| Signal | LFM2.5-1.2B-Instruct (free) | Delta | LFM2.5-1.2B-Thinking (free) |
|---|---|---|---|
Capabilities | 17 | -17 | |
Pricing | 30 | -- | |
Context window size | 72 | -- | |
Recency | 100 | -- | |
Output Capacity | 20 | -- | |
| Overall Result | 0 wins | of 5 | 1 wins |
10
days higher
2
days
18
days higher
Liquid AI
Liquid AI
| Metric | LFM2.5-1.2B-Instruct (free) | LFM2.5-1.2B-Thinking (free) | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #152 | #151 | LFM2.5-1.2B-Thinking (free) |
| Quality Rank | #152 | #151 | LFM2.5-1.2B-Thinking (free) |
| Adoption Rank | #152 | #151 | LFM2.5-1.2B-Thinking (free) |
| Parameters | 1.2B | 1.2B | -- |
| Context Window | 33K | 33K | -- |
| Pricing | Free | Free | -- |
| Signal Scores | |||
| Capabilities | 17 | 33 | LFM2.5-1.2B-Thinking (free) |
| Pricing | 30 | 30 | LFM2.5-1.2B-Instruct (free) |
| Context window size | 72 | 72 | LFM2.5-1.2B-Instruct (free) |
| Recency | 100 | 100 | LFM2.5-1.2B-Instruct (free) |
| Output Capacity | 20 | 20 | LFM2.5-1.2B-Instruct (free) |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 40/100 (rank #152), placing it in the top 48% of all 290 models tracked.
Scores 40/100 (rank #151), placing it in the top 48% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. LFM2.5-1.2B-Instruct (free) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (33K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
LFM2.5-1.2B-Instruct (free) and LFM2.5-1.2B-Thinking (free) are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
LFM2.5-1.2B-Instruct (free)
Marginally better benchmark scores; both are excellent
Best for Cost
LFM2.5-1.2B-Instruct (free)
0% lower pricing; better value at scale
Best for Reliability
LFM2.5-1.2B-Instruct (free)
Higher uptime and faster response speeds
Best for Prototyping
LFM2.5-1.2B-Instruct (free)
Stronger community support and better developer experience
Best for Production
LFM2.5-1.2B-Instruct (free)
Wider enterprise adoption and proven at scale
by Liquid AI
| Capability | LFM2.5-1.2B-Instruct (free) | LFM2.5-1.2B-Thinking (free) |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Liquid AI
Liquid AI
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | LFM2.5-1.2B-Instruct (free) | LFM2.5-1.2B-Thinking (free) |
|---|---|---|
| Context Window | 33K | 33K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | Yes |
| Created | Jan 20, 2026 | Jan 20, 2026 |
Both LFM2.5-1.2B-Instruct (free) and LFM2.5-1.2B-Thinking (free) score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
LFM2.5-1.2B-Instruct (free) is ranked #152 and LFM2.5-1.2B-Thinking (free) is ranked #151 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
LFM2.5-1.2B-Instruct (free) is cheaper at $0.00/M output tokens vs LFM2.5-1.2B-Thinking (free)'s $0.00/M output tokens - a significant difference. Input token pricing: LFM2.5-1.2B-Instruct (free) at $0.00/M vs LFM2.5-1.2B-Thinking (free) at $0.00/M.
LFM2.5-1.2B-Instruct (free) has a larger context window of 32,768 tokens compared to LFM2.5-1.2B-Thinking (free)'s 32,768 tokens. A larger context window means the model can process longer documents and conversations.