Llama 4 (Meta) vs Microsoft MAI-Transcribe-1
Which one should you pick? Here's the full breakdown.
Llama 4 (Meta)
Meta's open-weights flagship family -- Scout (10M context), Maverick (multimodal 400B MoE), Behemoth in preview
Microsoft MAI-Transcribe-1
Microsoft's first in-house speech-recognition model -- launched 2026-04-02. #1 on FLEURS WER overall, #1 by FLEURS WER in 11 of the top 25 global languages. Beats Whisper-large-v3, Scribe v2, GPT-Transcribe, Gemini 3.1 Flash-Lite. $0.36/hour of audio on Azure Foundry
| Category | Llama 4 (Meta) | Microsoft MAI-Transcribe-1 |
|---|---|---|
| Ease of Use | 5.0 | 6.0 |
| Output Quality | 8.5 | 9.5 |
| Value | 9.0 | 9.0 |
| Features | 9.0 | 7.0 |
| Overall | 7.9 | 7.9 |
Pricing Comparison
| Feature | Llama 4 (Meta) | Microsoft MAI-Transcribe-1 |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0.36 |
Benchmark Head-to-Head
Llama 4 Maverick (17B/400B MoE) benchmarks — Microsoft MAI-Transcribe-1 has no published benchmarks
| Benchmark | Description | Score |
|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 80.5% |
| GPQA Diamond | Graduate-level science questions | 69.8% |
| HumanEval | Python code generation | 88% |
| MMMU (multimodal) | 73.4% |
Which Should You Pick?
Pick Llama 4 (Meta) if...
- ✓More features (9 vs 7)
Developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (Scout), or multimodal (Maverick). Safe default choice given the ecosystem.
Visit Llama 4 (Meta)Pick Microsoft MAI-Transcribe-1 if...
- ✓Higher output quality (9.5 vs 8.5)
- ✓Easier to use (6 vs 5)
Developers and enterprises who need best-in-class multilingual speech-to-text for high-volume use cases (meeting recording pipelines, call-center transcription, accessibility captioning at scale, multilingual audio indexing). Especially relevant for Azure shops already on Microsoft infrastructure.
Visit Microsoft MAI-Transcribe-1Our Verdict
Llama 4 (Meta) and Microsoft MAI-Transcribe-1 are extremely close overall. Your choice comes down to specific needs -- Llama 4 (Meta) is better for developers and teams who need a permissively-licensed open-weights model with strong tooling, long context (scout), or multimodal (maverick), while Microsoft MAI-Transcribe-1 works best for developers and enterprises who need best-in-class multilingual speech-to-text for high-volume use cases (meeting recording pipelines, call-center transcription, accessibility captioning at scale, multilingual audio indexing).