Mistral: Mistral Medium 3.5 (batch) vs DeepSeek: DeepSeek V3.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Mistral: Mistral Medium 3.5 (batch) (mistralai/mistral-medium-3-5:batch) vs DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Mistral: Mistral Medium 3.5 (batch) (mistralai/mistral-medium-3-5:batch) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Mistral: Mistral Medium 3.5 (batch): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.375/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1) — Automatic Disk KV Cache ($0.125/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Mistral: Mistral Medium 3.5 (batch) (API ID: mistralai/mistral-medium-3-5:batch) vs DeepSeek: DeepSeek V3.1 (API ID: deepseek/deepseek-chat-v3.1), DeepSeek: DeepSeek V3.1 offers lower standard input pricing at $0.25/1M tokens ($0.95/1M output), while DeepSeek: DeepSeek V3.1 leads in cached prefix economics at $0.125/1M tokens (50% discount). Mistral: Mistral Medium 3.5 (batch) 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 | Mistral: Mistral Medium 3.5 (batch) | DeepSeek: DeepSeek V3.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | mistralai/mistral-medium-3-5:batch | deepseek/deepseek-chat-v3.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.75 / $3.75 | $0.25 / $0.95 | DeepSeek: DeepSeek V3.1 |
| Cache Read Price (per 1M) | $0.375/1M (50% Off) | $0.125/1M (50% Off) | DeepSeek: DeepSeek V3.1 |
| Context Window / Max Output | 256K In / 205K Out | 160K In / 32K Out | Mistral: Mistral Medium 3.5 (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | Mistral: Mistral Medium 3.5 (batch) (-0ms) |
Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, a
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba
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 Mistral: Mistral Medium 3.5 (batch) to DeepSeek: DeepSeek V3.1
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba Verified P90 latency of 280ms.