Microsoft
FrogMini is built on the Qwen3-14B transformer architecture with a maximum context length of 64k tokens. The model uses multi-turn debugging workflows and complex code reasoning. Unlike general-purpose LLMs, FrogMini is specialized for software engineering tasks.
Running this yourself: can likely run on your own machine.
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41.4
Quality Score
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Arena ELO
425.0K
Parameters
131K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
17 of 22 public signals
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1.1K
Downloads
71
Likes
Jan 2026
Released
5/5 signals
3/4 signals
3/5 signals
3/4 signals
3/4 signals
Parameters
425.0K
Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Gaps we are still tracking
Metadata sources
Launches
3
Benchmarks
4
Research
1
General
5
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
SWE-Bench Verified resolved rate 45.0
More ways to discover, access, and build with MAI models. MAI models are now available through Vercel, giving developers another way to bring Microsoft AI models into the products and experiences they’re building. This includes our existing MAI models plus today’s newest
View sourceThrilled to announce by popular demand MAI-Code-1-Flash is now generally available for GitHub Copilot Business and GitHub Copilot Enterprise - fast, efficient, and custom designed to help you build more for less on @github
View sourceSWE-Bench Verified resolved rate 45.0
View source
More ways to discover, access, and build with MAI models. MAI models are now available through Vercel, giving developers another way to bring Microsoft AI models into the products and experiences they’re building. This includes our existing MAI models plus today’s newest
Say how you feel. Get a poem that meets you there. Ode connects you with a poem for your moment. Guided by William Sieghart. Powered by Microsoft AI models. Try it out here: https://t.co/egu88NTf9C https://t.co/jQ3qm9sKnz
Thrilled to announce by popular demand MAI-Code-1-Flash is now generally available for GitHub Copilot Business and GitHub Copilot Enterprise - fast, efficient, and custom designed to help you build more for less on @github
We shipped a new coding model built for your everyday dev work. MAI-Code-1-Flash is fast, token-efficient, and trained inside real GitHub Copilot environments. It plans, builds, runs, and tests. All from Copilot Chat in VS Code. Watch it go from a single frost banner to a full
MAI-Image-2.5 ranked #2 for text-to-image and #3 for image editing on @ArtificialAnlys - showing strong performance across both generation and precise image edits. From rainy-window blur to a clear, usable street scene, while preserving object consistency, lighting, https://t.co/q8MFDrr8uB
The Muon optimizer incurs a significant overhead cost due to its cubic-time Newton-Schulz orthogonalization step. When weights are sharded, communication overhead compounds this computational cost, eroding the benefits of Muon in many settings. We present Dion3, a revision of Muon that targets this overhead at every level of the stack. Our Gram Newton-Schulz algorithm reduces the FLOP cost of orthogonalization, our CuteDSL kernels accelerate it by exploiting symmetry, and our megabatching strategy reduces communication overhead. Moreover, we propose a simple change to the update rule that cuts costs even further: selecting only a fraction of the momentum matrix's rows to orthogonalize at each step. This update rule improves on Dion (another "compressed" version of Muon), in both speed and performance. Overall, Dion3 matches or improves on the loss achieved by Muon but reduces optimizer step time by up to 6x. Dion3 is available via the dion package (https://github.com/microsoft/dion) as a drop-in replacement for Muon.
Microsoft published benchmark or leaderboard evidence for FrogMini-14B-2510.
Microsoft published benchmark or leaderboard evidence for FrogMini-14B-2510.