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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
171 tok/s
TTFT
2.71s
Xiaomi

MiMo-V2.5-Pro

Input
$0.43/M
Output
$0.87/M
Speed
41 tok/s
TTFT
6.14s

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
171 tok/s
MiMo-V2.5-Pro
41 tok/s
Time to First Token (seconds) — lower is better
Ling 3.0 Tiny
2.71s
MiMo-V2.5-Pro
6.14s

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 (11.9). MiMo-V2.5-Pro leads on Coding Index (60.2), Intelligence Index (26.4).

Speed. On throughput, Ling 3.0 Tiny generates tokens at 171 tok/s versus 41 tok/s — about 76% faster. On time-to-first-token, Ling 3.0 Tiny responds in 2710ms vs 6140ms, 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 4.21× faster (171 tok/s vs 41 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
11.926.4
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 171 tok/s vs 41 tok/s. Ling 3.0 Tiny also has lower time-to-first-token (2.71s vs 6.14s).

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.