Inkling vs LLM-jp-3.1 8x13B instruct4
Pricing, context window and real answers (October 2026)
Inkling (Thinking Machines) 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
| Inkling | LLM-jp-3.1 8x13B instruct4 | |
|---|---|---|
| Provider | Thinking Machines | LLM-jp |
| Input (per 1M tokens) | $1.01 | $0.102 |
| Output (per 1M tokens) | $4.28 | $0.5035 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $3.14 | $0.3537 |
| Context window | 524,288 tokens | 4,096 tokens |
| Release date | 7/17/2026 | 3/21/2026 |
| Input modalities | text, image, audio | text |
| Throughput (p50) | 120 tokens/s (n=20) | 125 tokens/s (n=25) |
| Arena · overall | #95 · ELO 1,441 | Unranked |
| Arena · Japanese | #56 · ELO 1,430 | Unranked |
| Arena · coding | #91 · ELO 1,492 | Unranked |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-08; 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, 88% less than Inkling.
- For long documents, Inkling: a 524,288-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.
Inkling
LLM-jp-3.1 8x13B instruct4
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…
Inkling
LLM-jp-3.1 8x13B instruct4
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):…
Inkling
LLM-jp-3.1 8x13B instruct4
Frequently asked questions
- Which is cheaper, Inkling or LLM-jp-3.1 8x13B instruct4?
- Per 1M output tokens, Inkling is $4.28 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 Inkling and LLM-jp-3.1 8x13B instruct4 compare?
- Inkling takes 524,288 tokens; LLM-jp-3.1 8x13B instruct4 takes 4,096.
- Can I use Inkling and LLM-jp-3.1 8x13B instruct4 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "inkling" 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 Inkling and LLM-jp-3.1 8x13B instruct4 from the browser chat and the API. No monthly fee.
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- LLM-jp-3.1 8x13B instruct4 details