
DeepRoute API
Affordable DeepSeek & Qwen API cheaper than official pricing
What it does
API relay for DeepSeek & Qwen from US servers. OpenAI-compatible. Pricing per 1M tokens: - DeepSeek V4 Flash: $0.16/$0.32 - DeepSeek R1: $0.63/$2.52 - Qwen3.6 Flash: $0.30/$1.79 - QwQ Plus: $0.95/$2.85 Free trial, no CC, PayPal accepted. ``` from openai import OpenAI client = OpenAI(base_url="https://deeproute-api.duckdns.org/v1", api_key="sk-...") ``` Register: https://deeproute-api.duckdns.org/register
Does the same job
all alternatives →- SSsllm – Split a GPU node with other developers, unlimited tokensApr 2026 · sllm.cloud · ▲188
Running DeepSeek V3 (685B) requires 8×H100 GPUs which is about $14k/month. Most developers only need 15-25 tok/s. sllm lets you join a cohort of developers sharing a dedicated node. You reserve a spot with your card, and nobody is charged until the cohort fills. Prices start at $5/mo for smaller models. The LLMs are completely private (we don't log any traffic). The API is OpenAI-compatible (we run vLLM), so you just swap the base URL. Currently offering a few models.
QuickSilver ProApr 2026 · quicksilverpro.io · ▲1OpenAI-compatible API for DeepSeek V3, R1, and Qwen3.5-35B




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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Parallel agents, diff reviewer, and multi-model comparisons
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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