Upstage
Solar Pro 4, Solar Pro 3, Solar Pro 2 — Korean frontier models with strong multilingual performance
Upstage is a Korean AI company offering the Solar model family via API. Solar Pro 4 is the current flagship, with Solar Pro 3 and Solar Pro 2 available at lower price points. The Solar models are known for strong multilingual performance, particularly in Korean and English, and competitive pricing relative to their capability tier. Pricing is published at upstage.ai/pricing.
Korean-built Solar models with strong multilingual performance and competitive frontier-tier pricing — Solar Pro 4 at $0.30/$1.20 per 1M tokens.
- Solar Pro 4: competitive frontier-tier pricing at $0.30/$1.20 per 1M tokens
- Strong multilingual performance — Korean and English
- Three-tier model family: Solar Pro 4, 3, and 2 for different cost/quality trade-offs
- OpenAI-compatible API
- Smaller global ecosystem than OpenAI or Anthropic
- Limited model variety beyond the Solar family
- Less documentation in English than major US providers
Upstage Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
solar-pro-3 Solar Pro | Chat | efficient | $0.15/ $0.60 in / out per 1M EST Oct 2026 | — | — | — | 33K | — | ||
solar-pro-2 Solar Pro | Chat | efficient | $0.15/ $0.60 in / out per 1M EST Oct 2026 | — | — | — | 33K | — | ||
solar-pro-4 Solar Pro | Chat | advanced | $0.30/ $1.20 in / out per 1M EST Oct 2026 | — | — | — | 33K | — |
Upstage — Frequently Asked Questions (FAQs)
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Upstage is a proprietary AI lab that develops and hosts its own large language models. Upstage is a Korean AI company offering the Solar model family via API. Solar Pro 4 is the current flagship, with Solar Pro 3 and Solar Pro 2 available at lower price points. The Solar models are known for strong multilingual performance, particularly in Korean and English, and competitive pricing relative to their capability tier. Pricing is published at upstage.ai/pricing. Common use cases include Chat, Multilingual, Korean NLP, Cost-efficiency. Headquartered in Seoul, South Korea, founded 2020. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Upstage vs other LLM providers
Upstage competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Korean-built Solar models with strong multilingual performance and competitive frontier-tier pricing — Solar Pro 4 at $0.30/$1.20 per 1M tokens. Use the LLM provider comparison tool to see Upstage token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Upstage?
Upstage stands out because: Korean-built Solar models with strong multilingual performance and competitive frontier-tier pricing — Solar Pro 4 at $0.30/$1.20 per 1M tokens.. The API currently offers 3 models, spanning 1 advanced, 2 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Upstage token pricing
Upstage charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.15/1M tokens. Prices shown are sourced from Upstage's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Upstage context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Upstage's largest context window is 33K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Upstage makes sense
Managed inference APIs like Upstage eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Upstage API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Upstage token price data is available in the LLM price history charts.
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