Tencent: Hunyuan A13B Instruct vs Qwen: Qwen3 Coder Next: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Tencent: Hunyuan A13B Instruct (tencent/hunyuan-a13b-instruct) vs Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Tencent: Hunyuan A13B Instruct (tencent/hunyuan-a13b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Tencent: Hunyuan A13B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.07/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 Coder Next: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.06/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Tencent: Hunyuan A13B Instruct (API ID: tencent/hunyuan-a13b-instruct) vs Qwen: Qwen3 Coder Next (API ID: qwen/qwen3-coder-next), Qwen: Qwen3 Coder Next offers lower standard input pricing at $0.12/1M tokens ($0.8/1M output), while Qwen: Qwen3 Coder Next leads in cached prefix economics at $0.06/1M tokens (50% discount). Tencent: Hunyuan A13B Instruct 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 | Tencent: Hunyuan A13B Instruct | Qwen: Qwen3 Coder Next | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | tencent/hunyuan-a13b-instruct | qwen/qwen3-coder-next | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.14 / $0.57 | $0.12 / $0.8 | Qwen: Qwen3 Coder Next |
| Cache Read Price (per 1M) | $0.07/1M (50% Off) | $0.06/1M (50% Off) | Qwen: Qwen3 Coder Next |
| Context Window / Max Output | 128K In / 115K Out | 256K In / 230K Out | Qwen: Qwen3 Coder Next |
| P90 First-Token / Edge Latency | 280ms | 280ms | Tencent: Hunyuan A13B Instruct (-0ms) |
Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thou
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only
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 Tencent: Hunyuan A13B Instruct to Qwen: Qwen3 Coder Next
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only Verified P90 latency of 280ms.