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GPT-5.3 Chat vs MiMo-V2.6-Flash

MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 29.5. MiMo-V2.6-Flash is 27.5x cheaper per token.

OpenAI · Xiaomi · Updated for 2026

Which is better?

MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 29.5, ranking #29 overall.

On price, MiMo-V2.6-Flash is roughly 27.5x 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 GPT-5.3 Chat

  • you want predictable pricing at $1.75/M input and $14.00/M output

Choose MiMo-V2.6-Flash

  • overall performance matters — it scores 45.6 and ranks #29 on LLM Stats
  • cost matters — it's about 27.5x 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

At a glance

The differences that matter most.

Core performance indexes
29.5
#130
45.6
#29
Cost, coverage & limits
Benchmark wins
Input price
$1.75 / M
$0.14 / M
Output price
$14.00 / M
$0.28 / M
Context window
128,000
1,048,576

Individual benchmarks

2 reported for GPT-5.3 Chat · 16 for MiMo-V2.6-Flash

No common benchmarks found

GPT-5.3 Chat and MiMo-V2.6-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

MiMo-V2.6-Flash costs less

For input processing, GPT-5.3 Chat ($1.75/1M tokens) is 12.5x more expensive than MiMo-V2.6-Flash ($0.14/1M tokens).

For output processing, GPT-5.3 Chat ($14.00/1M tokens) is 50.0x more expensive than MiMo-V2.6-Flash ($0.28/1M tokens).

In conclusion, GPT-5.3 Chat 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
OpenAI
GPT-5.3 Chat
Input tokens$1.75
Output tokens$14.00
Best providerOpenAI
Xiaomi
MiMo-V2.6-Flash
Input tokens$0.14
Output tokens$0.28
Best providerXiaomi
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 GPT-5.3 Chat's 128,000 tokens. Only GPT-5.3 Chat specifies output context (16,384 tokens).

OpenAI
GPT-5.3 Chat
Input128,000 tokens
Output16,384 tokens
Xiaomi
MiMo-V2.6-Flash
Input1,048,576 tokens
Output- tokens
Tue Sep 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both GPT-5.3 Chat and MiMo-V2.6-Flash support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GPT-5.3 Chat

Text
Images
Audio
Video

MiMo-V2.6-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

GPT-5.3 Chat is licensed under a proprietary license, while MiMo-V2.6-Flash uses MIT.

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

GPT-5.3 Chat

Proprietary

Closed source

MiMo-V2.6-Flash

MIT

Open weights

Release Timeline

When each model was launched

GPT-5.3 Chat was released on 2026-03-04, while MiMo-V2.6-Flash was released on 2026-09-22.

MiMo-V2.6-Flash is 7 months newer than GPT-5.3 Chat.

GPT-5.3 Chat

Mar 4, 2026

6 months ago

MiMo-V2.6-Flash

Sep 22, 2026

0 days ago

6mo newer

Knowledge Cutoff

When training data ends

GPT-5.3 Chat has a documented knowledge cutoff of 2025-08-31, while MiMo-V2.6-Flash's cutoff date is not specified.

We can confirm GPT-5.3 Chat's training data extends to 2025-08-31, but cannot make a direct comparison without MiMo-V2.6-Flash's cutoff date.

GPT-5.3 Chat

Aug 2025

MiMo-V2.6-Flash

Provider Availability

GPT-5.3 Chat is available from OpenAI. MiMo-V2.6-Flash is available from Xiaomi.

GPT-5.3 Chat

openai logo
OpenAI
Input Price:Input: $1.75/1MOutput Price:Output: $14.00/1M

MiMo-V2.6-Flash

xiaomi logo
Xiaomi
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
* 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 GPT-5.3 Chat and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.

GPT-5.3 Chat
✓ Preferred
MiMo-V2.6-Flash
Open in Playground

FAQ

Common questions about GPT-5.3 Chat vs MiMo-V2.6-Flash.

Which is better, GPT-5.3 Chat or MiMo-V2.6-Flash?

MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 29.5. GPT-5.3 Chat is made by OpenAI and MiMo-V2.6-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 GPT-5.3 Chat compare to MiMo-V2.6-Flash in benchmarks?

GPT-5.3 Chat scores HealthBench: 54.1%, HealthBench Hard: 25.9%. 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%.

Is GPT-5.3 Chat cheaper than MiMo-V2.6-Flash?

MiMo-V2.6-Flash is 12.5x cheaper for input tokens. GPT-5.3 Chat costs $1.75/M input and $14.00/M output via openai. MiMo-V2.6-Flash costs $0.14/M input and $0.28/M output via xiaomi.

What are the context window sizes for GPT-5.3 Chat and MiMo-V2.6-Flash?

GPT-5.3 Chat supports 128K tokens and MiMo-V2.6-Flash 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 GPT-5.3 Chat and MiMo-V2.6-Flash?

Key differences include LLM Stats Score (29.5 vs 45.6), context window (128K vs 1.0M), input pricing ($1.75 vs $0.14/M), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes GPT-5.3 Chat and MiMo-V2.6-Flash?

GPT-5.3 Chat is developed by OpenAI and MiMo-V2.6-Flash is developed by Xiaomi.