GPT-6 Luna vs Ministral 3 (8B Reasoning 2512)
GPT-6 Luna leads the LLM Stats Score 44.5 to 16.5. Ministral 3 (8B Reasoning 2512) is 1.3x cheaper per token.
OpenAI · Mistral AI · Updated for 2026
Which is better?
GPT-6 Luna leads the overall LLM Stats Score 44.5 to 16.5, ranking #41 overall.
On price, Ministral 3 (8B Reasoning 2512) is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-6 Luna also accepts a larger context window (1,050,000 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 GPT-6 Luna
- overall performance matters — it scores 44.5 and ranks #41 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,050,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Ministral 3 (8B Reasoning 2512)
- cost matters — it's about 1.3x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
5 reported for GPT-6 Luna · 4 for Ministral 3 (8B Reasoning 2512)
GPT-6 Luna and Ministral 3 (8B Reasoning 2512)don'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
For input processing, GPT-6 Luna ($0.10/1M tokens) is 1.5x cheaper than Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens).
For output processing, GPT-6 Luna ($0.50/1M tokens) is 3.3x more expensive than Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens).
In conclusion, GPT-6 Luna is more expensive than Ministral 3 (8B Reasoning 2512).*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-6 Luna accepts 1,050,000 input tokens compared to Ministral 3 (8B Reasoning 2512)'s 262,100 tokens. Ministral 3 (8B Reasoning 2512) can generate longer responses up to 262,100 tokens, while GPT-6 Luna is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-6 Luna and Ministral 3 (8B Reasoning 2512) support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-6 Luna
Ministral 3 (8B Reasoning 2512)
License
Usage and distribution terms
GPT-6 Luna is licensed under a proprietary license, while Ministral 3 (8B Reasoning 2512) uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT-6 Luna was released on 2026-09-22, while Ministral 3 (8B Reasoning 2512) was released on 2025-12-04.
GPT-6 Luna is 10 months newer than Ministral 3 (8B Reasoning 2512).
Sep 22, 2026
0 days ago
9mo newerDec 4, 2025
9 months ago
Knowledge Cutoff
When training data ends
GPT-6 Luna has a documented knowledge cutoff of 2026-05-18, while Ministral 3 (8B Reasoning 2512)'s cutoff date is not specified.
We can confirm GPT-6 Luna's training data extends to 2026-05-18, but cannot make a direct comparison without Ministral 3 (8B Reasoning 2512)'s cutoff date.
May 2026
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Provider Availability
GPT-6 Luna is available from OpenAI. Ministral 3 (8B Reasoning 2512) is available from Mistral AI.
GPT-6 Luna
Ministral 3 (8B Reasoning 2512)
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-6 Luna and Ministral 3 (8B Reasoning 2512) side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-6 Luna vs Ministral 3 (8B Reasoning 2512).