Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | LFM2.5-VL-1.6B | GLM-4.5V (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0 | $0.6 |
| Output ($/M tokens) | $0 | $1.8 |
Verdict. GLM-4.5V (Non-reasoning) takes the aggregate benchmark matchup 1–0 across 1 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.
Strengths. LFM2.5-VL-1.6B is strongest on Intelligence Index (1.0). GLM-4.5V (Non-reasoning) leads on Intelligence Index (6.8).
Speed. On throughput, LFM2.5-VL-1.6B generates tokens at 265 tok/s versus 92 tok/s — about 65% faster. On time-to-first-token, GLM-4.5V (Non-reasoning) responds in 1850ms vs 2320ms, which matters most for chat-style UIs.
Provider. Liquid AI and Z AI sell to overlapping but distinct developer audiences: Liquid AI tends to ship frontier reasoning models with premium positioning, while Z AI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): LFM2.5-VL-1.6B costs $0.00 ($0/year); GLM-4.5V (Non-reasoning) costs $45.00 ($540/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): LFM2.5-VL-1.6B ≈ $0.00/run, GLM-4.5V (Non-reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): LFM2.5-VL-1.6B ≈ $0/run, GLM-4.5V (Non-reasoning) ≈ $300/run. LFM2.5-VL-1.6B becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
LFM2.5-VL-1.6B is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $0.90/M for GLM-4.5V (Non-reasoning).
GLM-4.5V (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for LFM2.5-VL-1.6B. See the detailed benchmark chart above for per-category results.
LFM2.5-VL-1.6B generates tokens faster at 265 tok/s vs 92 tok/s. However, GLM-4.5V (Non-reasoning) has lower time-to-first-token (1.85s vs 2.32s).
Choose based on your priorities: LFM2.5-VL-1.6B for lower cost, GLM-4.5V (Non-reasoning) for stronger benchmark performance, and LFM2.5-VL-1.6B for faster generation. For latency-sensitive apps, check the TTFT comparison above.