Mercury 2.5 Preview: 1,107 Tokens per Second on Standard GPUs at $0.04 Input
Inception released Mercury 2.5 Preview on August 31, 2026, available on OpenRouter as the sole provider. The model is a diffusion LLM (dLLM) — instead of generating one token at a time in a left-to-right pass, it produces and refines multiple tokens in parallel across the full output.
Numbers
- Throughput: 1,107 tokens/second on standard GPUs
- Context: 260K tokens
- Input price: $0.04 / 1M tokens
- Output price: $0.15 / 1M tokens
- Cache read price: $0.004 / 1M tokens (80x cheaper than output)
- Availability: OpenRouter (
inception/mercury-2.5-preview)
The cache read discount at $0.004/M is the highest-leverage pricing lever for pipelines that repeatedly prompt with fixed system prompts or shared context blocks.
Architecture
The diffusion approach produces meaningfully different latency characteristics than autoregressive models. In a standard transformer, output token N cannot begin until token N-1 is complete. Mercury 2.5 distributes generation across positions simultaneously, then iterates to refine quality. The practical result is that throughput scales differently from latency: the model is faster at generating long outputs than short ones when normalized per-token, because fixed overhead per forward pass is amortized across more tokens.
Inception calls Mercury 2.5 a reasoning LLM. The 1,107 tok/s figure applies to standard GPU infrastructure — not custom silicon, not Groq or Cerebras.
Market Position
Mercury 2 appeared on Artificial Analysis speed rankings earlier this summer as among the fastest available models. Mercury 2.5 extends that lead with a hardware-agnostic claim: 1,107 tok/s without specialized inference accelerators.
At $0.15/M output, Mercury 2.5 is positioned below frontier reasoning models. GPT-5.5 costs roughly $15/M output; Claude Fable 5.1 is higher. Mercury 2.5 at $0.15/M is closer to commodity flash tiers — Gemini Flash pricing — but with throughput characteristics typically associated with purpose-built inference hardware.
The tradeoff against autoregressive frontier models is quality. Mercury 2.5 Preview is not benchmarked against SWE-bench Verified or Terminal-Bench in the available launch materials. The model is positioned for latency-sensitive, high-volume applications where throughput and cost-per-token dominate quality margins.
Mercury 2.5’s 260K context window is competitive with models in its price tier but below the 1M+ context offerings from Gemini and Qwen at similar price points.