
GPT-6 Astra
OpenAI
- Context window
- 1M 1,050,000 tokens
- Max output
- 128K 128,000 tokens
- Input / Output
- text, image → text
API pricing
USD / 1M tokens
| Input | Output | Cached input |
|---|---|---|
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Using GPT-6 Astra for a demanding task
Sources checked:
OpenAI's model reference positions Astra for demanding reasoning, coding and research. For a useful trial, choose a deliverable with a checkable result: a repository change with tests, an analysis whose claims can be traced to documents, or a structured extraction you can validate.
Watch the full request size. The reference describes a different price tier above 272,000 input tokens. Read the conditions beside this page's prices before budgeting a large document or a long agent session. A context-window maximum is a capacity limit, not a recommendation to send every file. Include tool calls, cache writes, retries and human review in the API task-cost worksheet.
Keep the model and the tool separate. A trial in Codex also tests its instructions, tools and permissions. Record those settings alongside the model and reasoning effort. The Codex guide and coding-tool comparison explain how to compare the workflow. These are suggested checks, not results of a new benchmark.
Timeline
- · API availability · evidence link · evidence link
- · API availability · evidence link
Input / output price · USD / 1M tokens
| Model | Evidence links | Input / output price |
|---|---|---|
gpt-6-astrabatch · long context | openai API route unavailable | $10.00 (Input > 272,000 tokens) / $37.50 (Input > 272,000 tokens) |
gpt-6-astrabatch · short context | openai API route unavailable | $5.00 (Input ≤ 272,000 tokens) / $25.00 (Input ≤ 272,000 tokens) |
gpt-6-astrafast · long context | openai API route unavailable | $40.00 (Input > 272,000 tokens) / $150.00 (Input > 272,000 tokens) |
gpt-6-astrafast · short context | openai API route unavailable | $20.00 (Input ≤ 272,000 tokens) / $100.00 (Input ≤ 272,000 tokens) |
gpt-6-astraflex · long context | openai API route unavailable | $10.00 (Input > 272,000 tokens) / $37.50 (Input > 272,000 tokens) |
gpt-6-astraflex · short context | openai API route unavailable | $5.00 (Input ≤ 272,000 tokens) / $25.00 (Input ≤ 272,000 tokens) |
gpt-6-astrastandard · long context | openai API route unavailable | $20.00 (Input > 272,000 tokens) / $75.00 (Input > 272,000 tokens) |
gpt-6-astrastandard · short context | openai API route unavailable | $10.00 (Input ≤ 272,000 tokens) / $50.00 (Input ≤ 272,000 tokens) |
gpt-6-astraultrafast · long context | openai API route unavailable | $120.00 (Input > 272,000 tokens) / $450.00 (Input > 272,000 tokens) |
gpt-6-astraultrafast · short context | openai API route unavailable | $60.00 (Input ≤ 272,000 tokens) / $300.00 (Input ≤ 272,000 tokens) |
openai/gpt-6-astra-prostandard | openrouter | $10.00 / $50.00 |
openai/gpt-6-astra-pro:batchbatch | openrouter | $5.00 / $25.00 |
- · Evidence links · OpenAI · official articles
- · Evidence links · OpenAI · official articles
Excerpt
Making longer videos possible with GPT‑6 Astra
- · Confirmed use · OpenAI · official articles · Asana cuts model costs 76x in browser tests with GPT-6.1 Sol
- · Evidence links · OpenAI · official articles
Excerpt
We used some of our most capable and aligned models, including our upcoming GPT‑6 Astra Cyber model and an internal frontier model
- · Confirmed use · OpenAI · official articles · Greptile takes bug detection beyond code review with GPT-6 Astra
Excerpt
How Greptile uses GPT‑6 Astra to test user workflows, Daybreak to strengthen security, and Codex to reduce rework
- · Confirmed use · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
- · Confirmed use · OpenAI · official articles · Qodo builds context into AI code review with Codex
- · Confirmed use · OpenAI · official articles · DevRev delivers autonomous self-service with GPT-6.1 Sol
Excerpt
DevRev’s sales engineers also use GPT‑6 Astra to create demos for the company’s booths at international conferences
- · Confirmed use · OpenAI · official articles · Merge builds product and business tools with OpenAI technology
Excerpt
For its own product development, Merge uses GPT‑6 Astra
- · Evidence links · OpenAI · official articles
- · Evidence links · OpenAI · official articles
- · Confirmed use · OpenAI · official articles · Introducing Astra for Law
- · Evidence links · OpenAI · official articles
Excerpt
It will appear in the model picker as “GPT‑6 Astra Law” and in the API as gpt-6-astra-law
- · Evidence links · OpenAI · official articles
Excerpt
We’ve also brought GPT‑6 Astra’s improved communication style to Sol and Luna, which we think will be especially noticeable in technical and coding conversations
- · Catalogue listing · GitHub · openai/openai-node · code
Excerpt
ils and * recommended replacements. * - "gpt-3.5-turbo-1106": Announced shutdown date: 2026-09-28. See * https://developers.openai.com/api/docs/deprecations for details and * recommended replacements. */ model: | 'gpt-6-astra' | 'gpt-6.1-sol' | 'gpt-6.1-terra' | 'gpt-6-sol' | 'gpt-6-luna' | 'gpt-5.6-sol' | 'gpt-5.6-terra' | 'gpt-5.6-luna' | 'gpt-5.5' | 'gpt-5.5-2026-04-23' | 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.4-mini-2026-03-17' | 'gpt-5.4-nano-2026-03-17' | 'gpt-5.3-chat-latest' | 'gpt-5.2' | 'gpt-5.2-2025-12-11' | 'gpt-5.2-chat-latest' | 'gpt-5.2-pro' | 'gpt-5.2-pro-2025-12-11' | 'gpt-5.1' | 'gpt-5.1-2025-11-13' | 'gpt-5.1-codex' | 'gpt-5.1-mini' | 'gpt-5.1-chat-latest' | 'gpt-5' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-5-20
- · Confirmed use · OpenAI · official articles · Cogent and OpenAI build cyber defense factories for enterprises
- · Catalogue listing · GitHub · openai/codex · code
Excerpt
es::start_mock_server().await; let board = responses::start_mock_server().await; let url = board.uri(); let root = test_codex() .with_model("gpt-6-astra") .with_config(move |config| { configure(config); config.ephemeral = true; for feature in [ Feature::CodeMode, Feature::CodeModeOnly, Feature::CodeModeHost, ] { config .features
This code reference does not establish a working model identifier.
- · Catalogue listing · GitHub · openai/codex · code
Excerpt
EL_ID: &str = "gpt-6.1-sol"; const GPT_6_1_TERRA_OPENAI_MODEL_ID: &str = "gpt-6.1-terra"; const GPT_6_SOL_OPENAI_MODEL_ID: &str = "gpt-6-sol"; const GPT_6_LUNA_OPENAI_MODEL_ID: &str = "gpt-6-luna"; const GPT_6_ASTRA_OPENAI_MODEL_ID: &str = "gpt-6-astra"; const GPT_5_5_OPENAI_MODEL_ID: &str = "gpt-5.5"; pub(crate) fn static_model_catalog() -> ModelsResponse { let catalog = ModelsResponse { models: vec![ bedrock_model( bundled_openai_model(GPT_6_1_SOL_OPENAI_MODEL_ID), AMAZON_BEDROCK_GPT_6_1_SOL_MODEL_ID, "GPT-6.1 Sol", /*priority*/ 0, ), bedrock_model( bundled_openai_model(GPT_6_ASTRA_OPENAI_MODEL_ID), AMAZON_BEDROCK_GPT_6_ASTRA_MODEL_ID, "GPT-6-Astra", /*priority*/ 1, ), bedrock_mod
- · Catalogue listing · GitHub · openai/openai-agents-python · code
Excerpt
ody={"reasoning_effort": "low"}), False, "low", ), ("gpt-6-sol", "string", "none", ModelSettings(), False, None), ("gpt-6.1-sol", "string", "function", ModelSettings(), False, None), ("gpt-6-astra", "string", "function", ModelSettings(), False, None), ("gpt-6-sol-unknown", "string", "function", ModelSettings(), False, None), ("gpt-6-sol", "string", "function", ModelSettings(), True, None), ], ) @pytest.mark.parametrize("stream", [False, True]) async def test_gpt6_tool_request_defaults( name: str, entry: str, tools: str, settings: ModelSettings, responses: bool, expected: str | None, stream: bool, ) -> None: if responses and stream: pytest.skip("Responses preservation is covered by the non-streaming request.") before = settings.to_json_dict() bodies: list[dic
This code reference does not establish a working model identifier.
- · Catalogue listing · GitHub · openai/openai-python · code
Excerpt
ormat, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters. """ model: Union[ Literal[ "gpt-6-astra", "gpt-6.1-sol", "gpt-6.1-terra", "gpt-6-sol", "gpt-6-luna", "gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", "gpt-5.5", "gpt-5.5-2026-04-23", "gpt-5.4", "gpt-5.4-mini", "gpt-5.4-nano", "gpt-5.4-mini-2026-03-17", "gpt-5.4-nano-2026-03-17", "gpt-5.3-chat-latest", "gpt-5.2", "gpt-5.2-2025-12-11", "gpt-5.2-chat-latest", "gpt-5.2-pro", "gpt-5.2-pro-2025-12-11", "gpt-5.1", "gpt-
- · Catalogue listing · GitHub · openai/openai-agents-js · code
Excerpt
(): boolean { return isGpt5OrNewerReasoningModel(getDefaultModel()); } const DEFAULT_REASONING_EFFORT_PATTERNS: Array< readonly [ RegExp, Exclude<ModelSettingsReasoningEffort, 'minimal' | 'xhigh' | 'max' | null>, ] > = [ [/^gpt-6-astra$/, 'low'], [/^gpt-5(?:-\d{4}-\d{2}-\d{2})?$/, 'low'], [/^gpt-5\.1(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.2(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.2-pro(?:-\d{4}-\d{2}-\d{2})?$/, 'medium'], [/^gpt-5\.2-codex$/, 'low'], [/^gpt-5\.3-codex$/, 'none'], [/^gpt-5\.4(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.4-pro(?:-\d{4}-\d{2}-\d{2})?$/, 'medium'], [/^gpt-5\.4-mini(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.4-nano(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.5(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.6$/, 'none'], [/^gpt-5\.6-sol(?:-\d{4}-\d{2}-\d{2})?$/, 'none'], [/^gpt-5\.6-terra(?:-\d{4
- · Catalogue listing · Vertex AI · quotas
- · Alex's posts · Alex Getman
Excerpt
Kimi K3.1 Pro benchmarks are out! Of all these benchmarks, I trust Terminal-Bench 4.0 the most. And the results are impressive: Kimi K3.1 Pro scores 65.7, just 0.7 points behind the leader, Opus 5.5, while outperforming GPT-6 Astra by 7.5 points.
- · Alex's posts · model family · Alex Getman
Excerpt
Looks like Astra 6.1 is coming OpenAI rolled out a private test model gpt-6-astra-api-priv-ev3
- · Evidence links · OpenAI · news
Excerpt
Higgsfield brings creators longer videos with GPT-6 Astra, precise visuals with GPT-Image-2.5, and a path to voice filmmaking with GPT-Live
- · Alex's posts · Alex Getman
Excerpt
Meta just released Muse Wonder According to the benchmarks, it's right up there with GPT 6 Astra and Claude Opus 5.5. The results look impressive, but the model might be heavily optimized for benchmarks. It's still unclear how well it actually performs in real world use. And yeah, most of you probably won't be able to try it through a subscription, since Muse is still only available in the US.
- · Alex's posts · Alex Getman
Excerpt
🚨 The French at Mistral just dropped Large 4 Looks like Europe’s biggest hope in frontier AI is still alive. 1 trillion parameters, with 49B active at a time. In some benchmarks, it already beats GPT 6 Astra and competes with the strongest open weight models. The API is live now, with weights coming by the end of October. And yes, Mistral internally calls it Le Chonk. After the fat cat meme. https://www.youtube.com/watch?v=hD51W2txi1Y
- · Alex's posts · Alex Getman
Excerpt
🚨 Gemini 4 Argon scores 53 and ties for 3rd place with Fable 5.1 and GPT 6 Astra on the AA Intelligence Index
- · Alex's posts · Alex Getman
Excerpt
🚨 Sonnet 5.5 Max beats GPT-6 Astra Max on the Intelligence Index vs. cost curve A few other things stand out: 1. At medium, GPT-6 Sol is much cheaper than Sonnet 5.5 at a fairly similar intelligence level. 2. Around the Pareto line, GPT-6 Sol Max and Sonnet 5.5 High are basically neck and neck. 3. GPT-6 Astra Max lands roughly around Sonnet 5.5 XHigh. + Sonnet 5.5 Max is in a league of its own at the top.
- · Alex's posts · Alex Getman
Excerpt
🚨 Gemini 4.0 Pro specs leaked • Pricing: $2.25/$11.25 • 2M token context window • Beats GPT-6 Astra and Claude Fable 5.1 on almost every reported benchmark • While being significantly cheaper than both
- · Alex's posts · Alex Getman
Excerpt
🚨 Union Alpha just dropped in stealth on OpenCode It scores around 51% on Terminal Bench v4 at roughly $1.5 per task, putting it close to GPT 6 Astra high for much less. My guess is this is a new Chinese model, possibly Kimi. Kimi has also brought its paid plans back to the site after pausing new memberships. The timing lines up.
- · Alex's posts · Alex Getman
Excerpt
OpenAI has a new internal model that is way ahead of GPT 6 Astra in math On a curated set of open math problems, it reaches about 48% pass rate with more test time compute. GPT 6 Astra peaks at around 17%.
- · Alex's posts · Alex Getman
Excerpt
🚨 OpenAI’s new internal model solved the Navier-Stokes Millennium Prize Problem Around 10,000 agents worked on the solution for 88 hours, powered by a model significantly more capable than GPT 6 Astra. After the solution was found, GPT 6 Astra formalized and verified the proof in Lean.
- · Alex's posts · Alex Getman
Excerpt
🚨 Fable 5.2 could launch this month after Astra’s strong debut Anthropic is reportedly preparing its first fresh pretrain for Fable ahead of its IPO. People there are confident it can dethrone Astra. For context: Fable 5.1 and GPT 6 Astra currently share #1 on AA, with Fable slightly ahead in coding and Astra much stronger on 3D tasks.
- · Alex's posts · Alex Getman
Excerpt
🚨 GPT-6 Astra is officially #1 on the updated Artificial Analysis Intelligence Index, sharing the top spot with Claude Fable 5.1.
- · Alex's posts · Alex Getman
Excerpt
GPT 6 Astra set a new record in MazeBench, a 3D puzzle benchmark It explored 40% of all rooms and collected 14% of gems Fable 5.1 reached just 4.3% of rooms and collected 2% of gems
- · Alex's posts · Alex Getman
Excerpt
GPT-6 Astra Fast mode quietly went from 1.5x to 2x speed, with the same 2.5x usage multiplier. It’s still pretty expensive though. A task that would count as 1,000 usage units on Standard would count as 2,500 on Fast, so that’s still 150% more usage. *My previous post was deleted because I got the math wrong. I misled some of you, and I’m sorry about that.
- · Alex's posts · Alex Getman
Excerpt
Correction: I got the calculation in this post wrong. A 2.5× usage multiplier means 150% more usage, not 25%. Corrected calculation: https://alexgetman.com/403/gpt-6-astra-fast-mode-quietly-went-from-1-5x-to-2x-speed-with-the-same-2-5x-usag/ GPT-6 Astra Fast mode quietly went from 1.5x to 2x speed, with usage still at 2.5x So you’re getting double the speed for just 25% extra usage. At this point, there’s very little reason to turn Fast mode off.
- · Alex's posts · Alex Getman
Excerpt
GPT 6 Astra and Fable 5.1 got 20 attempts to pick up a block and place it into a bowl Astra: 95% success, 2.5 min, $0.94 per run Fable 5.1: 40% success, 6.8 min, $2.12 per run Astra also used 6× fewer output tokens, just 2.1k per run.
- · Alex's posts · Alex Getman
Excerpt
Claude is no longer the best model for frontend GPT-6 Astra just took the #1 spot, knocking Claude Fable 5.1 off the top The jump is massive: the previous GPT generation, GPT-5.6 Sol, was #13 A GPT model hasn’t led frontend since August 2025
- · Alex's posts · Alex Getman
Excerpt
GPT 6 Astra and Fable 5.1 got 20 attempts to pick up a block and place it into a bowl Astra: 95% success, 2.5 min, $0.94 per run Fable 5.1: 40% success, 6.8 min, $2.12 per run Astra also used 6× fewer output tokens, just 2.1k per run.
- · Alex's posts · Alex Getman
Excerpt
🚨 Plus users got access to GPT-6 Astra
- · Alex's posts · Alex Getman
Excerpt
🚨 GPT-6 Astra is out for Pro users
- · Alex's posts · Alex Getman
Excerpt
GPT-6 Astra looked like a generational leap on OpenAI’s benchmarks. Artificial Analysis puts it closer to old Fable 5 territory.
- · Alex's posts · Alex Getman
Excerpt
🚨 GPT-6 Astra is officially out https://openai.com/index/gpt-6-astra/
- · Alex's posts · Alex Getman
Excerpt
🚨 GPT-6 Astra is absolutely crushing everyone on the benchmarks
- · Alex's posts · Alex Getman
Excerpt
🚨 GPT-6 Astra has been added to the OpenAI API Launch in less than 24 hours
- · Alex's posts · Alex Getman
Excerpt
🚨 OpenAI is launching GPT 6 Astra next week It is a brand new model trained from scratch and the company’s largest since GPT 4.5. The latest internal build, codenamed mewfour, is already the release candidate.
The forecast timing did not hold; retained as an early mention, not a successful prediction.
Alex's take
Signals 1
Specifications
GPT-6 Astra is a model from OpenAI, released on 3 Sep 2026. The context window is 1,050,000 tokens. The maximum output is 128,000 tokens. Pricing is $10.00 per million input tokens and $50.00 per million output tokens.
- Context window
- 1,050,000
- Max output
- 128,000
- Input
- text · image
- Output
- text
- Input · USD / 1M tokens
- $10.00
- Output · USD / 1M tokens
- $50.00
- Cached input · USD / 1M tokens
- $1.00
Questions
What is the context window of GPT-6 Astra?
The context window is 1,050,000 tokens. The maximum output is 128,000 tokens.
How much does GPT-6 Astra cost?
Pricing is $10.00 per million input tokens and $50.00 per million output tokens. Cached input is $1.00 per million.
When was GPT-6 Astra released?
On 3 Sep 2026.