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LFM3-1.7B vs Ripple 65B Preview

LFM3-1.7B

Liquid AI

40#175
vs
Ripple 65B Preview

ricursiveai

40#177
Signal-by-Signal Comparison
SignalLFM3-1.7BDeltaRipple 65B Preview
Capabilities
67
+17
50
Pricing
100
+0
99
Context window size
86
-9
96
Recency
100
--
100
Output Capacity
63
-19
82
Overall Result
2 wins
of 5
2 wins
It's a tie - both models win 2 signals each

Score History Unavailable

Not enough historical data to show a comparison chart. Score history is recorded weekly and will be available after at least two data points are collected.

40

LFM3-1.7B

vs

40

Ripple 65B Preview

Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

LFM3-1.7B

Liquid AI

Best Value
Per request$0.000180
Daily$0.60
Monthly$18.00
Annual$216.00

Ripple 65B Preview

ricursiveai

Per request$0.000500
Daily$1.67
Monthly$50.00
Annual$600.00

LFM3-1.7B saves you $32.00/month

That's $384.00/year compared to Ripple 65B Preview at your current usage level of 100K calls/month.

64% cheaper
Choose LFM3-1.7B for cost optimization

LFM3-1.7B pricing:
Input:$0.08/M tokens
Output:$0.20/M tokens
Ripple 65B Preview pricing:
Input:$0.20/M tokens
Output:$0.60/M tokens
Tie
LFM3-1.7B

Liquid AI

40

Composite Score

Tie
Ripple 65B Preview

ricursiveai

40

Composite Score

Signal-by-Signal Comparison
MetricLFM3-1.7BRipple 65B PreviewWinner
Overall Score
40
40
--
Rank#175#177
LFM3-1.7B
Quality Rank#175#177
LFM3-1.7B
Adoption Rank#175#177
LFM3-1.7B
Parameters1.7B65B--
Context Window262K1049K
Ripple 65B Preview
Pricing$0.08/$0.20/M$0.20/$0.60/M--
Signal Scores
Capabilities
67
50
LFM3-1.7B
Pricing
100
99
LFM3-1.7B
Context window size
86
96
Ripple 65B Preview
Recency
100
100
LFM3-1.7B
Output Capacity
63
82
Ripple 65B Preview
Benchmark Interpretation

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%). Learn more about our methodology.

LFM3-1.7BEntry Level

Scores 40/100 (rank #175), placing it in the top 40% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Ripple 65B PreviewEntry Level

Scores 40/100 (rank #177), placing it in the top 39% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

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.

When to Use Each Model

Choose LFM3-1.7B when you need:

  • High-volume production workloads where API costs must be minimized
  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model

Choose Ripple 65B Preview when you need:

  • Processing long documents or large codebases (1049K token context)
Cost-Performance Analysis
LFM3-1.7BBest Value
Input cost$0.08/M tokens
Output cost$0.20/M tokens
Cost per quality point$0.007
Est. monthly (1M tokens/day)$4.20
Ripple 65B Preview
Input cost$0.20/M tokens
Output cost$0.60/M tokens
Cost per quality point$0.020
Est. monthly (1M tokens/day)$12.00

LFM3-1.7B offers 65% better value per quality point. At 1M tokens/day, you'd spend $4.20/month with LFM3-1.7B vs $12.00/month with Ripple 65B Preview - a $7.80 monthly difference.

Latency & Speed
LFM3-1.7BFaster
Speed score0/100
Ripple 65B Preview
Speed score0/100

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.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

LFM3-1.7B

Customer support chatbot

Suitable for user-facing chat with competitive response times. LFM3-1.7B also offers lower per-token costs for high-volume support

LFM3-1.7B

Long document analysis

Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass

Ripple 65B Preview

Batch data extraction

Lower output pricing ($0.20/M) reduces costs when processing thousands of records daily

LFM3-1.7B

Creative writing & content

Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content

LFM3-1.7B
Which Should You Choose?
Our recommendation:
LFM3-1.7B

LFM3-1.7B and Ripple 65B Preview 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.

LFM3-1.7B
Recommended

by Liquid AI

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Cost - 65% lower pricing; better value at scale
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale

by ricursiveai

Consider for specialized use cases.

Capability Comparison
CapabilityLFM3-1.7BRipple 65B Preview
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoningdiffers
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

LFM3-1.7B

Liquid AI

Best Value
$0.3840
estimated monthly cost

Ripple 65B Preview

ricursiveai

$1.08
estimated monthly cost

LFM3-1.7B saves you $0.6960/month

That's 64% cheaper than Ripple 65B Preview 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.

Parameters & Context
ParameterLFM3-1.7BRipple 65B Preview
Context Window262K1.0M
Max Output Tokens8,192131,072
Open SourceYesNo
CreatedNov 6, 2026Nov 4, 2026
Last updated: 2m ago

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