Solar Pro vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Solar Pro by Upstage against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Solar Pro
旗舰前沿大模型Upstage 推出的 Solar Pro 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 33K 上下文窗口、100 Token/秒生成速度与 75/100 综合智能指数（每百万 Token $0.25 输入 / $0.25 输出）。
$0.25 / 1M
$0.25 / 1M
32.8K tokens
N/A
GPT-6 Astra
原生语音与音频大模型OpenAI 推出的 GPT-6 Astra 是一款专注于超自然语音合成、实时音频理解与低延迟语音交互的大模型，具备 272K 上下文窗口、100 Token/秒生成速度与 96/100 综合智能指数（每百万 Token $10 输入 / $50 输出）。
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
核心差异一览
- Solar Pro is 96.5x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 33K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 100 tok/s.
| Feature | Solar Pro | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 96 |
Provider | Upstage | OpenAI |
Input Price Cost per million input tokens | $0.25 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.25 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.09 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 100 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $1.00 / 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.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
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
- 提供 33K 上下文窗口与最高 4K 输出容量，核心基准表现优异（MMLU-Pro 73.5% · Arena Elo 1260）
- 高达 100 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 380 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.25 输入 / $0.25 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
GPT-6 Astra · Strengths & Key Advantages
- 提供 272K 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 82.4% · GPQA Diamond 80.9%）
- 高达 100 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 950 毫秒
- 极具竞争力的 API 定价（每百万 Token $10 输入 / $50 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Solar Pro | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $2.15 |
Output Tokens / Task Tokens generated per task | 10K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Solar Pro | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | solar-pro | gpt-6-astra |
| API Protocol | Upstage REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-10 | 2026-09 |
| Knowledge Cutoff | June 2024 | June 2026 |
| Architecture | Depth-Upscaled Dense Language Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 22B (Depth-Upscaled) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.25/1M in) across full 33K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.025/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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, vision, audio |
| Output Modalities | text | text, audio |
Solar Pro vs GPT-6 Astra: 常见问题解答 (FAQ)
Which is more intelligent, Solar Pro or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 75 on the composite Intelligence Index.
Which model is cheaper to run?
Solar Pro is cheaper at $0.09 per 1M tokens blended, compared with $7.70 (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 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 32.8K tokens.
综合结论
Choose Solar Pro for 面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。, or choose GPT-6 Astra for 专注于超自然语音合成、实时音频理解与低延迟语音交互的任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。.
常见问题 (FAQ)
Which is cheaper: Solar Pro or GPT-6 Astra?+
Solar Pro costs $0.25/1M input and $0.25/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Solar Pro or GPT-6 Astra?+
Solar Pro supports 33K tokens whereas GPT-6 Astra supports 272K tokens.
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: Upstage & OpenAI Official Documentation