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Ling 3.0 Flash vs MiMo-V2-Pro

Ling 3.0 Flash and MiMo-V2-Pro are closely matched at 36.5 and 35.8 on the LLM Stats Score. Ling 3.0 Flash is 16.7x cheaper per token.

InclusionAI · Xiaomi · Updated for 2026

Which is better?

Ling 3.0 Flash and MiMo-V2-Pro are closely matched on the overall LLM Stats Score at 36.5 and 35.8.

In the 1 individual benchmarks reported for both models, Ling 3.0 Flash wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Ling 3.0 Flash is roughly 16.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

MiMo-V2-Pro also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Ling 3.0 Flash

  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • cost matters — it's about 16.7x cheaper per token
  • you want the most recent training data — it shipped Aug 2026

Choose MiMo-V2-Pro

  • you process long inputs — it offers a 1,000,000 token context window

At a glance

The differences that matter most.

Core performance indexes
36.5
#75
35.8
#80
35.6
#77
37.6
#64
25.5
#67
25.9
#66
19.7
#61
17.4
#72
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.06 / M
$1.00 / M
Output price
$0.18 / M
$3.00 / M
Context window
131,072
1,000,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Ling 3.0 Flash
MiMo-V2-Pro
18.1#67
18.5#64
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for Ling 3.0 Flash · 7 for MiMo-V2-Pro

1 shared

Ling 3.0 Flash outperforms in 1 benchmarks (SWE-bench Multilingual), while MiMo-V2-Pro is better at 0 benchmarks.

Ling 3.0 Flash significantly outperforms across most benchmarks.

Tue Sep 08 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Ling 3.0 Flash costs less

For input processing, Ling 3.0 Flash ($0.06/1M tokens) is 16.7x cheaper than MiMo-V2-Pro ($1.00/1M tokens).

For output processing, Ling 3.0 Flash ($0.18/1M tokens) is 16.7x cheaper than MiMo-V2-Pro ($3.00/1M tokens).

In conclusion, MiMo-V2-Pro is more expensive than Ling 3.0 Flash.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Sep 08 2026 • llm-stats.com
InclusionAI
Ling 3.0 Flash
Input tokens$0.06
Output tokens$0.18
Best providerDeepinfra
Xiaomi
MiMo-V2-Pro
Input tokens$1.00
Output tokens$3.00
Best providerXiaomi
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

876.0B diff

MiMo-V2-Pro has 876.0B more parameters than Ling 3.0 Flash, making it 706.5% larger.

InclusionAI
Ling 3.0 Flash
124.0Bparameters
Xiaomi
MiMo-V2-Pro
1.0Tparameters
124.0B
Ling 3.0 Flash
1000.0B
MiMo-V2-Pro

Context Window

Maximum input and output token capacity

MiMo-V2-Pro accepts 1,000,000 input tokens compared to Ling 3.0 Flash's 131,072 tokens. Ling 3.0 Flash can generate longer responses up to 131,072 tokens, while MiMo-V2-Pro is limited to 16,384 tokens.

InclusionAI
Ling 3.0 Flash
Input131,072 tokens
Output131,072 tokens
Xiaomi
MiMo-V2-Pro
Input1,000,000 tokens
Output16,384 tokens
Tue Sep 08 2026 • llm-stats.com

Release Timeline

When each model was launched

Ling 3.0 Flash was released on 2026-08-04, while MiMo-V2-Pro was released on 2026-03-18.

Ling 3.0 Flash is 5 months newer than MiMo-V2-Pro.

Ling 3.0 Flash

Aug 4, 2026

1 months ago

4mo newer
MiMo-V2-Pro

Mar 18, 2026

5 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Ling 3.0 Flash is available from DeepInfra. MiMo-V2-Pro is available from Xiaomi.

Ling 3.0 Flash

deepinfra logo
Deepinfra
Input Price:Input: $0.06/1MOutput Price:Output: $0.18/1M

MiMo-V2-Pro

xiaomi logo
Xiaomi
Input Price:Input: $1.00/1MOutput Price:Output: $3.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Ling 3.0 Flash and MiMo-V2-Pro side-by-side, then vote on the output you prefer.

Ling 3.0 Flash
✓ Preferred
MiMo-V2-Pro
Open in Playground

FAQ

Common questions about Ling 3.0 Flash vs MiMo-V2-Pro.

Which is better, Ling 3.0 Flash or MiMo-V2-Pro?

Ling 3.0 Flash and MiMo-V2-Pro are closely matched on the LLM Stats Score at 36.5 and 35.8. Ling 3.0 Flash is made by InclusionAI and MiMo-V2-Pro is made by Xiaomi. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Ling 3.0 Flash compare to MiMo-V2-Pro in benchmarks?

Ling 3.0 Flash scores AIME 2026: 93.2%, Multi-IF: 87.7%, HMMT Feb 26: 87.0%, IMO-AnswerBench: 83.7%, LiveCodeBench v6: 82.8%. MiMo-V2-Pro scores Tau2 Telecom: 96.8%, DeepSearchQA: 86.7%, PinchBench: 81.0%, SWE-Bench Verified: 78.0%, SWE-bench Multilingual: 71.7%.

Is Ling 3.0 Flash cheaper than MiMo-V2-Pro?

Ling 3.0 Flash is 16.7x cheaper for input tokens. Ling 3.0 Flash costs $0.06/M input and $0.18/M output via deepinfra. MiMo-V2-Pro costs $1.00/M input and $3.00/M output via xiaomi.

What are the context window sizes for Ling 3.0 Flash and MiMo-V2-Pro?

Ling 3.0 Flash supports 131K tokens and MiMo-V2-Pro supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Ling 3.0 Flash and MiMo-V2-Pro?

Key differences include LLM Stats Score (36.5 vs 35.8), context window (131K vs 1.0M), input pricing ($0.06 vs $1.00/M), licensing (Unknown vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Ling 3.0 Flash and MiMo-V2-Pro?

Ling 3.0 Flash is developed by InclusionAI and MiMo-V2-Pro is developed by Xiaomi.