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OpenAI: GPT-6 Astra
OpenAI
•
GPT-6 Astra (max)
•
Released 
September 2026

OpenAI
GPT-6 Astra (max)

OpenAI's flagship reasoning model for long-horizon agentic work, computer use, software engineering, and document creation.

Modality:
Text
Image
PDF
model ID
openai/gpt-6-astra
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Output Speed *

63.57
tok/s

Intelligence Index *

52.8
/ 100

Context Window *

1050000
tokens

Input price

7
Anytoken

Output price

35
Anytoken
GPT-6 Astra: OpenAI's Flagship Model for Long-Horizon Agentic and Engineering Work GPT-6 Astra is OpenAI's most capable model, positioned at the top of its lineup as the successor to GPT-5.6 Sol. It is a text-and-image-input reasoning model built for the hardest end-to-end work: complex reasoning, agentic coding, computer use, deep research, and document creation. Its defining characteristics are a 1,050,000-token context window, five-level adjustable reasoning effort, and strong token efficiency on agentic tasks. Teams running long-horizon multi-step workflows, terminal and computer-use agents, or engineering pipelines benefit most; routine high-volume chat is better served by cheaper models. Integrate GPT-6 Astra via the AnyAPI.ai API and route it to your hardest tasks.

Performance

Where GPT-6 Astra Pulls Ahead: Agentic Coding and Token Efficiency

GPT-6 Astra's clearest strength is agentic engineering and computer-use work rather than raw composite intelligence. On OpenAI's evaluations it reached roughly 57.9% on Terminal-Bench 4.0 against 37.3% for GPT-5.6 Sol, a large jump on complex terminal-based tasks. Independent testing by Artificial Analysis found Astra matches Claude Fable 5.1 on their Coding Agent Index while using the lowest token count of any agent measured. That token efficiency matters in production: multi-step agent runs complete with fewer output tokens, lowering per-task cost even though per-token pricing is high.

Benchmarks

GPT-6 Astra Benchmarks: Specialized Wins, Flat Composite Intelligence

Independent results are more mixed than OpenAI's framing. Artificial Analysis places GPT-6 Astra (max) at 53 on its Intelligence Index v4.3, tying Claude Fable 5.1 at a lower cost per task, but on earlier composite runs it scored roughly level with GPT-5.6 Sol and behind Fable 5.1. Its gains concentrate in coding agents, terminal workflows, computer use, and mathematics rather than broad reasoning. Artificial Analysis also measured output at about 53 tokens per second, slower than the comparable-model average, reflecting the model's reasoning overhead at higher effort settings.

Output Speed

*
63.57
tok/s

Intelligence Index

*
52.8
/ 100

MMLU *

Broad world knowledge and problem-solving
0
%

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.
96
%

HLE *

Adherence to multi-step structured instructions.
55
%

LiveCodeBench *

Tool-calling reliability in long agentic loops.
0
%

Technical Specifications

What the model supports

GPT-6 Astra accepts text and image input and returns text only; audio and video are not supported native modalities. Its 1,050,000-token context window (up to roughly 922,000 input tokens) with 128,000 maximum output tokens suits large-repository and long-document workloads, but note the context window and output ceiling are separate limits. The largest production consideration is the long-context pricing tier: prompts over 272,000 input tokens are billed at higher input and output multipliers, so unbounded context filling directly raises cost.
Verified Specifications — 
GPT-6 Astra (max)
*
Input modalities
Text
Image
PDF
output modalities
Text
Image
Context window
1050000
 tokens
Maximum output tokens
128000
Reasoning
Yes
Knowledge cutoff
September 2026
Pricing (standard)
7
 AnyTokens in
 / 
35
 AnyTokens out

Comparison

GPT-6 Astra vs Claude Fable 5.1: Which for Coding Agents?

Both are top-tier proprietary reasoning models aimed at demanding agentic and engineering work, and independent evaluators repeatedly benchmark them head-to-head. On Artificial Analysis's Coding Agent Index they finish close, with Fable 5.1 slightly ahead on the composite while Astra leads OpenAI's Terminal-Bench comparisons and uses markedly fewer tokens per task. The practical decision is less about a single winner and more about cost structure, tool ecosystem, and whether your workload favors Astra's token efficiency and computer-use strengths or Fable 5.1's broader composite intelligence.

Dimension
GPT-6 Astra (max)
Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback)
Context window *
1050000
tokens
1000000
tokens
Output speed *
63.57
tok/s
69.67
tok/s
Intelligence Index *
52.8
53.4
Input pricing
7
AnyToken
60
AnyToken
Output pricing
35
AnyToken
300
AnyToken
Knowledge cutoff *
September 2026
September 2026

Choose GPT-6 Astra when your workload is agentic coding, terminal or computer-use automation, or long-horizon workflows where its token efficiency lowers per-task cost, and when you need a 1M-token context window with OpenAI's tool ecosystem. Choose Claude Fable 5.1 when you want the leading composite intelligence and coding-agent score, or when your stack is already built around Anthropic's tooling. For broad reasoning that doesn't lean on agents, Fable 5.1 has held a modest independent-benchmark edge.

Limitations & Trade-offs

Where GPT-6 Astra (max) falls short

1
High per-token pricing. GPT-6 Astra's output pricing sits well above GPT-5.6 Sol—roughly 2.5x per token—placing it firmly in the premium tier. Even with strong token efficiency, independent analysis found average cost per task materially higher than Sol at max effort. For routine, high-volume generation such as customer chat or bulk summarization, a lower-cost model is the better default; reserve Astra for high-value tasks where its capability justifies the spend.
2
Flat composite intelligence gains. Despite OpenAI's 'most intelligent' framing, independent aggregates tell a mixed story: on Artificial Analysis's Intelligence Index, Astra scores close to its own predecessor and behind Claude Fable 5.1 on some runs. Its improvements concentrate in coding, computer use, and mathematics rather than general reasoning. Teams expecting a broad across-the-board upgrade over GPT-5.6 Sol may see little benefit outside agentic and engineering workloads.
3
Slower output and high time-to-first-token. Artificial Analysis measured output around 53 tokens per second, below the comparable-model average, and independent trackers reported p95 time-to-first-token near 8.75 seconds. Higher reasoning-effort settings increase latency further. This makes Astra a poor fit for latency-sensitive, real-time interactive applications; a faster, lighter model is preferable when responsiveness matters more than depth.
4
No native audio, video, or image output. GPT-6 Astra accepts text and image input and returns text only; audio and video are unsupported native modalities. It can orchestrate an image-generation tool via the Responses API, but it does not natively produce images, audio, or video. Applications needing native speech, real-time voice, or media generation must pair Astra with dedicated tools or choose a different model.

Best-Fit Workloads

Where this model earns its place

01

Coding and computer-use agents

‍
Astra's strongest evidence is here: a large lead over GPT-5.6 Sol on Terminal-Bench 4.0 and category-leading token efficiency on Artificial Analysis's Coding Agent Index. It handles multi-step terminal workflows, system configuration, and desktop application control, making it well suited to autonomous engineering agents. Because it uses fewer tokens per task, long agent runs stay comparatively economical despite premium per-token pricing.

02

Deep research over long documents

‍
The 1,050,000-token context window lets Astra ingest large document sets, codebases, or research corpora in a single request, and it can conduct online research and draft summaries. Combined with adjustable reasoning effort, this suits analytical research pipelines. Watch the long-context pricing tier: prompts above 272,000 input tokens are billed at higher multipliers, so scope retrieval carefully rather than filling the full window.

03

Structured document and presentation generation

‍
OpenAI positions Astra as its best model for adhering to templates and producing well-laid-out slides, documents, and spreadsheets with a structured narrative. For professional knowledge-work automation—reports, briefs, formatted decks—its instruction adherence and formatting judgment are differentiators. Structured outputs via JSON schema make it straightforward to enforce document structure programmatically in production pipelines.

04

Scientific and mathematical reasoning

‍
Astra reached a new high on Terminal-Bench Science 0.1 for code-and-terminal research workflows and posted very high FrontierMath Tier 4 scores in OpenAI's evaluations, having reportedly assisted on open mathematical problems. For simulation, data-analysis, and quantitative research tasks that combine reasoning with tool use, it is a strong candidate—provided outputs receive appropriate expert validation.

Pricing in anytokens via AnyAPI
Input
7
₳
Output
35
₳
Cache write
75
₳
Cache read
6
₳
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PerformanceBenchmarksTechnical SpecificationsComparisonLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access GPT-6 Astra (max) via AnyAPI.ai

Access GPT-6 Astra (max) through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate GPT-6 Astra (max) without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access GPT-6 Astra (max) and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test GPT-6 Astra (max) against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use GPT-6 Astra (max) from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use GPT-6 Astra (max) for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

GPT-6 Astra has a 1,050,000-token context window, with up to roughly 922,000 input tokens and a maximum of 128,000 output tokens. These are separate limits. Prompts exceeding 272,000 input tokens are billed at a higher long-context pricing tier, so plan retrieval and context usage accordingly.

GPT-6 Astra accepts text and image input and returns text only. Audio and video are not supported native modalities, and it does not natively generate images. It can, however, call an image-generation tool through the Responses API. For native voice or media output, pair it with dedicated tools.

Yes, coding and agentic engineering are its strongest areas. It posted a large lead over GPT-5.6 Sol on Terminal-Bench 4.0 and matches Claude Fable 5.1 on Artificial Analysis's Coding Agent Index while using the fewest tokens of any agent measured. Fable 5.1 holds a slight edge on that composite index.

GPT-6 Astra is a reasoning model with a configurable reasoning.effort parameter supporting low, medium, high, xhigh, and max. Higher effort improves results on hard problems but increases latency and token cost. Temperature is unavailable while reasoning is active, and the API uses max_completion_tokens rather than max_tokens.

It depends on the workload. Astra's per-token price is roughly 2.5x GPT-5.6 Sol's, but its token efficiency can lower per-task cost on agentic work. For coding agents, computer use, and long-horizon workflows it is often justified; for broad reasoning or high-volume routine tasks, its composite intelligence gains are modest and Sol may be more economical.

* Benchmark data source: Artificial Analysis artificialanalysis.ai