Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | LFM2.5-VL-1.6B | JT-35B-Flash |
|---|---|---|
| Input ($/M tokens) | $0 | — |
| Output ($/M tokens) | $0 | — |
Verdict. JT-35B-Flash 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). JT-35B-Flash leads on Intelligence Index (29.0).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Liquid AI and China Mobile sell to overlapping but distinct developer audiences: Liquid AI tends to ship frontier reasoning models with premium positioning, while China Mobile often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
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.
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 $—/M for JT-35B-Flash.
JT-35B-Flash 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 — tok/s. LFM2.5-VL-1.6B also has lower time-to-first-token (2.32s vs —s).
Choose based on your priorities: LFM2.5-VL-1.6B for lower cost, JT-35B-Flash for stronger benchmark performance, and LFM2.5-VL-1.6B for faster generation. For latency-sensitive apps, check the TTFT comparison above.