LFM2.5-1.2B-Instruct (free) is free for input tokens while Gemma 4 26B A4B costs $0.06 per 1M tokens.
API cost decision in 10 seconds
Gemma 4 26B A4B vs LFM2.5-1.2B-Instruct (free)
Pick LFM2.5-1.2B-Instruct (free) for lower cost; pick Gemma 4 26B A4B only if the larger context window matters more.
Pricing data updated:
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 Gemma 4 26B A4B 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 $0.23 for Gemma 4 26B A4B, saving $0.23 (100% lower).
Cost-first pickLFM2.5-1.2B-Instruct (free)
Context-first pickGemma 4 26B A4B
Sample savings$0.23100%
10x traffic gap$2.25
Gemma 4 26B A4B has more context, but LFM2.5-1.2B-Instruct (free) saves $0.23 on the standard workload. At 10x that traffic, the same price gap is about $2.25. 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 | Gemma 4 26B A4B | LFM2.5-1.2B-Instruct (free) |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | LFM2.5-1.2B-Instruct (free) | $0.46 | $0 |
| Balanced workload | 1M input + 1M output | LFM2.5-1.2B-Instruct (free) | $0.39 | $0 |
| Output-heavy chatbot | 1M input + 5M output | LFM2.5-1.2B-Instruct (free) | $1.71 | $0 |
LFM2.5-1.2B-Instruct (free) is free for output tokens while Gemma 4 26B A4B costs $0.33 per 1M tokens.
Gemma 4 26B A4B 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 $0.23.
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; Gemma 4 26B A4B 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 $0.23 for Gemma 4 26B A4B and $0 for LFM2.5-1.2B-Instruct (free).
Choose Gemma 4 26B A4B 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 $0.23 for Gemma 4 26B A4B, saving $0.23 (100% lower).
- LFM2.5-1.2B-Instruct (free) is free for the standard workload while the other model is estimated at $0.23.
- LFM2.5-1.2B-Instruct (free) is free for input tokens while Gemma 4 26B A4B costs $0.06 per 1M tokens.
- LFM2.5-1.2B-Instruct (free) is free for output tokens while Gemma 4 26B A4B costs $0.33 per 1M tokens.
- Gemma 4 26B A4B has 229.38K more context (8x larger).
| Feature | Gemma 4 26B A4B (Google) | LFM2.5-1.2B-Instruct (free) (LiquidAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.06 | $0 |
| Completion Price per 1M tokens | $0.33 | $0 |
| Sample Workload Cost 1M input + 500K output | $0.23 | $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 $0.23 for Gemma 4 26B A4B, saving $0.23 (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 | Gemma 4 26B A4B | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- Gemma 4 26B A4B (free) can replace Gemma 4 26B A4B when lower sample workload cost matters most: $0.
- Gemma 4 31B (free) can replace Gemma 4 26B A4B when lower sample workload cost matters most: $0.
- Lyria 3 Pro Preview can replace Gemma 4 26B A4B when lower sample workload cost matters most: $0.
- Lyria 3 Clip Preview can replace Gemma 4 26B A4B when lower sample workload cost matters most: $0.
- 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.
- DeepSeek V4 Flash (free) offers 1.05M context with $0 sample workload cost.
- No popular competitor is currently available.
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 provider hubsGoogle catalog
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Open LiquidAI modelsGemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...
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.