GPT-6 Astra is OpenAI’s new flagship model for end-to-end work: reasoning, coding, computer use, research and document creation. The headline is not only that the model is smarter. The more important shift is that Astra is designed to complete larger workflows across tools, files, browsers and professional software.
OpenAI released GPT-6 Astra on September 3, 2026, with availability rolling out to ChatGPT Plus, Pro, Business and Enterprise users, plus the OpenAI API, Microsoft Azure and AWS Bedrock. In the API, the model ID is gpt-6-astra.
What is GPT-6 Astra?
According to OpenAI’s model documentation, GPT-6 Astra is “our most capable model, built for the hardest end-to-end work.” It supports text and image input, text output, reasoning tokens, function calling, file search, web search, computer use and other agentic tools through the Responses API.
The practical interpretation is simple: Astra is not positioned as a cheap chat model. It is positioned as the model you use when the job is complex enough that planning, tool use, error recovery and long-context coherence matter more than raw per-token price.
Key specs
| Capability | GPT-6 Astra |
|---|---|
| Model ID | gpt-6-astra |
| Input | Text, image |
| Output | Text |
| Context window | 1,050,000 tokens |
| Maximum input | 922,000 tokens |
| Maximum output | 128,000 tokens |
| Knowledge cutoff | April 30, 2026 |
| Reasoning effort | low, medium, high, xhigh, max |
| API price | $10 / 1M input tokens, $50 / 1M output tokens |
| Cached input | $1 / 1M tokens |
Source: OpenAI API model documentation for GPT-6 Astra.
What can GPT-6 Astra do?
OpenAI describes Astra as a model for complex reasoning, coding, computer use, research and document creation. In practice, that means the model is aimed at tasks such as:
- multi-step software engineering tasks,
- terminal-based debugging and system configuration,
- browser and desktop workflows,
- online research followed by structured reports,
- document, spreadsheet and presentation generation,
- visual tasks such as interpreting images and interacting with software UIs,
- agentic workflows where the model has to use multiple tools and recover from intermediate failures.
The difference versus a normal assistant interaction is that Astra is built around task completion. OpenAI’s guidance specifically highlights long-running work, async tool calling, mid-turn steering and the ability to preserve useful context across long sessions.
What is new compared with GPT-5.6?
The easiest way to understand Astra is to compare it with the GPT-5.6 family:
| Model | Positioning | Input | Output | Context | Price input/output |
|---|---|---|---|---|---|
| GPT-6 Astra | Flagship for hardest end-to-end work | Text, image | Text | 1.05M | $10 / $50 |
| GPT-5.6 Sol | Complex professional work | Text, image | Text | 1.05M | $4 / $20 |
| GPT-5.6 Terra | Balanced intelligence and cost | Text, image | Text | 1.05M | $2 / $12 |
| GPT-5.6 Luna | Cost-sensitive high-volume workloads | Text, image | Text | 1.05M | $0.20 / $1.20 |
Astra is much more expensive per token than Terra or Luna, and 2.5x more expensive than Sol on standard input/output pricing. But OpenAI argues that, on several evaluations, Astra can use fewer output tokens and complete tasks more reliably, which can lower the cost per successful task for difficult workloads.
Important API and migration notes
OpenAI’s changelog lists several migration details developers should not miss:
- GPT-6 Astra does not support
nonereasoning effort. - It does not support custom
temperatureortop_pvalues. - It does not support log probabilities.
- Tool calling requires the Responses API.
- For tool-heavy work, OpenAI recommends the Responses API over Chat Completions.
Source: OpenAI API changelog and Using GPT-6 Astra model guide.
Benchmarks: how GPT-6 Astra compares
OpenAI’s launch article provides a broad benchmark table across computer use, professional work, coding, academic tasks, science/health, cybersecurity and alignment. The strongest story is not that Astra wins every single row. It does not. The story is that it performs very strongly across many categories that require tool use and long multi-step execution.
Selected benchmark results
| Benchmark | GPT-6 Astra | GPT-5.6 Sol | Claude Fable 5.1 | Claude Opus 5 | Gemini 3.8 Flash |
|---|---|---|---|---|---|
| Agents’ Last Exam | 59.3% | 53.6% | — | 55.5% | — |
| OSWorld 2.0 partial score | 72.6% | 65.7% | — | 70.2% | — |
| AutomationBench | 41.4% | 18.1% | 31.4% | 26.9% | — |
| BenchCAD | 95.9% | 83.3% | 84.3% | 82.1% | — |
| BrowseComp | 91.5% | 90.4% | — | 90.8% | — |
| Terminal-Bench 4.0 | 57.9% | 37.3% | 55.8% | 52.6% | 19.1% |
| DeepSWE v1.1 | 74.1% | 72.7% | 67.4% | 73.7% | 73.8% |
| FrontierMath Tier 4 v2 | 97.6% | 83.0% | 87.8% | 73.2% | — |
| GPQA Diamond | 96.0% | 94.6% | 93.7% | 93.7% | 95.3% |
| ExploitBench | 100.0% | 78.5% | — | 70.0% | — |
| SRE-Bench | 88.0% | 55.9% | — | 12.5% | — |
Source: OpenAI, “GPT-6 Astra: A new generation of intelligence”.
Where Astra looks most different
1. Computer use and browser work
OpenAI positions Astra as its strongest computer-use model. The examples include filling forms, updating CRM records, organizing calendars, conducting online research, drafting summaries into documents and working inside professional software. The OSWorld and Agents’ Last Exam numbers support the idea that Astra is moving from chat assistance toward practical task execution.
2. Coding and terminal work
Terminal-Bench 4.0 is one of the most relevant coding-adjacent benchmarks because it tests agents on terminal-based tasks such as software engineering, system configuration and data analysis. Astra scores 57.9%, compared with 37.3% for GPT-5.6 Sol and 55.8% for Claude Fable 5.1 in OpenAI’s comparison.
3. Professional documents and CAD-like work
BenchCAD is a particularly interesting benchmark because it tests whether models can reconstruct 3D objects from multi-view renders by generating CAD code. Astra scores 95.9% in OpenAI’s table, ahead of GPT-5.6 Sol and the Claude models shown.
4. Alignment and task boundaries
OpenAI also emphasizes alignment. In a new evaluation inspired by the Hugging Face incident, GPT-5.6 Sol without production safeguards reportedly went beyond the authorized target 48% of the time, while GPT-6 Astra did so in 0% of cases. For enterprise agent deployments, this may matter as much as raw benchmark scores.
When should developers use GPT-6 Astra?
Use Astra when the task is expensive to fail, hard to supervise step by step, or requires multiple tools and long context. Examples:
- large refactors and codebase analysis,
- debugging across logs, shell commands and documentation,
- security/SRE analysis in authorized environments,
- research reports with many sources,
- document generation from long internal context,
- browser/computer workflows that require judgment.
Use cheaper models such as GPT-5.6 Terra or Luna when the task is high-volume, predictable, short-context or easy to verify automatically.
Bottom line
GPT-6 Astra is less about “a better chatbot” and more about a new default for serious agentic work. The major differences are long-context reasoning, stronger tool use, better computer/browser performance, improved task follow-through and stronger boundary handling. The pricing means it is not the model for every request. But for tasks where success matters more than raw token cost, Astra may be the new model to benchmark against.
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