API cost decision in 10 seconds
LFM2.5-1.2B-Instruct (free) vs Qwen3 VL 32B Instruct
Pick LFM2.5-1.2B-Instruct (free) for lower cost; pick Qwen3 VL 32B Instruct only if the larger context window matters more.
Budget verdict
Pick LFM2.5-1.2B-Instruct (free) for lower cost; pick Qwen3 VL 32B Instruct only if the larger context window matters more.
On the standard 1M input plus 500K output workload, LFM2.5-1.2B-Instruct (free) is estimated at $0 vs $0.31 for Qwen3 VL 32B Instruct, saving $0.31 (100% lower).
Qwen3 VL 32B Instruct has more context, but LFM2.5-1.2B-Instruct (free) saves $0.31 on the standard workload. At 10x that traffic, the same price gap is about $3.12. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
LFM2.5-1.2B-Instruct (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | LFM2.5-1.2B-Instruct (free) | Qwen3 VL 32B Instruct |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | LFM2.5-1.2B-Instruct (free) | $0 | $0.73 |
| Balanced workload | 1M input + 1M output | LFM2.5-1.2B-Instruct (free) | $0 | $0.52 |
| Output-heavy chatbot | 1M input + 5M output | LFM2.5-1.2B-Instruct (free) | $0 | $2.18 |
Estimate your workload cost
Your Workload Cost
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
LFM2.5-1.2B-Instruct (free) has the lower input price, LFM2.5-1.2B-Instruct (free) has the lower output price, and Qwen3 VL 32B Instruct offers the larger context window.
For a 1M input token plus 500K output token workload, the estimated API cost is $0 for LFM2.5-1.2B-Instruct (free) and $0.31 for Qwen3 VL 32B Instruct.
Choose LFM2.5-1.2B-Instruct (free) when you care most about lower input-token price, and lower output-token price.
Choose Qwen3 VL 32B Instruct when you care most about larger context window.
| Feature | LFM2.5-1.2B-Instruct (free) (LiquidAI) | Qwen3 VL 32B Instruct (Qwen) |
|---|---|---|
| Input Price prompt tokens per 1M | $0 | $0.1 |
| Completion Price per 1M tokens | $0 | $0.42 |
| Sample Workload Cost 1M input + 500K output | $0 | $0.31 |
| Context Window | 32.77K | 262.14K |
| Release Date | 2026-01-20 | 2025-10-23 |
LFM2.5-1.2B-Instruct is a compact, high-performance instruction-tuned model built for fast on-device AI. It delivers strong chat quality in a 1.2B parameter footprint, with efficient edge inference and broad runtime support.
Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | LFM2.5-1.2B-Instruct (free) | On the standard 1M input plus 500K output workload, LFM2.5-1.2B-Instruct (free) is estimated at $0 vs $0.31 for Qwen3 VL 32B Instruct, saving $0.31 (100% lower). |
| High-volume input processing | LFM2.5-1.2B-Instruct (free) | Lower prompt-token price matters most when prompts or retrieved passages dominate the bill. |
| Long responses and chatbots | LFM2.5-1.2B-Instruct (free) | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Qwen3 VL 32B Instruct | A larger context window leaves more room for retrieved passages and source files. |