| Signal | LFM2-2.6B | Delta | Sonar |
|---|---|---|---|
Capabilities | 17 | -33 | |
Pricing | 0 | -1 | |
Context window size | 72 | -9 | |
Recency | 100 | +45 | |
Output Capacity | 20 | -- | |
| Overall Result | 1 wins | of 5 | 3 wins |
10
days higher
3
days
17
days higher
Liquid AI
Perplexity
LFM2-2.6B saves you $148.00/month
That's $1776.00/year compared to Sonar at your current usage level of 100K calls/month.
| Metric | LFM2-2.6B | Sonar | Winner |
|---|---|---|---|
| Overall Score | 53 | 53 | -- |
| Rank | #263 | #264 | LFM2-2.6B |
| Quality Rank | #263 | #264 | LFM2-2.6B |
| Adoption Rank | #263 | #264 | LFM2-2.6B |
| Parameters | 2.6B | -- | -- |
| Context Window | 33K | 127K | Sonar |
| Pricing | $0.01/$0.02/M | $1.00/$1.00/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | Sonar |
| Pricing | 0 | 1 | Sonar |
| Context window size | 72 | 81 | Sonar |
| Recency | 100 | 55 | LFM2-2.6B |
| Output Capacity | 20 | 20 | LFM2-2.6B |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 53/100 (rank #263), placing it in the top 10% of all 290 models tracked.
Scores 53/100 (rank #264), placing it in the top 9% 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.
LFM2-2.6B offers 99% better value per quality point. At 1M tokens/day, you'd spend $0.45/month with LFM2-2.6B vs $30.00/month with Sonar - a $29.55 monthly difference.
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. LFM2-2.6B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (127K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.02/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (53/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
LFM2-2.6B and Sonar 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
LFM2-2.6B
Marginally better benchmark scores; both are excellent
Best for Cost
LFM2-2.6B
99% lower pricing; better value at scale
Best for Reliability
LFM2-2.6B
Higher uptime and faster response speeds
Best for Prototyping
LFM2-2.6B
Stronger community support and better developer experience
Best for Production
LFM2-2.6B
Wider enterprise adoption and proven at scale
by Liquid AI
| Capability | LFM2-2.6B | Sonar |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
Liquid AI
Perplexity
LFM2-2.6B saves you $2.96/month
That's 99% cheaper than Sonar at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | LFM2-2.6B | Sonar |
|---|---|---|
| Context Window | 33K | 127K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | No |
| Created | Oct 20, 2025 | Jan 27, 2025 |
Both LFM2-2.6B and Sonar score 53/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
LFM2-2.6B is ranked #263 and Sonar is ranked #264 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
LFM2-2.6B is cheaper at $0.02/M output tokens vs Sonar's $1.00/M output tokens - 50.0x more expensive. Input token pricing: LFM2-2.6B at $0.01/M vs Sonar at $1.00/M.
Sonar has a larger context window of 127,072 tokens compared to LFM2-2.6B's 32,768 tokens. A larger context window means the model can process longer documents and conversations.