Solar Pro vs Yi-Large
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Solar Pro by Upstage and Yi-Large by 01.AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Solar Pro holds a notable pricing advantage for token ingestion. On output generation, Solar Pro is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 33K tokens. In latency-sensitive production environments, Solar Pro leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Solar Pro
旗舰 frontier 大模型Upstage 推出的 Solar Pro 人工智能模型，具备 32K 上下文窗口，输入价格为 $0.25/百万 Token，输出价格为 $0.25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.25 / 1M
$0.25 / 1M
32.8K tokens
N/A
Yi-Large
旗舰 frontier 大模型01.AI 推出的 Yi-Large 人工智能模型，具备 32K 上下文窗口，输入价格为 $2.5/百万 Token，输出价格为 $2.5/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.50 / 1M
$2.50 / 1M
32.8K tokens
N/A
核心差异一览
- Solar Pro is 10.0x cheaper for API usage (input + output tokens combined).
- Solar Pro is faster at 100 tok/s vs 75 tok/s.
| Feature | Solar Pro | Yi-Large |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 79 |
Provider | Upstage | 01.AI |
Input Price Cost per million input tokens | $0.25 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $0.25 / 1M | $2.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.09 / 1M | $0.93 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 100 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | N/A | 52% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.25 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Solar Pro · Strengths & Key Advantages
- 由 Upstage 研发的高精度 Depth-Upscaled Dense Language Transformer 架构，支持 32K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.25/1M，输出 $0.25/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Yi-Large · Strengths & Key Advantages
- 由 01.AI 研发的高精度 Dense Bilingual Foundation Transformer 架构，支持 32K 上下文窗口
- 极具竞争力的 API 定价（输入 $2.5/1M，输出 $2.5/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Solar Pro | Yi-Large |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $0.15 |
Output Tokens / Task Tokens generated per task | 10K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Solar Pro | Yi-Large |
|---|---|---|
| Official API Model ID (SDK) | solar-pro | yi-large |
| API Protocol | Upstage REST API (OpenAI-Compatible) | 01.AI REST API (OpenAI-Compatible) |
| Release Date | 2024-10 | 2024-05 |
| Knowledge Cutoff | June 2024 | March 2024 |
| Architecture | Depth-Upscaled Dense Language Transformer | Dense Bilingual Foundation Transformer |
| Parameters | 22B (Depth-Upscaled) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.25/1M in) across full 33K window | Flat rate ($2.5/1M in) across full 33K window |
| Prompt Cache Policy & TTL | Cached input at $0.025/1M tokens | Cached input at $0.25/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Upstage Production Safety & Alignment Verified | 01.AI Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text |
| Output Modalities | text | text |
Solar Pro vs Yi-Large: 常见问题解答 (FAQ)
Which is more intelligent, Solar Pro or Yi-Large?
Yi-Large is more intelligent. It scores 79 compared with 75 on the composite Intelligence Index.
Which model is faster?
Solar Pro is faster, generating 100 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Solar Pro is cheaper at $0.09 per 1M tokens blended, compared with $0.93 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Solar Pro has lower latency, with a time to first token of 0.38s compared with 0.51s.
综合结论
Verdict & Model Selection Recommendation: Choose Solar Pro if your priority is Korean enterprise applications, customer service bots, and specialized legal translation. Its performance in demanding tasks justifies its operational token cost. Choose Yi-Large if your workflow prioritizes Cross-border bilingual corporate communication, translation, and high-quality Chinese prose. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Solar Pro compare to Yi-Large in API token pricing and cost per token?+
Solar Pro costs $0.25 per 1M input tokens and $0.25 per 1M output tokens, whereas Yi-Large costs $2.50 input and $2.50 output. Overall, Solar Pro is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Solar Pro or Yi-Large?+
Both Solar Pro and Yi-Large offer an identical context window of 33K tokens (~25k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Solar Pro or Yi-Large?+
Solar Pro scores at the frontier of coding benchmarks, while Yi-Large scores competitively for software generation. For complex autonomous debugging and agentic engineering, Solar Pro provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Solar Pro and Yi-Large?+
Solar Pro is optimized for low-latency direct generation. In contrast, Yi-Large focuses on standard fast inference.
How does inference speed (tokens per second) compare between Solar Pro and Yi-Large?+
Solar Pro generates approximately 100 tokens per second, while Yi-Large delivers 75 tokens per second. In latency-sensitive production environments, Solar Pro provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Solar Pro and Yi-Large to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Solar Pro to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Yi-Large. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs