LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning): The Ultimate Performance & Pricing Comparison

Deep dive into reasoning, benchmarks, and latency insights.

The Final Verdict in the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) Showdown

After a comprehensive analysis, GLM-4.7-Flash (Reasoning) emerges as the superior choice for users prioritizing raw intelligence and reasoning. However, LFM2.5-VL-1.6B remains a highly competitive option, especially for scenarios demanding faster response times and cost-efficiency. This detailed LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) comparison will break down every critical metric to help you make the best decision.

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Model Snapshot

Key decision metrics at a glance.

LFM2.5-VL-1.6B

Other

Reasoning: 6
Coding: 1
Multimodal: 1
Long Context: 1
Blended Price / 1M tokens: $0.015
P95 Latency: 1000ms
Tokens per second: —

GLM-4.7-Flash (Reasoning)

Other

Reasoning: 6
Coding: 3
Multimodal: 3
Long Context: 4
Blended Price / 1M tokens: $0.000
P95 Latency: 1000ms
Tokens per second: 89.62tokens/sec

Overall Capabilities

The capability radar provides a holistic view of the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) matchup. This chart illustrates each model's strengths and weaknesses at a glance, forming a cornerstone of our LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) analysis.

This radar chart visually maps the core capabilities (reasoning, coding, math proxy, multimodal, long context) of `LFM2.5-VL-1.6B` vs `GLM-4.7-Flash (Reasoning)`.

Benchmark Breakdown

For a granular look, this chart directly compares scores across standardized benchmarks. In the critical MMLU Pro test, a key part of the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) debate, LFM2.5-VL-1.6B scores 60 against GLM-4.7-Flash (Reasoning)'s 60. This data-driven approach is essential for any serious LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) comparison.

This grouped bar chart provides a side-by-side comparison for each benchmark metric.

Speed & Latency

Speed is a crucial factor in the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) decision for interactive applications. The metrics below highlight the trade-offs you should weigh before shipping to production.

Time to First Token

LFM2.5-VL-1.6B300ms

GLM-4.7-Flash (Reasoning)300ms

Tokens per Second

LFM2.5-VL-1.6B46

GLM-4.7-Flash (Reasoning)89.62

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The Economics of LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning)

Power is only one part of the equation. This LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) pricing analysis gives you a true sense of value.

Pricing Breakdown

Compare input and output pricing at a glance.

Real-World Cost Scenario

To process 1M input tokens and generate 250k output tokens, LFM2.5-VL-1.6B would cost $0.018, whereas GLM-4.7-Flash (Reasoning) would cost $0.000. This practical calculation is vital for any developer considering the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) choice.

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Which Model Wins the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) Battle for You?

Choose LFM2.5-VL-1.6B if...

Cost is a secondary concern to power in your LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) decision.

You need the most advanced reasoning capabilities available.

Your use case demands cutting-edge AI performance.

Choose GLM-4.7-Flash (Reasoning) if...

You need a highly responsive model for user-facing applications.

Your budget is a primary consideration in the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) choice.

You are developing at scale where operational costs are critical.

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Your Questions about the LFM2.5-VL-1.6B vs GLM-4.7-Flash (Reasoning) Comparison

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Data source: https://artificialanalysis.ai/