Reflection: Beam 1.0 501B A23B vs Meta: Llama 3.3 70B Instruct: Live Promos, Pricing, Hidden Fees & Latency
Reflection: Beam 1.0 501B A23B (reflection/beam-1.0-501b-a23b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Reflection: Beam 1.0 501B A23B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
Meta: Llama 3.3 70B Instruct (meta-llama/llama-3.3-70b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 3.3 70B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.05/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Reflection: Beam 1.0 501B A23B (API ID: reflection/beam-1.0-501b-a23b) vs Meta: Llama 3.3 70B Instruct (API ID: meta-llama/llama-3.3-70b-instruct), Meta: Llama 3.3 70B Instruct offers lower standard input pricing at $0.1/1M tokens ($0.32/1M output), while Meta: Llama 3.3 70B Instruct leads in cached prefix economics at $0.05/1M tokens (50% discount). Reflection: Beam 1.0 501B A23B 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 | Reflection: Beam 1.0 501B A23B | Meta: Llama 3.3 70B Instruct | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | reflection/beam-1.0-501b-a23b | meta-llama/llama-3.3-70b-instruct | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1 / $3 | $0.1 / $0.32 | Meta: Llama 3.3 70B Instruct |
| Cache Read Price (per 1M) | $0.5/1M (50% Off) | $0.05/1M (50% Off) | Meta: Llama 3.3 70B Instruct |
| Context Window / Max Output | 1024K In / 922K Out | 128K In / 16K Out | Reflection: Beam 1.0 501B A23B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Reflection: Beam 1.0 501B A23B (-0ms) |
Beam 1.0 is Reflection's open-weight reasoning model for coding and long-horizon agentic workflows. It uses a sparse mixture-of-experts architecture with 501B total and 23B active
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text on
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 Reflection: Beam 1.0 501B A23B to Meta: Llama 3.3 70B Instruct
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text on Verified P90 latency of 280ms.