GPT-6 Astra API: Pricing, Playground and Examples

What Is ChatGPT-6 Astra?

A GPT-Series Model for Complex Reasoning, Coding, and Professional Work

The ChatGPT-6 Astra discussed here corresponds to OpenAI's official name, GPT-6 Astra, and has the API model ID gpt-6-astra. Released on September 3, 2026, it is primarily intended for complex reasoning, coding, computer use, research, and document creation. What matters most to us is whether it can combine these capabilities to keep working toward a goal and ultimately deliver a result that can be checked and used. OpenAI changelog

For example, when handling a production issue involving multiple modules, we want the model to understand the requirements and logs, find the code that needs to change, and then continue refining the fix based on test results. We think Astra is worth a serious evaluation for this kind of work that requires repeatedly cross-checking information.

A Multimodal Model with a 1.05 Million Token Context Window and Image Understanding

The official GPT-6 Astra API provides a 1,050,000 Token context window and a maximum output of 128,000 Token. It supports text and image inputs with text output, and its knowledge cutoff is April 30, 2026. We think this configuration is suited to analyzing requirements documents, code, logs, and interface screenshots as part of the same task. Official model specifications

In practice, we recommend labeling materials with filenames, versions, and dates, and asking the model to tie its conclusions to specific evidence. Long inputs also need to leave room for reasoning and the answer; when the task involves current information, provide fresh materials or connect a retrieval tool. Tasks that require image generation, video creation, or software operation also need the corresponding tools, because a harness and agent play an equally important role in completing the task.

Five Reasoning Levels Let You Adjust Reasoning Effort

GPT-6 Astra supports five reasoning effort levels: low, medium, high, xhigh, and max. We treat these settings as ways to adjust reasoning effort; actual consumption still varies by task. Official reasoning effort guidance

We recommend starting at medium to establish a business baseline: compare low for simple extraction and formatting, and try high for cross-file analysis, multiple constraints, or repeated debugging. For problems the earlier levels still cannot solve, evaluate xhigh and max. What matters more to us is whether the added wait and cost reduce rework.

RequestBeam's ChatGPT-6 Astra Online Tool and API

We want RequestBeam to help you understand ChatGPT-6 Astra's real performance on your own tasks more quickly. You only need to sign in, create an API Key, and add a small amount of credit to start trying it through the online tool. You do not need to write API integration code first or buy a fixed plan for a short evaluation, and you have fewer model-platform accounts to manage.

ChatGPT-6 Astra's Core Capabilities and Domain Strengths

Project Coding: Pay Closer Attention to Cross-File Understanding and Complex Code Review

We think Astra deserves priority for code reviews that require understanding multiple modules at once. In CodeRabbit's early evaluation, Astra's actionable issue coverage on the more difficult cross-file review subset was 57.1%, compared with 47.6% for GPT-5.6 Sol. This metric measures whether a model can identify annotated issues and provide feedback developers can act on; the authors also explicitly note that these are early results from a specific test. CodeRabbit's firsthand evaluation

For work involving API changes, shared state, permission logic, and cross-module dependencies, we recommend providing the callers, relevant tests, and business constraints together, and asking the model to include the code location and triggering condition for each finding. Our customers report that Astra is quite capable of tracking down and resolving cross-module and relatively complex issues.

Tool Coordination: Carry Out Multistep Workflows Across Browsers and Business Software

We are optimistic about Astra's ability to carry out specific operations in existing business systems. In a public hands-on account, Claire Vo of ChatPRD had Astra in Codex operate a CRM workflow editor in Chrome, add nodes and connect logic, complete a lead-assignment workflow, draft customized emails, and send the drafts to Slack for review. Claire Vo's hands-on account

Among our customers, using ChatGPT's Computer Use to replace repetitive business operations accounts for a very large share. Based on their feedback, Astra does perform Computer Use better, but ChatGPT-5.6 Sol may offer better value in some scenarios.

Research: Gather Information, Then Check the Evidence Against a Specific Question

We would include Astra on the trial list for technical research, competitor-information gathering, and long-form review. In our internal tests, using the same prompt and reasoning level for Astra and GPT-5.6 Sol, we found that Astra surfaced more useful materials and blog posts, with broader search coverage.

At the same time, Astra's response speed is a real concern. Our customers also report that Astra is generally not as fast as they expected, and this becomes more noticeable when they need to gather a lot of information.

Visual Development: Turn a Description into an Editable 3D Prototype

We think an interesting use for Astra is to quickly create editable visual prototypes with professional software. Simon Willison used Codex mode in the ChatGPT macOS app, with Astra at medium, to operate a local Blender installation and create a scene of a pelican riding a bicycle. He then made two more rounds of changes to add the background and details, and published the .blend file, Python script, and conversation record. Simon Willison's complete process

Some of our customers have also used Blender MCP to make game assets and were satisfied with the results, while acknowledging that some character models still needed manual cleanup. We therefore recommend testing it in your actual business scenarios to assess whether it can really complete the task.

Testing and Debugging: Tasks That Need Repeated Interaction to Reveal Problems

We are especially interested in whether Astra can keep checking code execution results after writing code. In another hands-on account, Claire Vo had Astra test a preview branch in the browser for 1 hour and 45 minutes, including sending chats, refreshing the page, and checking the console. The author said it found issues that had previously been missed. Browser QA example

However, OpenAI's developer article also notes that Astra sometimes stops after the first implementation and waits for feedback; repeatedly emphasizing checks in old prompts may also lead to excessive testing. If you feel it is spending too much time testing, you can adjust AGENTS.md to limit this. In our own development, testing has not taken an unusually large share, though that may also relate to the reasoning level we use. If you set a higher reasoning level, its share of testing may increase accordingly.

ChatGPT-6 Astra Benchmarks and Performance

Terminal Tasks: About 59% in the Independent Terminal-Bench 4.0 Evaluation

In Artificial Analysis's current comparison table, Astra at max scores approximately 59% on Terminal-Bench 4.0 after rounding. AA uses mini-swe-agent to run all 66 tasks, repeating each task three times and scoring the average single-attempt pass rate. A task is successful only if it passes every test. Tasks cover software maintenance, system operations, and data processing. AA model results · Terminal-Bench 4.0 methodology

We recommend that teams handling dependency failures, database migrations, or cross-file fixes include Astra in tests on a real project. Start with the high reasoning level; lower or raise it after evaluating the results against your business tests.

Scientific Workflows: 68.1% on Terminal-Bench-Science 0.1

In the current Terminal-Bench-Science 0.1 leaderboard, Astra at max, paired with Codex, achieves a solve rate of 68.1%. This evaluation contains 70 scientific workflows covering tasks such as data analysis, simulation, optimization, and model fitting, with acceptance based on actual artifacts and task tests. Scientific workflow leaderboard · Evaluation scope and methodology

We think this result is more useful for evaluating a research assistant that can analyze, write code, run it, and check the result. For routine data cleaning, we recommend comparing lower-cost models first. For repeated failures in fitting, simulation, or complex analysis, raise Astra's reasoning level and then check whether its output meets your requirements.

Professional Documents: 34.2% in the Original GDP.pdf Evaluation

In Surge AI's original GDP.pdf leaderboard, Astra at max scores 34.2%, GPT-5.6 Sol at max scores 30.7%, and Claude Opus 5.5 at adaptive/max scores 30.6%. This evaluation uses PDFs and questions from real professional work, including tables, technical drawings, and easily overlooked details. Surge AI's original leaderboard

We recommend that users who need to work with manuals, extract data from charts, or cross-check information across pages try Astra first, while requiring answers to specify page numbers, applicable models, and the original evidence.

Business Automation: A 41.4% Completion Rate at Max on AutomationBench

Zapier's AutomationBench 1.0.6 tests multistep tasks in simulated business applications and scores whether the final state meets every requirement. The benchmark tests workflows that operate applications through APIs. Some Astra results are shown below. Zapier's original evaluation

From high to max, the completion rate increases by 4.26 percentage points, while average task cost rises by USD 0.29. We recommend first running a workflow with clear acceptance criteria at medium or high, then checking whether max reduces failures that genuinely affect the business. In our business scenarios, response time at max is nearly 20% slower than at high, so consider whether that delay is acceptable for your scenario.

Overall Capability and Response Speed: An Index Score of 53 and About 48 Token/s Output

Artificial Analysis's Intelligence Index v4.3.2 gives Astra at max a score of 53. AA's measurements of the OpenAI API show an output speed of about 47.6 Token per second. This is the rate after generation begins; the actual wait also includes input processing and reasoning time. AA capability and speed evaluation

We recommend recording both the time until an answer starts appearing and the total time to complete a task for features sensitive to wait time. For simple Q&A and short-text organization, we would first compare lower reasoning levels or lighter models. For complex debugging and research analysis, check whether waiting a little longer can produce a better result. In our current workflow, we mainly use Astra at high; for more complex or different tasks, we may switch models and reasoning levels.

ChatGPT-6 Astra Compared with GPT-6.1 Sol, Claude Opus 5.5, and Claude Fable 5.1

Compared with GPT-6.1 Sol: Calculate Everyday Task Costs First, Then Give Harder Problems to Astra

In Artificial Analysis's comparison at max, Astra and GPT-6.1 Sol have overall index scores of 53 and 52, respectively. Their weighted per-task costs in the same index are USD 3.26 and USD 0.72. We think this gap is enough to affect everyday model selection, especially for products handling large numbers of requests every day. Like-for-like comparison of Astra and GPT-6.1 Sol

For batch code maintenance, document organization, and information processing with clear rules, we recommend first checking whether GPT-6.1 Sol can meet the requirements. Astra can be reserved for problems that still fail after repeated attempts, complex research workflows, and reviews where mistakes are more costly. In our everyday development, GPT-6.1 Sol accounts for 80% of use; unless an issue spans modules or is especially complex, we consider switching to Astra.

Compared with Claude Opus 5.5: Consider Source Understanding, Finished Deliverables, and Task Costs Together

In AA's comparison, Opus 5.5 uses max with the default fallback configuration, while Astra uses max. Their overall index scores are 58 and 53, respectively, with weighted per-task costs of USD 5.98 and USD 3.26. We think Opus's score advantage in this overall test should be evaluated together with its higher per-task cost. Opus 5.5 and Astra comparison

If the task is mainly reading professional PDFs and checking tables and technical drawings, we recommend trying Astra first and comparing it closely with Opus 5.5. We also recommend splitting models across different, more specific scenarios in your business workflow. For example, you could use Astra for recognition and Opus 5.5 for understanding materials.

Compared with Claude Fable 5.1: More Attractive Execution Costs, but Keep a Comparison for Long-Document Workflows

In AA's current comparison, Astra at max and Fable 5.1 at max with the default fallback configuration both have an overall index score of 53. Their weighted per-task costs are USD 3.26 and USD 7.63, respectively. Results differ across individual tests: rounded Terminal-Bench 4.0 scores are 59% and 52%, while AA-LCR v1.1 long-context reasoning scores are 81% and 85%, respectively. We think these results are more useful for assigning different models to different tasks. Astra and Fable 5.1 comparison

Teams already using Fable 5.1 can start with comparative tests of terminal execution and multistep fixes, focusing on completion rates and the cost of the whole task. If the existing workflow mainly involves cross-document research and long-form analysis, and you have already developed effective prompts, we recommend keeping Fable while testing Astra on the same materials. Then decide which tasks, if any, to migrate. In our experience, Fable 5.1 and Astra serve different scenarios and are not replacements for each other.