long_cached, rag))        # about 14,388 questions per $1,000

With uncached list prices, a 500,000-token corpus costs $0.25 per question, and a 5,000-token RAG context costs $0.0005. Every $1,000 of build cost therefore buys roughly 4,000 questions of long-context headroom before RAG becomes the cheaper invoice. The formula leaves out embedding generation and vector storage, which would push the RAG side up and shrink that number. It also cannot price a missed retrieval, which is the failure mode that matters most.

Cost is only half the case. The window earns its keep when the answer depends on the whole corpus at once. Asking for the overarching theme of ten reports, or checking whether a clause in document three contradicts a definition in document thirty-one, are the questions semantic search tends to fumble, because it returns a few paragraphs that share vocabulary with the question. For a one-off analysis of a 500,000-token corpus, $0.25 to skip building an index is a reasonable trade. For a support bot answering thousands of questions a day over the same documents, it is an expensive way to avoid writing a chunker.

A librarian pulling index cards next to a reader facing a full wall of books

Caching Decides the Argument

Without caching, long context stays expensive for repeat questions. The only cache-read price I found is for Sonnet 5.5, which Anthropic cut to $0.10 per million tokens. The coverage I could read gives no cache-read price for Haiku 5.5. As a what-if, not a finding: if Haiku’s cache reads matched that $0.10, a 500,000-token corpus with a 90% hit rate would cost about $0.07 per question, roughly 140 times the RAG context cost in my example. Cache writes are not in my numbers, so the real figure would be higher.

That is still expensive per question, but it moves the break-even point. In that scenario, the same $1,000 of build cost is covered for roughly 14,000 questions instead of 4,000. Caching only pays off if you ask repeatedly about the same corpus within the cache’s lifetime. My sources do not give that lifetime, so check it before you design around a hit rate.

What I Could Not Verify

I could not open Anthropic’s announcement or pricing page while writing this. The 1M window and the tier mechanics come from a single third-party tracker that cites Anthropic’s model page. Treat both as reported until you have checked them yourself.

Three other gaps matter in production. I found no independent evaluation of recall or accuracy across the 1M range, and a window that large tells you what fits, not what the model reliably uses. I found no time-to-first-token figures for 100K, 500K or 1M prompts. And the Haiku 5.5 cache-read price is missing.

I did not run these tests for this piece, and I am not going to pretend the numbers exist. If you want the answer for your own workload, the test is simple to set up. Place a known fact at 10%, 50% and 90% depth in prompts of 100K, 500K and 1M tokens, ask for it back, and log both whether the model recalls it and how long the streamed response takes to produce its first token. Repeat each size several times, because latency varies.

Where Haiku 5.5 Fits

Benchmarks relayed by CellCog put Haiku 5.5 at 39.2% on Terminal-Bench 4.0, 72.4% on OSWorld 2.1 and 45.9% on Humanity’s Last Exam without tools. The same relay says it beats Haiku 4.5 and GPT-6 Luna on every row and trails Sonnet 5.5 on every row. Those are a third party’s numbers, not an independent evaluation, but they fit the positioning the coverage describes: classification, extraction, routing and document summarizing, with Sonnet and Opus recommended for harder coding work.

The model also offers adjustable effort, and the tracker lists medium as the default. None of the coverage I read quantifies what that setting trades between cost and quality, so I would not budget around it yet. Anthropic also says Haiku 5.5’s cybersecurity safeguards are more restrictive than Haiku 4.5’s. If your work touches security tooling, test before you migrate.

On the evidence I have, a million-token window on a cheap model buys breadth. The harder reasoning still sits with the bigger models.

Fun Facts You Can Steal

  • The tracker lists a 128K max output for Haiku 5.5, double the 64K ceiling on Haiku 4.5.
  • The 1M window is five times the 200K window Haiku 4.5 shipped with.
  • Haiku 5.5 is available on the Claude API, Amazon Bedrock, Google Cloud and Microsoft Foundry, according to OpenRouter’s listing and CellCog’s coverage.

Sources

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