Upstage Releases Solar Mini 4: 35B MoE Model with 524K Context at $0.05/$0.20 per Million Tokens

Upstage Releases Solar Mini 4

Upstage has released Solar Mini 4, a compact mixture-of-experts (MoE) language model built for cost-sensitive, agentic workloads. The model is now available through OpenRouter and Upstage's API.

Specifications

Solar Mini 4 uses a 35-billion-parameter MoE architecture with only 3 billion parameters active per forward pass, a design intended to keep inference costs and latency low while retaining broad model capacity. It ships with a 524,288-token (524K) context window, positioning it for long-document and long-horizon agentic tasks.

According to Upstage, the model offers fluent Korean alongside strong English and Japanese performance, continuing the company's focus on multilingual support centered on Korean-language use cases.

Pricing

Solar Mini 4 is listed at $0.05 per 1 million input tokens and $0.20 per 1 million output tokens on OpenRouter — a promotional 50% discount off the standard listed rate of $0.10 / $0.40 per 1 million tokens. Cache-read pricing is listed at $0.01 per 1M tokens (input) and $0.005 per 1M tokens (output) under the discounted tier.

OpenRouter data shows throughput up to 95 tokens per second and P50 latency as low as 0.28 seconds across providers, with 100% uptime recorded over the most recent 24-hour and 3-day windows.

Deployment and Access

Upstage supports bring-your-own-key (BYOK) access with zero-data-retention (ZDR) enforcement. Organizations wanting ZDR enabled on their Upstage Console API key must contact Upstage directly to activate it; without this, standard data-retention terms apply.

Model Family Context

Solar Mini 4 joins two other current Upstage releases available on OpenRouter: Solar Pro 4, which shares the same 524K context window and is priced at $0.09 / $0.36 per 1M tokens for office- and coding-oriented workflows, and Solar Pro 3, a larger 102B-parameter MoE model (12B active) with a 131K context window priced at $0.15 / $0.60 per 1M tokens. Solar Mini 4 sits below both in parameter count and price, targeting lighter-weight, higher-volume use cases.

No benchmark scores (e.g., MMLU, HumanEval) or a training data cutoff date have been disclosed by Upstage for this release.

What This Means

Solar Mini 4's combination of a 524K context window and 3B active parameters puts it in a niche aimed squarely at high-throughput agentic pipelines — think document retrieval, multi-step tool use, and long-running conversational agents — where per-token cost and latency matter more than raw benchmark ceiling. The MoE design (35B total, 3B active) is a common strategy for keeping inference costs down while preserving some of the capacity benefits of a larger dense model, though without published benchmark scores it's not possible to independently verify how it compares to similarly priced competitors like smaller Gemini, Llama, or Qwen variants. Upstage's continued emphasis on Korean-language fluency, paired with English and Japanese support, signals the company is prioritizing regional strength over head-to-head competition with frontier labs on general-purpose leaderboards. The ZDR/BYOK requirement also suggests enterprise and compliance-focused customers are a target segment.