Llama 4 (Meta) vs Cohere Transcribe
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
Cohere Transcribe
Cohere's first audio model -- launched 2026-03-26 under Apache 2.0, 2B parameters, #1 on Hugging Face Open ASR Leaderboard (5.42 avg WER), 14 enterprise-critical languages. Free API with rate limits; Model Vault for production
| Category | Llama 4 (Meta) | Cohere Transcribe |
|---|---|---|
| Ease of Use | 5.0 | 7.0 |
| Output Quality | 8.5 | 9.0 |
| Value | 9.0 | 9.0 |
| Features | 9.0 | 7.0 |
| Overall | 7.9 | 8.0 |
Pricing Comparison
| Feature | Llama 4 (Meta) | Cohere Transcribe |
|---|---|---|
| Free Tier | Yes | Yes |
| Starting Price | $0 | $0 |
Benchmark Head-to-Head
Llama 4 Maverick (17B/400B MoE) benchmarks — Cohere Transcribe 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 Cohere Transcribe if...
- ✓Easier to use (7 vs 5)
Enterprise teams transcribing English, European, and major APAC languages at scale who want open weights they can self-host, fine-tune, or deploy on-prem. The Apache 2.0 license removes a major procurement blocker compared to proprietary ASR, and the accuracy tier is now best-in-class for open models.
Visit Cohere TranscribeOur Verdict
Llama 4 (Meta) and Cohere Transcribe 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 Cohere Transcribe works best for enterprise teams transcribing english, european, and major apac languages at scale who want open weights they can self-host, fine-tune, or deploy on-prem.