GPT-6 Astra (max)
OpenAI • text#3 of 616 in intelligence
GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon...
Specifications
Context Window
1.1M tokens
Input Price/1M
$10.00
#713 of 744
Output Price/1M
$50.00
Parameters
—
Speed
54 tok/s
#271 of 602
Latency (TTFT)
323.3s
Max Output
128K tokens
Cost per Task
2.6
AA Score / US$ · higher = better
Benchmarks
GPT-6 Astra (max) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Coding Index | 76.9 | 100.0 | Artificial Analysis official API |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LMArena Elo | 1478.0 | 2000.0 | Crowdsourced blind pairwise comparisons |
| AA Intelligence Index | 52.8 | 100.0 | Artificial Analysis official API |
Information
- Release date
- September 03, 2026
- Tool Calling
- ✅ Supported
- Vision
- ✅ Supported
- Audio
- ❌ Not supported
Full Analysis: GPT-6 Astra (max)
What is GPT-6 Astra (max)?
GPT-6 Astra (max) is an AI model developed by OpenAI, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via OpenAI's cloud API. With a context window of 1.1M tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
GPT-6 Astra (max) is usage-based, priced at $10/1M input tokens and $50/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The premium pricing reflects the model's frontier capabilities, recommended for complex reasoning, analysis, and generation tasks where quality takes priority over cost.
Benchmarks & Performance
GPT-6 Astra (max) was evaluated on 3 different benchmarks, covering categories like Coding, overall. Results show exceptional performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
GPT-6 Astra (max) specializes in text, offering advanced capabilities for creating and processing text content.
Comparison with Alternatives
In the 2026 AI model ecosystem, GPT-6 Astra (max) competes directly with similarly capable models. Key competitors include Claude (Anthropic), Gemini (Google), and open source models like Llama (Meta) and Qwen (Alibaba). The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is GPT-6 Astra (max)?
GPT-6 Astra (max) is an AI model developed by OpenAI. It is a text model.
How much does GPT-6 Astra (max) cost?
GPT-6 Astra (max) costs $10/1M input tokens and $50/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does GPT-6 Astra (max) compare with other models?
In available benchmarks, GPT-6 Astra (max) scored: AA Coding Index: 76.9/100, LMArena Elo: 1478/2000, AA Intelligence Index: 52.8/100. See the full table above for a detailed comparison.
Is GPT-6 Astra (max) open source?
No, GPT-6 Astra (max) is a proprietary model from OpenAI. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is GPT-6 Astra (max) best for?
GPT-6 Astra (max) excels at multimodal tasks including text and vision. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: September 12, 2026 • View methodology →