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Ling 3.0 Flash Fin vs MiMo-V2-Flash

Ling 3.0 Flash Fin leads the LLM Stats Score 43.0 to 30.7. Ling 3.0 Flash Fin is 1.7x cheaper per token.

InclusionAI · Xiaomi · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.0 to 30.7, ranking #44 overall.

On price, Ling 3.0 Flash Fin is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Ling 3.0 Flash Fin also accepts a larger context window (262,144 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 Fin

  • overall performance matters — it scores 43.0 and ranks #44 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • cost matters — it's about 1.7x cheaper per token
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Sep 2026

Choose MiMo-V2-Flash

  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Core performance indexes
43.0
#44
30.7
#117
44.2
#35
30.8
#113
29.2
#37
8.1
#134
Cost, coverage & limits
Benchmark wins
Input price
$0.06 / M
$0.10 / M
Output price
$0.18 / M
$0.30 / M
Context window
262,144
256,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Ling 3.0 Flash Fin
MiMo-V2-Flash
20.4#58
6.3#147
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

6 reported for Ling 3.0 Flash Fin · 15 for MiMo-V2-Flash

No common benchmarks found

Ling 3.0 Flash Fin and MiMo-V2-Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Ling 3.0 Flash Fin costs less

For input processing, Ling 3.0 Flash Fin ($0.06/1M tokens) is 1.7x cheaper than MiMo-V2-Flash ($0.10/1M tokens).

For output processing, Ling 3.0 Flash Fin ($0.18/1M tokens) is 1.7x cheaper than MiMo-V2-Flash ($0.30/1M tokens).

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

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

Lowest available price from all providers
Sun Sep 20 2026 • llm-stats.com
InclusionAI
Ling 3.0 Flash Fin
Input tokens$0.06
Output tokens$0.18
Best providerDeepinfra
Xiaomi
MiMo-V2-Flash
Input tokens$0.10
Output tokens$0.30
Best providerXiaomi
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

185.0B diff

MiMo-V2-Flash has 185.0B more parameters than Ling 3.0 Flash Fin, making it 149.2% larger.

InclusionAI
Ling 3.0 Flash Fin
124.0Bparameters
Xiaomi
MiMo-V2-Flash
309.0Bparameters
124.0B
Ling 3.0 Flash Fin
309.0B
MiMo-V2-Flash

Context Window

Maximum input and output token capacity

Ling 3.0 Flash Fin accepts 262,144 input tokens compared to MiMo-V2-Flash's 256,000 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while MiMo-V2-Flash is limited to 16,384 tokens.

InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Xiaomi
MiMo-V2-Flash
Input256,000 tokens
Output16,384 tokens
Sun Sep 20 2026 • llm-stats.com

Release Timeline

When each model was launched

Ling 3.0 Flash Fin was released on 2026-09-03, while MiMo-V2-Flash was released on 2025-12-16.

Ling 3.0 Flash Fin is 9 months newer than MiMo-V2-Flash.

Ling 3.0 Flash Fin

Sep 3, 2026

2 weeks ago

8mo newer
MiMo-V2-Flash

Dec 16, 2025

9 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 Fin is available from DeepInfra. MiMo-V2-Flash is available from Xiaomi.

Ling 3.0 Flash Fin

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

MiMo-V2-Flash

xiaomi logo
Xiaomi
Input Price:Input: $0.10/1MOutput Price:Output: $0.30/1M
* 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 Fin and MiMo-V2-Flash side-by-side, then vote on the output you prefer.

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

FAQ

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

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

Ling 3.0 Flash Fin leads the LLM Stats Score 43.0 to 30.7. Ling 3.0 Flash Fin is made by InclusionAI and MiMo-V2-Flash 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 Fin compare to MiMo-V2-Flash in benchmarks?

Ling 3.0 Flash Fin scores SpreadSheetBench-v1: 86.5%, Finance Agent v1.1: 69.2%, Finance Agent v2: 59.8%, Tau3 Banking: 41.0%, APEX-Agents: 29.2%. MiMo-V2-Flash scores AIME 2025: 94.1%, Arena-Hard v2: 86.2%, MMLU-Pro: 84.9%, HMMT 2025: 84.4%, GPQA: 83.7%.

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

Ling 3.0 Flash Fin is 1.7x cheaper for input tokens. Ling 3.0 Flash Fin costs $0.06/M input and $0.18/M output via deepinfra. MiMo-V2-Flash costs $0.10/M input and $0.30/M output via xiaomi.

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

Ling 3.0 Flash Fin supports 262K tokens and MiMo-V2-Flash supports 256K 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 Fin and MiMo-V2-Flash?

Key differences include LLM Stats Score (43.0 vs 30.7), context window (262K vs 256K), input pricing ($0.06 vs $0.10/M), licensing (Unknown vs MIT). See the full comparison above for benchmark-by-benchmark results.

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

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