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Qwen3 235B A22B Instruct 2507 FP8 Throughput vs Inkling

On provider list prices, Qwen3 235B A22B Instruct 2507 FP8 Throughput costs $0.20 per million input tokens against $1.87 for Inkling: 9.3x apart. Output is $0.60 against $4.68 (7.8x). On Allocate both bill at list plus the 7% transaction fee.

Qwen3 235B A22B Instruct 2507 FP8 Throughput Inkling
LabQwenThinking Machines
AccessOpen weightsOpen weights
Context window256K tokens1M tokens
List price, input$0.2 / M tokens$1.87 / M tokens
List price, output$0.6 / 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 $450 a month on Qwen3 235B A22B Instruct 2507 FP8 Throughput and $3,882 on Inkling at list: a gap of $3,432, or 8.6x.

Inkling reads 1M tokens per request against 256K for Qwen3 235B A22B Instruct 2507 FP8 Throughput, 3.8x the window. That decides which one can take whole documents without splitting them.

Qwen3 235B A22B Instruct 2507 FP8 Throughput$0.20$0.60
Inkling$1.87$4.68
InputOutput

Choose Qwen3 235B A22B Instruct 2507 FP8 Throughput for

  • The lower list price ($0.20 in / $0.60 out per M tokens)
  • Fine-tuning under a permissive license (Apache 2.0)
Qwen3 235B A22B Instruct 2507 FP8 Throughput details

Choose Inkling for

  • The longer context window (1M vs 256K 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, Qwen3 235B A22B Instruct 2507 FP8 Throughput or Inkling?

Qwen3 235B A22B Instruct 2507 FP8 Throughput, on this workload shape. At list prices it is $0.20/$0.60 per million tokens in and out against $1.87/$4.68 for Inkling. Billed on Allocate: $0.21/$0.64 against $2.00/$5.01, list plus 7%.

Which has the bigger context window?

Inkling: 1,000,000 tokens (1M) against 262,144 (256K) for Qwen3 235B A22B Instruct 2507 FP8 Throughput.

Can I fine-tune Qwen3 235B A22B Instruct 2507 FP8 Throughput or Inkling?

Both publish open weights (Qwen3 235B A22B Instruct 2507 FP8 Throughput: 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

Qwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3.5 397B A17bInkling vs Qwen3.5 397B A17bQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3.7 MaxInkling vs Qwen3.7 MaxQwen3 235B A22B Instruct 2507 FP8 Throughput vs Qwen3-VL-8B-InstructQwen3 235B A22B Instruct 2507 FP8 Throughput vs Kimi K2.5 Fp4

Run the numbers on your workload

LLM price comparisonAgent cost calculatorQwen3 235B A22B Instruct 2507 FP8 ThroughputInkling

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.

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