Compare/LFM2.5-VL-1.6B vs MiMo-V2.5-Pro

LFM2.5-VL-1.6BvsMiMo-V2.5-Pro

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Liquid AI

LFM2.5-VL-1.6B

Input
$0/M
Output
$0/M
Speed
265 tok/s
TTFT
2.32s
Xiaomi

MiMo-V2.5-Pro

Input
$0.43/M
Output
$0.87/M
Speed
59 tok/s
TTFT
3.18s

Winner by Category

Cheaper
LFM2.5-VL-1.6B
Faster (tok/s)
LFM2.5-VL-1.6B
Lower Latency
LFM2.5-VL-1.6B
Benchmarks (0-2)
MiMo-V2.5-Pro

Pricing Comparison

MetricLFM2.5-VL-1.6BMiMo-V2.5-Pro
Input ($/M tokens)$0$0.43
Output ($/M tokens)$0$0.87
Cost for 1M input + 100K output tokens:
LFM2.5-VL-1.6B$0.00
MiMo-V2.5-Pro$0.52

Speed Comparison

Output Speed (tokens/s) — higher is better
LFM2.5-VL-1.6B
265 tok/s
MiMo-V2.5-Pro
59 tok/s
Time to First Token (seconds) — lower is better
LFM2.5-VL-1.6B
2.32s
MiMo-V2.5-Pro
3.18s

Editorial Analysis

Verdict. MiMo-V2.5-Pro takes the aggregate benchmark matchup 2–0 across 2 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). MiMo-V2.5-Pro leads on Coding Index (60.2), Intelligence Index (42.9).

Speed. On throughput, LFM2.5-VL-1.6B generates tokens at 265 tok/s versus 59 tok/s — about 78% faster. On time-to-first-token, LFM2.5-VL-1.6B responds in 2320ms vs 3180ms, which matters most for chat-style UIs.

Provider. Liquid AI and Xiaomi sell to overlapping but distinct developer audiences: Liquid AI tends to ship frontier reasoning models with premium positioning, while Xiaomi 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); MiMo-V2.5-Pro costs $25.95 ($311/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): LFM2.5-VL-1.6B ≈ $0.00/run, MiMo-V2.5-Pro ≈ $3.89/run. At agent/realtime scale (200M input / 100M output per million requests): LFM2.5-VL-1.6B ≈ $0/run, MiMo-V2.5-Pro ≈ $173/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

  • On throughput, LFM2.5-VL-1.6B is 4.53× faster (265 tok/s vs 59 tok/s). For streaming chat or real-time agents this alone often flips the recommendation.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
1.042.9
Coding Index
60.2
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
LFM2.5-VL-1.6B0 wins
2 winsMiMo-V2.5-Pro

Frequently Asked Questions

Which is cheaper, LFM2.5-VL-1.6B or MiMo-V2.5-Pro?

LFM2.5-VL-1.6B is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $0.54/M for MiMo-V2.5-Pro.

Which model performs better on benchmarks?

MiMo-V2.5-Pro wins 2 out of 12 benchmarks compared to 0 for LFM2.5-VL-1.6B. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

LFM2.5-VL-1.6B generates tokens faster at 265 tok/s vs 59 tok/s. LFM2.5-VL-1.6B also has lower time-to-first-token (2.32s vs 3.18s).

When should I use LFM2.5-VL-1.6B vs MiMo-V2.5-Pro?

Choose based on your priorities: LFM2.5-VL-1.6B for lower cost, MiMo-V2.5-Pro for stronger benchmark performance, and LFM2.5-VL-1.6B for faster generation. For latency-sensitive apps, check the TTFT comparison above.