Claude Haiku 5.5 vs Opus 5.5: Pricing & API Guide

By Beat API Team · Prices and documentation checked October 9, 2026

Start with Haiku 5.5 for bounded, verifiable tasks. Start with Opus 5.5 when the task requires sustained reasoning across files, tools, or conflicting evidence. For applications containing both kinds of work, evaluate a Haiku-first route with explicit acceptance checks and an Opus escalation path.

The Claude Haiku 5.5 vs Opus 5.5 comparison turns on pricing, cache behavior, and the cost of completing your task:

• Haiku's official uncached input/output rates are 40 times lower for prompts up to 100,000 tokens, but only eight times lower above that boundary.
• Cache reads count toward that boundary. A small new message attached to a large cached conversation still belongs to the long-prompt tier.
• Identical context limits do not imply identical ability to complete a task—or identical bills.
• Measure cost per accepted result, including retries and escalation. A cheap first attempt can leave expensive rework.
• API migration includes request parameters and response parsing; changing the model ID alone is insufficient.

Disclosure: We build BeatAPI, which lists both models. This article separates Anthropic's published specifications, BeatAPI's dated public retail listing, and our own arithmetic and implementation recommendations. We have not run a controlled Haiku-versus-Opus quality or latency benchmark for this article.

Claude Haiku 5.5 vs Opus 5.5: what actually differs?

Both models accept text and image inputs and return text. Both advertise a 1M-token context window and a normal maximum output of 128K tokens. Haiku 5.5 was released on October 7; Opus 5.5 on September 22. Haiku is positioned for classification, extraction, routing, and subagent work; Opus for long-running coding and knowledge work. These are model specifications, rather than guarantees for every gateway, account, or integration. See the Haiku overview and Opus overview.

That distinction suggests a useful first split:

Workload	Initial candidate	Acceptance check	Escalation trigger
Classify a support ticket	Haiku	Allowed label, explicit intent, known exceptions	Multiple intents or policy ambiguity
Extract a field from a document	Haiku	Value exists in a cited source span	Conflicting records or missing evidence
Find likely files for a bug	Haiku	Existing paths and relevant symbols	Scout cannot justify the shortlist
Fix a multi-file regression	Opus	Reproduction, regression tests, diff review	Review or test failure
Reconcile conflicting documents	Opus	Traceable claims and resolved contradictions	Evidence remains incomplete
Write a summary for a recurring report	Haiku for extraction; evaluate Opus for synthesis	Source coverage and figure checks	Cross-document inference or high rework rate

This is a proposed routing policy. A simple task with unusual domain rules can still be difficult, while a large document with one exact lookup may be easy. Start with representative examples from your application before assigning a whole category to either model.

Haiku 5.5 pricing vs Opus: input, output, and cache costs

The following are Anthropic standard API prices in USD per million tokens, excluding Fast mode, Batch discounts, geography modifiers, and separately billed tools.

Token category	Haiku: prompt ≤100K	Haiku: prompt >100K	Opus: standard
New input	$0.10	$0.50	$4.00
Output	$0.50	$2.50	$20.00
Cache read	$0.01	$0.05	$0.20
5-minute cache write	$0.125	$0.625	$5.00
1-hour cache write	$0.20	$1.00	$8.00

For uncached input and output, Opus/Haiku is 40× below or at 100K and 8× above it. For cache reads alone, those ratios become 20× and 4×. A workload mixing these buckets has its own ratio. Rates and the counting rule come from Anthropic's pricing documentation.

The boundary applies to the whole request, rather than a marginal surcharge on tokens after 100K. Prompt length includes new input, cache reads, and cache writes. Output does not determine prompt length, but its rate changes when the prompt crosses the boundary. Earlier requests retain their original prices.

This matters when a conversation grows gradually. One additional tool result can move the next request into a different tier even if most of its context is cached.

BeatAPI's separately verified retail listing

On October 9, the anonymous BeatAPI pricing endpoint listed these rates for the exact IDs claude-haiku-5-5 and claude-opus-5-5:

Token category	BeatAPI Haiku: standard	BeatAPI Haiku: long_context	BeatAPI Opus
New input	$0.05	$0.25	$2.00
Output	$0.25	$1.25	$10.00
Cache read	$0.005	$0.025	$0.10
5-minute cache write	$0.0625	$0.3125	$2.50
1-hour cache write	$0.10	$0.50	$4.00

These published rates are half the corresponding Anthropic standard rates. The listing verifies a public price book; it does not independently establish output equivalence, uptime, end-to-end latency, or support for every Anthropic feature. The endpoint exposes both Haiku tiers; the 100K rule described above is independently documented by Anthropic. Boundary settlement through BeatAPI has not been tested here. Recheck current pricing before budgeting.

For an implementation check, the Claude Haiku 5.5 API page and Claude Opus 5.5 API page on BeatAPI bring the model IDs, current token rates, and request examples together. Use those examples to test one representative task before adopting the routing policy below.

Five workloads you can calculate before calling either model

For disjoint token buckets, a token-only estimate is:

cost = (new_input × input_rate
      + cache_read × read_rate
      + cache_write_5m × write_5m_rate
      + cache_write_1h × write_1h_rate
      + output × output_rate) / 1,000,000

Do not add cached tokens to both new input and cache read. For the examples below, output means the full billed output token count, including reasoning where applicable, rather than just the visible answer. The token counts are assumed identical between models to isolate pricing; real model runs will usually differ.

Assumed request	Anthropic Haiku	Anthropic Opus	BeatAPI Haiku	BeatAPI Opus
2K new input + 500 output	$0.00045	$0.018	$0.000225	$0.009
90K cache read + 10K new input + 2K output	$0.0029	$0.098	$0.00145	$0.049
190K cache read + 10K new input + 2K output	$0.0195	$0.118	$0.00975	$0.059
100,000 new input + 2K output	$0.011	$0.440	$0.0055	$0.220
100,001 new input + 2K output	$0.0550005	$0.440004	$0.02750025	$0.220002

These are illustrative calculations, not observed invoices or benchmark results. Cache-hit examples exclude the earlier cost of creating the cache. BeatAPI columns apply its published tier rates with Anthropic's documented boundary as the budgeting assumption.

Two consequences are easy to overlook:

First, the cache-heavy 200K example makes Opus about 6.05× as expensive, rather than 40×. Most of the prompt is cheap cache-read traffic, while Haiku has crossed into its higher tier.

Second, at this output length, increasing an uncached Haiku prompt from 100,000 to 100,001 tokens increases the estimated request cost roughly fivefold. Opus's estimate changes only by the additional input token. This is why token counting belongs before routing, especially near the boundary.

For the small-request example, one million requests would cost $450 on Anthropic Haiku or $18,000 on Anthropic Opus. BeatAPI's listed rates imply $225 or $9,000. Those totals assume one attempt per request, unchanged token usage, no tools, and no other charges. They are planning scenarios, not promised savings.

When is compaction worth paying for?

Suppose the next ten requests each read 190K cached tokens, add 10K new tokens, and generate 2K output tokens. At Anthropic rates, Haiku costs 10 × $0.0195 = $0.195.

If a validated summary lets each request read 90K cached tokens instead, the ten requests cost 10 × $0.0029 = $0.029. That leaves $0.166 for creating the summary and writing its replacement cache before the token-only saving disappears. At BeatAPI's listed rates, the corresponding allowance is $0.083.

For example, writing a 90K replacement cache at Haiku's five-minute standard rate costs $0.01125 at Anthropic rates. The cost of generating the summary is additional. You also need to account for cache expiration and misses.

The important qualification is semantic: a cheaper summary that loses the one detail needed later can increase retries or produce a wrong answer. Preserve source references and measure downstream acceptance. Compaction is an optimization to evaluate, rather than a universal instruction to shorten everything.

What the published benchmarks can—and cannot—tell you

Anthropic reports 39.2% for Haiku 5.5 and 66.4% for Opus 5.5 on Terminal-Bench 4.0, and 46.4% versus 54.4% on FrontierCode 1.1 Main. The reported GDPval-AA v2.1 scores are 1620 and 1846, respectively. Sources: the Haiku announcement and Opus announcement.

These published results help choose what to test first. They do not provide your application's success probability. The Opus announcement specifies xhigh effort for Terminal-Bench and generally max effort for other reported results, and describes safeguard fallbacks in some evaluations. These figures are not a controlled comparison at equal effort, equal cost, or through BeatAPI. Avoid turning them into claims about a specific production route.

Some apparently comparable visual scores also use different tool conditions or subsets. A result with tools and a result without tools should not be presented as a clean model-only ranking. Likewise, Elo scores are not percentages: a higher GDPval score does not imply a proportional increase in accepted tasks.

A useful evaluation separates three questions:

1. Capability: Can the model complete the task under the allowed tools and context?
2. Economics: What does an accepted result cost with the actual request sequence?
3. Product experience: Does it meet the latency and correction burden users can tolerate?

Keep these measurements separate. The least expensive accepted answer may still arrive too late for an interactive workflow.

A Haiku-first route needs a rejection policy

A practical candidate architecture is:

Task → explicit complexity rules
       ├─ known complex task → Opus → acceptance check
       └─ bounded task → Haiku → deterministic checks
                              ├─ accepted → return
                              └─ unresolved → Opus with source evidence

Use observable evidence for the gate. For extraction, require a source span and validate the requested field. For code, run the reproduction and regression tests. For classification, check the allowed categories and evaluate difficult labeled examples. A model saying “I am confident” is not an acceptance test.

Do not let Opus see only Haiku's conclusion when escalating a disputed result. Pass the original source, the relevant excerpt, and the failed check; identify Haiku's draft as an unverified candidate. Otherwise, escalation can amplify the first model's mistake.

The escalation break-even formula

Let H be average Haiku cost per incoming task, Oe the Opus cost when escalated, Od the cost of sending that task directly to Opus, V validation overhead, and p the escalation fraction. A one-attempt Haiku route costs:

expected_cost = H + V + p × Oe

It is cheaper than direct Opus when:

p < (Od - H - V) / Oe

If escalation and direct Opus cost the same and validation is free, this simplifies to p < 1 - H/Od.

With the small-request assumptions above, H = $0.00045 and Oe = Od = $0.018. If 20% escalate, the result is $0.00405 per incoming task, 77.5% below sending every task directly to Opus. At BeatAPI's listed rates it is $0.002025 versus $0.009, with the same relative saving under identical assumptions.

This is a sensitivity example, not a measured 20% escalation rate. If Opus needs to reread more context or correct a damaged draft, Oe may exceed Od. Add all failed attempts, validator calls, tools, and review time before claiming a production saving.

Also measure false acceptance: how often an incorrect Haiku result bypasses escalation. Lower escalation is not an improvement if the gate silently returns more wrong answers.

Migration traps that affect the comparison

Haiku 5.5's migration guide documents changes beyond the model ID:

Old assumption	Adjustment
Token counts measured on Haiku 4.5 still apply	Recount using the new model; the tokenizer can produce approximately 30% more tokens for the same text
Fixed thinking.budget_tokens controls reasoning	Use adaptive thinking and output_config.effort
temperature=0 makes extraction predictable	Remove sampling parameters; use explicit requirements and validation
content[0] is always the answer	Collect blocks whose type is text
A tiny max_tokens cap is sufficient	Reasoning can consume the cap before visible text appears
Assistant prefill enforces JSON	Replace prefill with a supported output mechanism and validate the result

Opus has its own differences: adaptive thinking cannot be disabled and forced tool use returns an error. A shared router should maintain model-specific capability settings, rather than passing every Haiku request option unchanged to Opus. See the Opus overview.

A minimal two-model request

BeatAPI's public gateway contract exposes POST /v1/messages. This example uses that route and asks both models the same bounded question. It is an integration example; authenticated execution and model-specific parameter forwarding have not been tested for this article. Confirm them with a small canary before using the code in production.

export BEATAPI_API_KEY='YOUR_API_KEY'
# Save the Python example below as compare.py, then:
python3 compare.py

import json
import os
import time
import urllib.request

key = os.environ["BEATAPI_API_KEY"]
prompt = """Classify this ticket as billing, bug, or feature.
Return only a JSON object with label and a short evidence quote.
Ticket: I was charged twice for the same invoice. Can you refund one charge?
Do not take any action on the account."""

for model in ("claude-haiku-5-5", "claude-opus-5-5"):
    body = {
        "model": model,
        "max_tokens": 4096,
        "output_config": {"effort": "low"},
        "messages": [{"role": "user", "content": prompt}],
    }
    request = urllib.request.Request(
        "https://api.beatapi.io/v1/messages",
        data=json.dumps(body).encode(),
        headers={
            "Authorization": "Bearer " + key,
            "Content-Type": "application/json",
            "anthropic-version": "2023-06-01",
        },
        method="POST",
    )
    start = time.monotonic()
    with urllib.request.urlopen(request, timeout=90) as response:
        result = json.load(response)
    text = "".join(
        block.get("text", "")
        for block in result.get("content", [])
        if block.get("type") == "text"
    )
    print(json.dumps({
        "model": model,
        "elapsed_seconds": round(time.monotonic() - start, 3),
        "stop_reason": result.get("stop_reason"),
        "usage": result.get("usage"),
        "answer": text,
    }, ensure_ascii=False))

This makes two billable requests when run. It records total non-streaming request duration, not time to first token. It does not guarantee valid JSON, implement retries, or form a benchmark. Validate the returned object and inspect stop reasons before treating the result as accepted. Avoid logging sensitive tickets or credentials in production.

Start a new, source-based request when escalating between models. Cross-model or cross-account replay of opaque thinking blocks is not a safe generic routing strategy; preserve those blocks only as required by the original conversation's API rules.

How to run an evaluation that answers your routing question

Use a held-out set with ordinary cases and deliberately hard cases. A pilot of 50–100 labeled examples can reveal integration and rubric problems; it is too small to establish a low production error rate.

Measurement	What to retain	Why it changes the decision
Accepted-result rate	Expected result, rubric, observed output	Distinguishes useful answers from plausible prose
False acceptance	Gate decision and independent correctness label	Finds errors that escalation never sees
Cost per accepted result	Every attempt's usage and applicable rates	Includes failures and rework
End-to-end latency	Start, finish, tool and review time	Captures the actual user wait
Escalation fraction	Route and rejection reason	Tests the routing cost formula
Cache behavior	Writes, reads, misses, total prompt length	Separates warm-cache estimates from real traffic

Keep prompts, tools, input snapshots, output limits, and grader rules fixed. Alternate model order to reduce time-of-day bias. Run a matched-effort comparison first, then allow each model a tuned configuration: those answer different questions. Record all attempts, rather than keeping only the nicest response.

For a three-route experiment—Haiku only, Opus only, and Haiku with escalation—evaluate the same task IDs. The routing experiment must grade final delivered answers and record both model calls where escalation occurs. Do not compare a routed system's final success rate with a single model's first-attempt success rate without labeling that difference.

For a business-facing metric, calculate:

cost_per_accepted_result =
    (all inference + tools + validators + review cost) / accepted_results

If no result is accepted, report the metric as undefined rather than zero. Rejected tasks still contributed cost. Report the acceptance rate beside this metric so a route cannot appear efficient simply by dropping hard cases.

Questions worth answering before switching

Is Haiku 5.5 a replacement for Opus 5.5?

It is a candidate replacement for particular task classes once they meet your acceptance bar. A file scout and a migration planner can belong in the same application while requiring different models.

Is Haiku always 40 times cheaper?

No. That ratio applies to equal uncached input/output token counts at standard rates with prompts up to 100K. Long-prompt rates, caching mixes, generated reasoning, and retries change the comparison.

Does caching keep Haiku under the 100K boundary?

No. Cached input still contributes to prompt length. Count the complete prompt before choosing a tier.

Does a 1M context window mean I should send the whole repository?

No. It is capacity, not a retrieval strategy. Test whether a relevant slice preserves acceptance while reducing cost and latency. Keep source references available for follow-up.

Can I compare API bills with a Claude subscription?

Not directly. This article models per-token API rates. Subscription allowances, usage accounting, and user workflows are a separate comparison.

Is Haiku faster in my application?

Anthropic positions it as its fastest standard-speed model. That does not predict latency on your route or workload. Measure end-to-end completion and time to first token separately, including tools and escalation.

What should I deploy first?

Choose one bounded, labeled workload. Check the model ID and request shape, capture actual usage, validate results, and compare all three routes. Expand only after the candidate route meets your correctness and latency requirements.

The practical decision in Claude Haiku 5.5 vs Opus 5.5 is where to spend reasoning. Give narrow, checkable work to the inexpensive candidate; spend more on unresolved complexity; and make the acceptance check strong enough to know the difference.