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MiMo-V2.6-Flash vs o1

MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 20.9. MiMo-V2.6-Flash is 150.0x cheaper per token.

Xiaomi · OpenAI · Updated for 2026

Which is better?

MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 20.9, ranking #29 overall.

On price, MiMo-V2.6-Flash is roughly 150.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

MiMo-V2.6-Flash also accepts a larger context window (1,048,576 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 MiMo-V2.6-Flash

  • overall performance matters — it scores 45.6 and ranks #29 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • cost matters — it's about 150.0x cheaper per token
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Sep 2026
  • you need open weights you can self-host or fine-tune

Choose o1

  • you want predictable pricing at $15.00/M input and $60.00/M output

At a glance

The differences that matter most.

Core performance indexes
45.6
#29
20.9
#194
42.5
#45
21.3
#184
36.7
#21
4.6
#209
Cost, coverage & limits
Benchmark wins
Input price
$0.14 / M
$15.00 / M
Output price
$0.28 / M
$60.00 / M
Context window
1,048,576
200,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
MiMo-V2.6-Flash
o1
24.7#29
12.7#103
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for MiMo-V2.6-Flash · 19 for o1

No common benchmarks found

MiMo-V2.6-Flash and o1don'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

MiMo-V2.6-Flash costs less

For input processing, MiMo-V2.6-Flash ($0.14/1M tokens) is 107.1x cheaper than o1 ($15.00/1M tokens).

For output processing, MiMo-V2.6-Flash ($0.28/1M tokens) is 214.3x cheaper than o1 ($60.00/1M tokens).

In conclusion, o1 is more expensive than MiMo-V2.6-Flash.*

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

Lowest available price from all providers
Tue Sep 22 2026 • llm-stats.com
Xiaomi
MiMo-V2.6-Flash
Input tokens$0.14
Output tokens$0.28
Best providerXiaomi
OpenAI
o1
Input tokens$15.00
Output tokens$60.00
Best providerAzure
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

MiMo-V2.6-Flash accepts 1,048,576 input tokens compared to o1's 200,000 tokens. Only o1 specifies output context (100,000 tokens).

Xiaomi
MiMo-V2.6-Flash
Input1,048,576 tokens
Output- tokens
OpenAI
o1
Input200,000 tokens
Output100,000 tokens
Tue Sep 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

MiMo-V2.6-Flash supports multimodal inputs, whereas o1 does not.

MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

MiMo-V2.6-Flash

Text
Images
Audio
Video

o1

Text
Images
Audio
Video

License

Usage and distribution terms

MiMo-V2.6-Flash is licensed under MIT, while o1 uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

MiMo-V2.6-Flash

MIT

Open weights

o1

Proprietary

Closed source

Release Timeline

When each model was launched

MiMo-V2.6-Flash was released on 2026-09-22, while o1 was released on 2024-12-17.

MiMo-V2.6-Flash is 21 months newer than o1.

MiMo-V2.6-Flash

Sep 22, 2026

0 days ago

1.8yr newer
o1

Dec 17, 2024

1.8 years 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

MiMo-V2.6-Flash is available from Xiaomi. o1 is available from Azure, OpenAI.

MiMo-V2.6-Flash

xiaomi logo
Xiaomi
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M

o1

azure logo
Azure
Input Price:Input: $15.00/1MOutput Price:Output: $60.00/1M
openai logo
OpenAI
Input Price:Input: $15.00/1MOutput Price:Output: $60.00/1M
* 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 MiMo-V2.6-Flash and o1 side-by-side, then vote on the output you prefer.

MiMo-V2.6-Flash
✓ Preferred
o1
Open in Playground

FAQ

Common questions about MiMo-V2.6-Flash vs o1.

Which is better, MiMo-V2.6-Flash or o1?

MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 20.9. MiMo-V2.6-Flash is made by Xiaomi and o1 is made by OpenAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does MiMo-V2.6-Flash compare to o1 in benchmarks?

MiMo-V2.6-Flash scores CyberGym: 95.1%, Terminal-Bench 2.1: 87.6%, OSWorld-Verified: 80.8%, MiMo Cyber Bench: 77.2%, Toolathlon-Verified: 73.6%. o1 scores GSM8k: 97.1%, MATH: 96.4%, GPQA Physics: 92.8%, MMLU: 91.8%, MGSM: 89.3%.

Is MiMo-V2.6-Flash cheaper than o1?

MiMo-V2.6-Flash is 107.1x cheaper for input tokens. MiMo-V2.6-Flash costs $0.14/M input and $0.28/M output via xiaomi. o1 costs $15.00/M input and $60.00/M output via azure.

What are the context window sizes for MiMo-V2.6-Flash and o1?

MiMo-V2.6-Flash supports 1.0M tokens and o1 supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between MiMo-V2.6-Flash and o1?

Key differences include LLM Stats Score (45.6 vs 20.9), context window (1.0M vs 200K), input pricing ($0.14 vs $15.00/M), multimodal support (yes vs no), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes MiMo-V2.6-Flash and o1?

MiMo-V2.6-Flash is developed by Xiaomi and o1 is developed by OpenAI.