Comparisons

OpenAI GPT-OSS 20B vs Inkling

On provider list prices, OpenAI GPT-OSS 20B costs $0.05 per million input tokens against $1.87 for Inkling: 37.4x apart. Output is $0.20 against $4.68 (23.4x). On Allocate both bill at list plus the 7% transaction fee.

OpenAI GPT-OSS 20B Inkling
LabOpenAIThinking Machines
AccessOpen weightsOpen weights
Context window128K tokens1M tokens
List price, input$0.05 / M tokens$1.87 / M tokens
List price, output$0.2 / M tokens$4.68 / M tokens
Cached inputn/a$0.374 / M tokens
LicenseApache 2.0Apache 2.0
Fine-tunableYesYes

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $130 a month on OpenAI GPT-OSS 20B and $3,882 on Inkling at list: a gap of $3,752, or 29.9x.

Inkling reads 1M tokens per request against 128K for OpenAI GPT-OSS 20B, 7.6x the window. That decides which one can take whole documents without splitting them.

OpenAI GPT-OSS 20B$0.05$0.20
Inkling$1.87$4.68
InputOutput

Choose OpenAI GPT-OSS 20B for

  • The lower list price ($0.05 in / $0.20 out per M tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
OpenAI GPT-OSS 20B details

Choose Inkling for

  • The longer context window (1M vs 128K tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
  • Published cached-input pricing ($0.37 per M tokens)
Inkling details

Common questions

Which is cheaper, OpenAI GPT-OSS 20B or Inkling?

OpenAI GPT-OSS 20B, on this workload shape. At list prices it is $0.05/$0.20 per million tokens in and out against $1.87/$4.68 for Inkling. Billed on Allocate: $0.053/$0.21 against $2.00/$5.01, list plus 7%.

Which has the bigger context window?

Inkling: 1,000,000 tokens (1M) against 131,072 (128K) for OpenAI GPT-OSS 20B.

Can I fine-tune OpenAI GPT-OSS 20B or Inkling?

Both publish open weights (OpenAI GPT-OSS 20B: Apache 2.0; Inkling: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

Run the numbers on your workload

Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.