API cost decision in 10 seconds

LFM2-24B-A2B vs Qwen3.5 397B A17B

Pick LFM2-24B-A2B for lower cost; pick Qwen3.5 397B A17B only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick LFM2-24B-A2B for lower cost; pick Qwen3.5 397B A17B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, LFM2-24B-A2B is estimated at $0.09 vs $1.56 for Qwen3.5 397B A17B, saving $1.47 (94.2% lower).

Cost-first pickLFM2-24B-A2B
Context-first pickQwen3.5 397B A17B
Sample savings$1.4794.2%
10x traffic gap$14.7

Qwen3.5 397B A17B has more context, but LFM2-24B-A2B saves $1.47 on the standard workload. At 10x that traffic, the same price gap is about $14.7. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

LFM2-24B-A2B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickLFM2-24B-A2BQwen3.5 397B A17B
Input-heavy / RAG5M input + 500K outputLFM2-24B-A2B$0.21$3.12
Balanced workload1M input + 1M outputLFM2-24B-A2B$0.15$2.73
Output-heavy chatbot1M input + 5M outputLFM2-24B-A2B$0.63$12.09
Cheaper input LFM2-24B-A2B $0.03 vs $0.39 / 1M

LFM2-24B-A2B is $0.36 cheaper per 1M input tokens (92.3% lower; 13x difference).

Cheaper output LFM2-24B-A2B $0.12 vs $2.34 / 1M

LFM2-24B-A2B is $2.22 cheaper per 1M output tokens (94.9% lower; 19.5x difference).

Larger context Qwen3.5 397B A17B 128K vs 262.14K

Qwen3.5 397B A17B has 134.14K more context (2.05x larger).

Sample workload LFM2-24B-A2B $0.09 vs $1.56

LFM2-24B-A2B is $1.47 cheaper on the standard workload (94.2% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
LFM2-24B-A2B Calculating… Estimated API cost
Qwen3.5 397B A17B Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

LFM2-24B-A2B has the lower input price; LFM2-24B-A2B has the lower output price; Qwen3.5 397B A17B offers the larger context window. For the 1M input plus 500K output sample, LFM2-24B-A2B is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.09 for LFM2-24B-A2B and $1.56 for Qwen3.5 397B A17B.

Best Fit

Choose LFM2-24B-A2B when you care most about lower input-token price, and lower output-token price.

Choose Qwen3.5 397B A17B when you care most about larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, LFM2-24B-A2B is estimated at $0.09 vs $1.56 for Qwen3.5 397B A17B, saving $1.47 (94.2% lower).
  • LFM2-24B-A2B is $1.47 cheaper on the standard workload (94.2% lower).
  • LFM2-24B-A2B is $0.36 cheaper per 1M input tokens (92.3% lower; 13x difference).
  • LFM2-24B-A2B is $2.22 cheaper per 1M output tokens (94.9% lower; 19.5x difference).
  • Qwen3.5 397B A17B has 134.14K more context (2.05x larger).
Head-to-Head Specs
FeatureLFM2-24B-A2B
(LiquidAI)
Qwen3.5 397B A17B
(Qwen)
Input Price
prompt tokens per 1M
$0.03$0.39
Completion Price
per 1M tokens
$0.12$2.34
Sample Workload Cost
1M input + 500K output
$0.09$1.56
Context Window128K262.14K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLFM2-24B-A2BOn the standard 1M input plus 500K output workload, LFM2-24B-A2B is estimated at $0.09 vs $1.56 for Qwen3.5 397B A17B, saving $1.47 (94.2% lower).
High-volume input processingLFM2-24B-A2BLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLFM2-24B-A2BLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workQwen3.5 397B A17BA larger context window leaves more room for retrieved passages, conversation history, or source files.

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Provider catalogs

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LiquidAI catalog

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Qwen catalog

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LFM2-24B-A2B

LFM2-24B-A2B is the largest model in the LFM2 family of hybrid architectures designed for efficient on-device deployment. Built as a 24B parameter Mixture-of-Experts model with only 2B active parameters per...

Qwen3.5 397B A17B

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...