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Kyeo AI
AI conversation model
Anthropic
Claude

Claude Haiku 4.5

Claude Haiku 4.5 is a text-only Text chat entry on this site. Each new message is limited to 20,000 characters and a request includes only the 40 most recently submitted messages; images, web search, reasoning/thinking, and tool configuration are not available.

Use this modelView credit pricingBack to Model Hub
Capability
Text chat
Credit cost
Input 55 / output 285 / million tokens
Prompt limit
20,000 characters
Upload limit
No attachments
30-second overview
  • Processes text with a 4,096-token output cap per response
  • Does not expose images, search, tools, thinking, or effort
  • Base rates are 55 input and 285 output credits per million tokens, with 0.1× cache reads and 1.25×/2× cache writes.

Popular searches: How do I use Claude Haiku 4.5? · What is the current price of Claude Haiku 4.5? · Does Claude Haiku 4.5 offer low latency?

Examples

Puzzle Exchange: Opening day coordination

Plan a 90-minute Sunday opening for Puzzle Exchange at the reading room. We have 70 puzzle labels and groups of 14.
Use 5 groups with one coordinator per group. 00:00–00:10: welcome and explain the activity. 00:10–00:20: distribute 14 puzzle labels to each group. 00:20–01:05: run the main activity. 01:05–01:20: collect feedback and reusable equipment. 01:20–01:30: clean the reading room and check the inventory.
What should the coordinator check first?
Count the 70 puzzle labels before visitors arrive. Confirm a clear entrance and an accessible work area at the reading room, then give each of the 5 group leads the same timing sheet.

Puzzle Exchange: Supply budget calculation

Puzzle Exchange needs 70 puzzle labels at $12.40 each. Add a 10% reserve. Show the arithmetic.
Supplies: 70 × $12.40 = $868.00. Reserve: $868.00 × 0.10 = $86.80. Planned total: $954.80. This is a planning calculation using the supplied prices.
What if we reduce the quantity by one group?
Remove 14 units, leaving 56. Supplies become $694.40; a 10% reserve adds $69.44, for a total of $763.84.

Puzzle Exchange: Visitor announcement

Write a warm announcement for Puzzle Exchange, meeting on Sunday at the reading room. Mention limited puzzle labels, without promising availability.
Join Puzzle Exchange this Sunday at the reading room. Meet neighbors, learn together, and take part at your own pace. We will share the available puzzle labels across small groups. Supplies are limited, so please check with the welcome desk when you arrive.
Shorten it to an event sign.
Puzzle Exchange Sunday · reading room Welcome! Ask the desk about available puzzle labels and today’s small-group activities.

Puzzle Exchange: Inventory CSV validation

Write Python to validate Puzzle Exchange inventory CSV with columns item,quantity. Quantity must be a nonnegative integer.
```python import csv def read_inventory(path): rows = [] with open(path, newline="", encoding="utf-8") as handle: reader = csv.DictReader(handle) if reader.fieldnames != ["item", "quantity"]: raise ValueError("Expected item,quantity columns") for line, row in enumerate(reader, 2): name = row["item"].strip() quantity = row["quantity"].strip() if not name or not quantity.isascii() or not quantity.isdecimal(): raise ValueError(f"Invalid inventory row {line}") rows.append((name, int(quantity))) return rows # Example row: puzzle labels,70 ```
What are two useful validation cases?
The row `puzzle labels,70` should pass. The row `puzzle labels,-1` should raise a ValueError because a negative quantity is outside the stated inventory contract. An empty item should also fail.

Puzzle Exchange: Coordinator handover note

Create a concise handover note for Puzzle Exchange: 70 puzzle labels, 5 groups, meeting Sunday at reading room.
Handover — Puzzle Exchange Location: reading room; day: Sunday. Inventory: 70 puzzle labels, allocated as 5 groups × 14. Before opening: count supplies, set the welcome desk, and confirm group leads. During the event: record shortages and visitor questions. After closing: count returns and write one improvement for the next meeting.
Add a useful open question for the next lead.
Ask: “Were 14 puzzle labels per group enough for the activity, and which step created the longest wait?” Record the answer beside the returned inventory count.

Puzzle Exchange: Participant question response

A visitor asks whether they can join Puzzle Exchange halfway through the Sunday session at reading room. Draft a welcoming but practical reply.
You are welcome to check in at the desk when you arrive. A coordinator can explain the current activity and see whether a group has space and spare puzzle labels. If the activity is already full, we can help you observe or find the next joining point.
What should we avoid saying?
Avoid guaranteeing a place or a set of puzzle labels before checking the group capacity. Keep the reply specific to the reading room session and offer a clear next step: speak with the welcome desk.

At a glance

Is this model right for you?

Use representative non-sensitive samples to evaluate quality, truncation, role behavior, actual token use, cache use, latency, and privacy. This page describes Kyeo AI's current product limits, not every feature available from the model vendor.

What it does best
Claude Haiku 4.5 is Kyeo AI's fast Claude AI chat option for quick reading, lightweight rewrites, inbox triage, and low-cost everyday assistant work.
Who it is for
Redacted fixed-sample evaluation of summarization, rewriting, classification, basic Q&A, and local code explanation; not unsupervised customer replies or high-risk decisions.
Why use it on Kyeo AI
The current page deliberately focuses on text chat and exposes a clear 20,000-character input limit, 40-message history, and 4,096-token output cap.

Key facts

Quickly evaluate whether this model fits your business use case.

Category
AI conversation model
Vendor
Anthropic
Model family
Claude
Output limit
4,096 tokens per response
Input type
Text only
Conversation history
Up to 40 submitted messages
Prompt limit
20,000 characters
Upload limit
No attachments

Other names

The same model may have different names in official documentation, community discussions, and API integrations. This section brings them together for you.

Claude Haiku 4 5
claude-haiku-4-5
Anthropic Claude

Common questions

These practical questions focus on the task, input conditions, and result requirements you should confirm before choosing.

How do I use Claude Haiku 4.5?
What is the current price of Claude Haiku 4.5?
Does Claude Haiku 4.5 offer low latency?
How does Claude Haiku 4.5 differ from Claude Sonnet 4.5?
How does Claude Haiku 4.5 differ from GPT-5.2?
Does Claude Haiku 4.5 currently support images?
Does Claude Haiku 4.5 currently support web search?
Does Claude Haiku 4.5 currently support tool calls?
Is Claude Haiku 4.5 suitable for customer Q&A?
Is Claude Haiku 4.5 suitable as the default lightweight Claude model?

Decision guide

Still deciding? Start with these key decision points.

1
When evaluating frequent lightweight tasks

Use a fixed set of summarization, classification, rewriting, and short-code samples. Record first-pass acceptance, latency, tokens, and credits before deciding whether to use it in bulk.

2
What can I set up on this page?

This page is set up as the public chat entry for Claude Haiku 4.5. That keeps the first result centered on inbox triage and quick internal Q&A instead of forcing the work into a longer thread too early.

3
Before customer-facing automation

Build source controls, refusal rules, human escalation, sensitive-data handling, injection defenses, and sampled QA. Fluent output is not a customer-service or compliance process.

Popular comparisons

Compare common alternatives on the same task to clarify differences in inputs, controls, and cost.

How does Claude Haiku 4.5 compare with Claude Sonnet 4.5?
Claude Haiku 4.5 vs Claude Sonnet 4.5
How does Claude Haiku 4.5 compare with Claude Sonnet 4.6?
Claude Haiku 4.5 vs Claude Sonnet 4.6
How does Claude Haiku 4.5 compare with GPT-5.2?
Claude Haiku 4.5 vs GPT-5.2

Model comparison table

Not sure which model to choose? Compare the key differences between this model and its alternatives at a glance.

Role on Kyeo AI
Claude Haiku 4.5
Lightweight Claude entry point
Claude Sonnet 4.5
Legacy Sonnet compatibility baseline
Claude Sonnet 4.6
Intermediate-generation migration baseline
Visible capabilities
Claude Haiku 4.5
Text-only Claude workspace
Claude Sonnet 4.5
Text only, legacy Sonnet comparison tier
Claude Sonnet 4.6
Text only, intermediate-generation comparison tier
Best fit
Claude Haiku 4.5
Frequent Q&A, lightweight analysis, and quick code explanation
Claude Sonnet 4.5
Regression testing and migration comparisons for existing Sonnet 4.5 tasks
Claude Sonnet 4.6
Regression testing and migration comparisons for existing Sonnet 4.6 tasks

Real-world experience

Practical guidance based on public sources, current on-site limits, and representative tasks.

Why Claude is often considered for writing and analysis

Searchers often ask how Claude differs from GPT or Gemini for long-document summaries, complex writing, and research rather than simply asking what Claude is. These queries reflect how the model is actually used.

Benchmarks and real-world work measure different things

Benchmark sites focus on intelligence, latency, and price, while content teams care more about consistent long-form output, stylistic consistency, and whether a workflow can be standardized as a template.

Capabilities

Bounded text chat

The page accepts text only, includes at most 40 submitted messages, and caps each response at 4,096 output tokens.

Settlement from actual usage

Successful responses record base input, output, cache creation, cache read, and cache-duration usage, then settle the final charge from the reported request total.

Feature scope stays explicit

Images, web search, tools, and advanced reasoning controls are not available on this page. Use the visible controls and limits as the product contract.

Use cases

Summarization, rewriting, and classification evaluation

Suitable for well-specified text tasks with human sampling; launch decisions must use measured pass rate.

Local code explanation

Explain snippets and suggest test ideas; this cannot replace running the code, security review, dependency checks, or production validation.

Supervised response drafts

Create reply drafts for human review; do not make unreviewed factual, policy, refund, medical, legal, or financial commitments.

Prompt tips

Put evidence in the current message

There is no web search. Supply the required material and request evidence-bound answers, then check omissions and misreadings.

Put critical role constraints in the user message

The current workflow converts system-like roles into user text.

Track rework with fixed samples

Measure accuracy, human editing time, failed retries, tokens, and credits together—not just one response speed or list price.

Why choose it

The current input boundary is explicitly text-only.
Supports up to 20,000 characters per message.
At most 40 submitted messages are included.
Successful responses record input and output token usage.

What to know first

This page does not claim a 1M-token context or 128K-token output; each response is capped at 4,096 output tokens.
Images, search, tools, adaptive thinking, and effort controls are not available.
Start with one representative sample using the selected settings, confirm that the result meets the objective, and only then scale up.
Before commercial use, test with your actual prompt, actual material, and actual delivery standards.

Adjustable settings

Quickly evaluate whether this model fits your business use case.

No additional settings are available for this model.

Credit cost

Token-based pricing

55 credits per million base input tokens and 285 per million output tokens; cache reads use 0.1× the input rate, while 5-minute and 1-hour cache writes use 1.25× and 2×.

Credit-saving tip

Before generating images or videos in bulk, run an A/B test with the same input on this model and similar alternatives. Confirm the result first to avoid wasting credits.

Frequently asked questions

Related models

Still deciding? Take a quick look at these similar alternatives.

Claude Sonnet 4.5

Kyeo AI offers Claude Sonnet 4.5 as a text-only chat model for demanding analysis and writing, with up to 20,000 characters per message, 40 submitted messages of history, and at most 4,096 output tokens per response.

AI conversation model
Token-based pricing
View detailsUse now

Claude Sonnet 4.6

Claude Sonnet 4.6 is a text-only migration baseline on Kyeo AI between Sonnet 4.5 and Sonnet 5. It is no longer Anthropic's newest Sonnet, but remains useful for rerunning established 4.6 workflows or comparing 4.5, 4.6, and newer Kyeo AI models on the same tasks. This implementation is narrower than the official platform: each new message is limited to 20,000 JavaScript characters, only the 40 most recent messages are included, and attachments, web search, thinking, and tool execution are not exposed.

AI conversation model
Token-based pricing
View detailsUse now

Claude Haiku 5.5

Use text conversations to organize ideas, draft content, or discuss questions.

AI conversation model
See the estimate for your current settings
View detailsUse now

Sources

This page is based on vendor materials, model-family documentation, and Kyeo AI integration settings.

Source note

This page describes the current Kyeo AI experience: text-only chat, up to 20,000 characters per message, the 40 most recently submitted messages, and at most 4,096 output tokens per response. Pricing and feature claims below are limited to controls that are publicly available on this page.

Last updated: 2026-08-26
Anthropic: Claude Haiku 4.5
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