GPT-6 Sol vs Aion-3.0
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-6 Sol by OpenAI against Aion-3.0 by AionLabs across real-world coding, reasoning, token economics, and latency.
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GPT-6 Sol
Cost-Efficient GPT-6A GPT-6 tier delivering complex coding and professional work at half the price of GPT-5.6 Sol.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.1%
Aion-3.0
Reasoning DuoFrontier scientific reasoning model designed for biochemical research, formal mathematical logic, and multi-step theorem proving.
$0.50 / 1M
$2.00 / 1M
131.1K tokens
N/A
Key Differences at a Glance
- Aion-3.0 is 4.6x cheaper for API usage (input + output tokens combined).
- GPT-6 Sol has a larger context window (1M vs 131K tokens).
- GPT-6 Sol is faster at 116 tok/s vs 80 tok/s.
| Feature | GPT-6 Sol | Aion-3.0 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 83 |
Provider | OpenAI | AionLabs |
Input Price Cost per million input tokens | $2.00 / 1M | $0.50 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.34 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 128K | 16K |
Throughput Speed Tokens generated per second | 116 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | 82.1% | N/A |
GPQA Diamond Graduate-level reasoning | 80.6% | 63.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.05 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6 Sol · Strengths & Key Advantages
- Roughly 50% cheaper than GPT-5.6 Sol
- Error rate close to half of the previous generation
- Strong agentic coding and professional workflows
- Roughly half the price of GPT-5.6 Sol with better error rates
Considerations & Trade-offs
- Weaker than GPT-6 Astra on the hardest tasks
- Sits close in price to GPT-6.1 Sol, which is stronger
Aion-3.0 · Strengths & Key Advantages
- Strong proficiency in scientific literature, biomedical terminology, and formal logic
- Robust multi-step deductive reasoning capabilities
- Clean structured JSON outputs for laboratory pipeline integration
Considerations & Trade-offs
- Relatively compact 131K context window compared to million-token peers
- Higher latency on heavy reasoning requests
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6 Sol | Aion-3.0 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.00 | $0.16 |
Output Tokens / Task Tokens generated per task | 62K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 7K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Sol | Aion-3.0 |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-sol | aion-3-0 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | AionLabs REST API (OpenAI-Compatible) |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | April 2026 | May 2026 |
| Architecture | Cost-efficient frontier unified reasoning model | Deliberate Scientific Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 1000K window | Flat rate ($0.5/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.1/1M (no extra cache-write fee) | Cached input at $0.05/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | 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 | OpenAI Preparedness Framework Audited & System Card Published | AionLabs 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, vision | text |
| Output Modalities | text | text |
GPT-6 Sol vs Aion-3.0: Quick Answers
Which is more intelligent, GPT-6 Sol or Aion-3.0?
GPT-6 Sol is more intelligent. It scores 93 compared with 83 on the composite Intelligence Index.
Which model is faster?
GPT-6 Sol is faster, generating 116 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Aion-3.0 is cheaper at $0.34 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aion-3.0 has lower latency, with a time to first token of 0.52s compared with 1.16s.
Which model has a larger context window?
GPT-6 Sol has a larger context window, supporting 1M tokens compared with 131.1K tokens.
Verdict
Choose GPT-6 Sol for cost-sensitive frontier work and agentic coding, or choose Aion-3.0 for biotech research, chemical synthesis workflows, and formal logical analysis.
Frequently asked questions
Which is cheaper: GPT-6 Sol or Aion-3.0?+
GPT-6 Sol costs $2/1M input and $10/1M output tokens, while Aion-3.0 costs $0.5/1M input and $2/1M output tokens.
Which model has a larger context window: GPT-6 Sol or Aion-3.0?+
GPT-6 Sol supports 1M tokens whereas Aion-3.0 supports 131K 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.
Sources: OpenAI & AionLabs Official Documentation