Compare/Ling 3.0 Tiny vs MiMo-V2.5-Pro

Ling 3.0 TinyvsMiMo-V2.5-Pro

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

InclusionAI

Ling 3.0 Tiny

Input
$0/M
Output
$0/M
Speed
175 tok/s
TTFT
2.57s
Xiaomi

MiMo-V2.5-Pro

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

Winner by Category

Cheaper
Ling 3.0 Tiny
Faster (tok/s)
Ling 3.0 Tiny
Lower Latency
Ling 3.0 Tiny
Benchmarks (0-2)
MiMo-V2.5-Pro

Pricing Comparison

MetricLing 3.0 TinyMiMo-V2.5-Pro
Input ($/M tokens)$0$0.43
Output ($/M tokens)$0$0.87
Cost for 1M input + 100K output tokens:
Ling 3.0 Tiny$0.00
MiMo-V2.5-Pro$0.52

Speed Comparison

Output Speed (tokens/s) — higher is better
Ling 3.0 Tiny
175 tok/s
MiMo-V2.5-Pro
59 tok/s
Time to First Token (seconds) — lower is better
Ling 3.0 Tiny
2.57s
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. Ling 3.0 Tiny is strongest on Coding Index (26.5), Intelligence Index (24.5). MiMo-V2.5-Pro leads on Coding Index (60.2), Intelligence Index (42.9).

Speed. On throughput, Ling 3.0 Tiny generates tokens at 175 tok/s versus 59 tok/s — about 67% faster. On time-to-first-token, Ling 3.0 Tiny responds in 2570ms vs 3180ms, which matters most for chat-style UIs.

Provider. InclusionAI and Xiaomi sell to overlapping but distinct developer audiences: InclusionAI 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): Ling 3.0 Tiny 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): Ling 3.0 Tiny ≈ $0.00/run, MiMo-V2.5-Pro ≈ $3.89/run. At agent/realtime scale (200M input / 100M output per million requests): Ling 3.0 Tiny ≈ $0/run, MiMo-V2.5-Pro ≈ $173/run. Ling 3.0 Tiny 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, Ling 3.0 Tiny is 2.99× faster (175 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
24.542.9
Coding Index
26.560.2
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Ling 3.0 Tiny0 wins
2 winsMiMo-V2.5-Pro

Frequently Asked Questions

Which is cheaper, Ling 3.0 Tiny or MiMo-V2.5-Pro?

Ling 3.0 Tiny 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 Ling 3.0 Tiny. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Ling 3.0 Tiny generates tokens faster at 175 tok/s vs 59 tok/s. Ling 3.0 Tiny also has lower time-to-first-token (2.57s vs 3.18s).

When should I use Ling 3.0 Tiny vs MiMo-V2.5-Pro?

Choose based on your priorities: Ling 3.0 Tiny for lower cost, MiMo-V2.5-Pro for stronger benchmark performance, and Ling 3.0 Tiny for faster generation. For latency-sensitive apps, check the TTFT comparison above.