Model comparison

Claude Haiku 5.5 vs LLM-jp-3.1 8x13B instruct4

Pricing, context window and real answers (October 2026)

Claude Haiku 5.5 (Anthropic) and LLM-jp-3.1 8x13B instruct4 (LLM-jp), compared as you can actually call them on FastMetal. Both are served from the same OpenAI-compatible endpoint and API key; switching is a change to the model string.

Specs and pricing

anthropic logoClaude Haiku 5.5LLM-jp-3.1 8x13B instruct4
ProviderAnthropicLLM-jp
Input (per 1M tokens)$0.1055$0.102
Output (per 1M tokens)$0.5275$0.5035
Typical cost (1,000 in + 500 out tokens × 1,000 calls)$0.3693$0.3537
Context window1,000,000 tokens4,096 tokens
Release date10/7/20263/21/2026
Input modalitiestext, image, filetext
Throughput (p50)—125 tokens/s (n=24)
Arena · overallUnrankedUnranked
Arena · JapaneseUnrankedUnranked
Arena · codingUnrankedUnranked

Prices are FastMetal's rates, before tax.

Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-02; category names mapped and ratings rounded by FastMetal.

Which should you pick?

  • On price, LLM-jp-3.1 8x13B instruct4: $0.5035 per 1M output tokens, 5% less than Claude Haiku 5.5.
  • For long documents, Claude Haiku 5.5: a 1,000,000-token context window against 4,096 for LLM-jp-3.1 8x13B instruct4.
  • If in doubt, try both on the same key. Switching is a change to the model string, and each is billed at its own rate.

Real answers to the same prompts

Exactly what the FastMetal gateway returned, side by side. Not benchmark scores: actual output.

Count the number of 'r's in 'strawberry'

Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

Claude Haiku 5.5

There are **3** r's in "strawberry." 1. **Spell out the word:** s, t, r, a, w, b, e, r, r, y 2. **Go through each letter and count the r's:** - Position 3: **r** (count = 1) - Position 8: **r** (count = 2) - Position 9: **r** (count = 3) 3. **Check the rest:** s, t, a, w, b, e, and y are not r's. Total: **3**
2.4s200 output tokens

LLM-jp-3.1 8x13B instruct4

To count the number of 'r's in the word 'strawberry', follow these steps: 1. Identify the word: The word we are examining is 'strawberry'. 2. Understand the task: We need to find and count all occurrences of the letter 'r' in this word. 3. Begin with the first letter: The first letter is 's', which is not 'r', so we move on to the next letter. 4. Proceed to the second letter: The second letter is 't', which is also not 'r', so we continue to the third letter. 5. Reach the third letter: The third letter is 'r'. We have found one 'r'. 6. Continue to the fourth letter: The fourth letter is 'a', which is not 'r', so we move on to the fifth letter. 7. Examine the fifth letter: The fifth letter i…
4.0s400 output tokens

Debug This Error

I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…

Claude Haiku 5.5

The error means `users` is `undefined` when `UserList` renders, so `.map` is called on nothing. The component destructures the `users` prop, but the parent didn't pass it, or passed `undefined`. ## Common causes **1. The parent doesn't pass the prop, or uses a different name** ```jsx <UserList /> // users is undefined <UserList user={users} /> // wrong name: "user" instead of "users" ``` **2. The data hasn't loaded yet (most common with fetched data)** ```jsx function App() { const [users, setUsers] = useState(); // undefined initially useEffect(() => { fetch('/api/users').then(r => r.json()).then(setUsers); }, []); return <UserList users={users} />;…
5.1s888 output tokens

LLM-jp-3.1 8x13B instruct4

The error TypeError: Cannot read properties of undefined (reading 'map') suggests that the 'users' argument passed to the UserList component is undefined or does not have a 'map' method. The cause of the problem may be that the users variable is not being passed correctly or is not defined in the parent component. Check the props being passed to the UserList component to ensure that they are defined and contain the expected data. To fix the issue, you can add some error handling and debugging to your component. Try logging the users variable to the console to verify that it is not undefined: ```jsx function UserList({ users }) { console.log("Users:", users); if (!users) { return <…
3.0s279 output tokens

Code Review

Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…

Claude Haiku 5.5

## Issues **Correctness and robustness** 1. **Bare `except:`** catches everything, including `KeyboardInterrupt` and `SystemExit`, so Ctrl+C may not stop the program. Catch specific exceptions: `requests.RequestException` for network problems and `ValueError` for invalid JSON. 2. **Retries non-retryable errors.** A 404 or 401 won't succeed on retry, so retrying wastes time. Only retry transient failures such as 429, 5xx, timeouts, and connection errors. 3. **Sleeps after the final attempt.** The loop sleeps even when no retry follows. 4. **Fixed sleep with no backoff.** Exponential backoff is the standard approach for retries. 5. **Silent failure.** Every failure returns `[]` with no loggin…
18.1s4096 output tokens

LLM-jp-3.1 8x13B instruct4

The provided Python function aims to get data from a URL using the requests library, filter out specific items based on their "active" field, and return the result. Here are some suggestions to improve readability, performance, and adherence to best practices: 1. Configure the linter (e.g., flake8 or pylint) to ensure code quality and enforce a consistent coding style. 2. Use type hints to improve readability and maintainability. 3. Use context managers (with statement) for handling requests: 4. Rewrite the function using a more functional programming style: Here's the improved version of the function: ```python import requests import time from typing import List, Dict, Any def get_da…
3.9s406 output tokens

Compare on more prompts →

Frequently asked questions

Which is cheaper, Claude Haiku 5.5 or LLM-jp-3.1 8x13B instruct4?
Per 1M output tokens, Claude Haiku 5.5 is $0.5275 and LLM-jp-3.1 8x13B instruct4 is $0.5035 on FastMetal (US dollars, before tax), so LLM-jp-3.1 8x13B instruct4 is cheaper.
How do the context windows of Claude Haiku 5.5 and LLM-jp-3.1 8x13B instruct4 compare?
Claude Haiku 5.5 takes 1,000,000 tokens; LLM-jp-3.1 8x13B instruct4 takes 4,096.
Can I use Claude Haiku 5.5 and LLM-jp-3.1 8x13B instruct4 with the same API key?
Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "anthropic-claude-haiku-5-5" or "llm-jp-3.1-8x13b-instruct4" as the model. Each is billed at its own rate from the same prepaid balance.

Try both on one API key

Create an account and add credit to call Claude Haiku 5.5 and LLM-jp-3.1 8x13B instruct4 from the browser chat and the API. No monthly fee.

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