LFM2.5-1.2B-Instruct (free) is free for input tokens while Qwen3.5-35B-A3B costs $0.14 per 1M tokens.
API cost decision in 10 seconds
Qwen3.5-35B-A3B vs LFM2.5-1.2B-Instruct (free)
Pick LFM2.5-1.2B-Instruct (free) for lower cost; pick Qwen3.5-35B-A3B 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 LFM2.5-1.2B-Instruct (free) for lower cost; pick Qwen3.5-35B-A3B 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 $0.64 for Qwen3.5-35B-A3B, saving $0.64 (100% lower).
Cost-first pickLFM2.5-1.2B-Instruct (free)
Context-first pickQwen3.5-35B-A3B
Sample savings$0.64100%
10x traffic gap$6.39
Qwen3.5-35B-A3B has more context, but LFM2.5-1.2B-Instruct (free) saves $0.64 on the standard workload. At 10x that traffic, the same price gap is about $6.39. 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 | Qwen3.5-35B-A3B | LFM2.5-1.2B-Instruct (free) |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | LFM2.5-1.2B-Instruct (free) | $1.2 | $0 |
| Balanced workload | 1M input + 1M output | LFM2.5-1.2B-Instruct (free) | $1.14 | $0 |
| Output-heavy chatbot | 1M input + 5M output | LFM2.5-1.2B-Instruct (free) | $5.14 | $0 |
LFM2.5-1.2B-Instruct (free) is free for output tokens while Qwen3.5-35B-A3B costs $1 per 1M tokens.
Qwen3.5-35B-A3B has 229.38K more context (8x larger).
LFM2.5-1.2B-Instruct (free) is free for the standard workload while the other model is estimated at $0.64.
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; Qwen3.5-35B-A3B offers the larger context window. For the 1M input plus 500K output sample, LFM2.5-1.2B-Instruct (free) is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $0.64 for Qwen3.5-35B-A3B and $0 for LFM2.5-1.2B-Instruct (free).
Choose Qwen3.5-35B-A3B when you care most about larger context window.
Choose LFM2.5-1.2B-Instruct (free) when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, LFM2.5-1.2B-Instruct (free) is estimated at $0 vs $0.64 for Qwen3.5-35B-A3B, saving $0.64 (100% lower).
- LFM2.5-1.2B-Instruct (free) is free for the standard workload while the other model is estimated at $0.64.
- LFM2.5-1.2B-Instruct (free) is free for input tokens while Qwen3.5-35B-A3B costs $0.14 per 1M tokens.
- LFM2.5-1.2B-Instruct (free) is free for output tokens while Qwen3.5-35B-A3B costs $1 per 1M tokens.
- Qwen3.5-35B-A3B has 229.38K more context (8x larger).
| Feature | Qwen3.5-35B-A3B (Qwen) | LFM2.5-1.2B-Instruct (free) (LiquidAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.139 | $0 |
| Completion Price per 1M tokens | $1 | $0 |
| Sample Workload Cost 1M input + 500K output | $0.64 | $0 |
| Context Window | 262.14K | 32.77K |
| Release Date |
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 $0.64 for Qwen3.5-35B-A3B, saving $0.64 (100% lower). |
| High-volume input processing | LFM2.5-1.2B-Instruct (free) | Lower prompt-token price matters most when prompts, retrieved passages, or documents 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 | Qwen3.5-35B-A3B | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- Qwen3 Next 80B A3B Instruct (free) can replace Qwen3.5-35B-A3B when lower sample workload cost matters most: $0.
- Qwen3 Coder 480B A35B (free) can replace Qwen3.5-35B-A3B when lower sample workload cost matters most: $0.
- Qwen2.5 7B Instruct can replace Qwen3.5-35B-A3B when lower sample workload cost matters most: $0.09.
- Qwen3.5-9B can replace Qwen3.5-35B-A3B when lower sample workload cost matters most: $0.11.
- Llama 4 Scout offers 10M context with $0.23 sample workload cost.
- Owl Alpha offers 1.05M context with $0 sample workload cost.
- DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
- Gemini 2.5 Flash Lite offers 1.05M context with $0.3 sample workload cost.
- DeepSeek V4 Flash · DeepSeek · #1
- Hy3 preview · Tencent · #2
- Claude Opus 4.7 · Anthropic · #3
- Claude Sonnet 4.6 · Anthropic · #4
Cheaper alternatives
Review low-cost models sorted by a standard 1M input plus 500K output workload.
Open cheapest modelsLarger context alternatives
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Open LiquidAI modelsThe Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
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.