Meta: Llama Guard 4 12B vs Cohere: Command A: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama Guard 4 12B (meta-llama/llama-guard-4-12b) vs Cohere: Command A (cohere/command-a). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta: Llama Guard 4 12B (meta-llama/llama-guard-4-12b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama Guard 4 12B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.09/1M) or route via asynchronous Batch API for 50% off standard token rates.
Cohere: Command A (cohere/command-a) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Cohere: Command A: Activate ephemeral prefix caching to slash input costs by 50% (down to $1.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Meta: Llama Guard 4 12B (API ID: meta-llama/llama-guard-4-12b) vs Cohere: Command A (API ID: cohere/command-a), Meta: Llama Guard 4 12B offers lower standard input pricing at $0.18/1M tokens ($0.18/1M output), while Meta: Llama Guard 4 12B leads in cached prefix economics at $0.09/1M tokens (50% discount). Meta: Llama Guard 4 12B achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
Auto-discovered via live global telemetry registry.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | Meta: Llama Guard 4 12B | Cohere: Command A | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-guard-4-12b | cohere/command-a | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.18 / $0.18 | $2.5 / $10 | Meta: Llama Guard 4 12B |
| Cache Read Price (per 1M) | $0.09/1M (50% Off) | $1.25/1M (50% Off) | Meta: Llama Guard 4 12B |
| Context Window / Max Output | 160K In / 16K Out | 250K In / 8K Out | Meta: Llama Guard 4 12B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama Guard 4 12B (-0ms) |
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content
Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to
Real-Time Stack Cost & Hidden-Fee Simulator (MAU / Token / Egress / GMV)
Calibrated with Reddit r/FinOps & r/devops telemetry: Includes Context Caching, Bandwidth Egress, and MoR Tax
🛰️Switch from Meta: Llama Guard 4 12B to Cohere: Command A
Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to Verified P90 latency of 280ms.