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GPT-6 Astra API: Endpoint & Pricing

GPT-6 Astra brings Gpt Astra conversational intelligence to LLM.API so teams can ship assistants and agents without juggling multiple vendor SDKs.

Compare GPT-6 Astra alternatives (30) →

What is GPT-6 Astra?

GPT-6 Astra is a Gpt Astra chat model exposed on LLM.API under id `gpt-6-astra`. GPT-6 Astra is OpenAI's most capable model for complex reasoning, coding, computer use, research, and document creation. Teams use it when they need reliable text generation with text, image inputs and text outputs over an OpenAI-compatible API.


Can you self-host GPT-6 Astra?

No. We found no public weights for GPT-6 Astra on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Providers

LLM.API routes GPT-6 Astra to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput
openai$10 in
$50 out
azure$10 in
$50 out
novita$10 in
$50 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
openaiin $10; out $50 per 1M tokens1.1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
azurein $10; out $50 per 1M tokens1.1M tokensvision, tools, streaming, reasoning, web search, JSON, structured
novitain $10; out $50 per 1M tokens1.1M tokensvision, tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test GPT-6 Astra right here — free to start.

GPT-6 Astra
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call GPT-6 Astra through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to gpt-6-astra and the call runs unchanged.

bash
curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "medium"
  }'
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="gpt-6-astra",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "gpt-6-astra",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);
stream = client.chat.completions.create(
    model="gpt-6-astra",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)
{
  "model": "gpt-6-astra",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "medium"
}

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID gpt-6-astra · OpenAI-compatible.

  • POST /v1/chat/completionsChat Completions — supported
FeatureStatusHow to set it
Function calling (tools)Supportedtools, tool_choice
Parallel tool callsNot supportedparallel_tool_calls
JSON modeSupportedresponse_format: {"type": "json_object"}
Structured outputsSupportedresponse_format: {"type": "json_schema", …}
StreamingSupportedstream: true
Vision (image input)Supportedimage_url content parts
Web searchSupportedweb_search
Reasoning effortlow · medium · high · xhigh · maxreasoning_effort: "medium" default medium

Errors & fallback routing

CodeWhat it meansWhat to do
401Missing or invalid API key.Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active.
429Rate limited, or the upstream provider is throttling the request.Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
5xxUpstream provider error or timeout.Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

GPT-6 Astra in production

What the model is good at.

  • Long-context synthesis

    Summarizes and cross-references information across large prompts when context allows.

  • Multi-step reasoning

    Breaks down complex problems into intermediate steps before answering. Relevant for `gpt-6-astra` workloads on LLM.API.

  • Tool-ready dialogue

    Works well in agent loops that call functions, browsers, or retrieval APIs.

  • Conversational UX

    Maintains coherent multi-turn assistant behavior for product chat surfaces. Reflects Gpt Astra positioning for this endpoint.

  • Analytical writing

    Produces clear analyses, comparisons, and decision memos from messy source material. Tuned to how teams typically call GPT-6 Astra.

What you get when you call GPT-6 Astra through LLM.API.

  • Unified AI Routing

    Reach GPT-6 Astra and sibling models through one OpenAI-compatible endpoint.

    One endpoint, every model.
  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale GPT-6 Astra.

    More performance, less spend.
  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

    Never go dark on users.
  • Observability

    Trace prompts, tokens, and errors for GPT-6 Astra alongside the rest of your stack.

    See every token, everywhere.
  • Drop-in SDKs

    Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

    Think tasks, not models.
  • Model Breadth

    Swap GPT-6 Astra for chat, media, or embedding alternatives without rewriting auth.

    Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

  • You want OpenAI-compatible chat completions through a single LLM.API key (GPT-6 Astra)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (GPT-6 Astra)
  • You need provider failover options exposed for this model id (GPT-6 Astra)
  • You need a general-purpose text model for assistants, agents, or content workflows (GPT-6 Astra)

Avoid if...

  • You require on-prem only deployment with no cloud inference
  • You need guaranteed real-time hard latency SLAs without benchmarking the provider
  • Your use case depends on unpublished proprietary benchmarks not listed here
  • You need pure embedding, OCR, or media generation instead of chat

LLM API rating for GPT-6 Astra

★ 4.4/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value
✓ Verified benchmark · LMArena + Artificial Analysis
5.0Production adoptionTop 1% of production API volume
4.4Benchmark qualityArtificial Analysis Intelligence Index 52.7 · LMArena text rank #64, Elo 1443
2.8Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 4.4Coding 4.4Vision & multimodal 4.3Tool & function calling 4.6Long context 4.6
How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

  1. Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
  2. Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
  3. Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

GPT-6 Astra benchmark scores

Intelligence index

This model53.
Tracked median24.

Scale: 0-100 index points

Output speed

This model54 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$10.00
Output$50.00

Bars compare input and output list prices for this model.

Intelligence index+121% vs median
GPT-6 Astra API: Endpoint & 30% Discount53
Median tracked model24

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed
54tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens
Input$10.00
Output$50.00

Provider list prices, before the LLM.API discount.

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

GPT-6 Astra uptime, last 30 days

30-Day Uptime
96.70%
Past Incidents (30d)
3
Error rate (24h)
9.27%

Last 30 days

26/30 days operational | 96.70% uptime

Availability tracked for this model. Full history on the LLM Uptime Status page or the status hub. LLM.API routes around provider outages automatically.

What developers say about OpenAI models

LMArena#64 · Elo 1443Arena coding#36 · Elo 1488Speed54 tokens/sSources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to GPT-6 Astra.

  • Long-horizon coding reports on GPT-6 Astra describe a clear step up over the GPT-5.x line, with the higher reasoning tiers seen as the sweet spot for planning and implementation in large (100K+ LOC) codebases.
  • The same field reports note that fast/ultra modes burn quota quickly and that very large refactors still stall, so teams tend to mix a cheap tier for routine calls with a reasoning tier for hard steps.
  • Community threads temper the hype: capability gains are acknowledged, but developers still report the usual failure modes on obscure reverse-engineering and modding work.

Frequently Asked Questions

  • How is GPT-6 Astra priced on LLM.API?

    Listed pricing metadata shows: In $10.00 / 1M tokens · Out $50.00 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Where is the canonical page for GPT-6 Astra?

    https://llmapi.ai/models/openai-gpt-6-astra/

  • How do I call GPT-6 Astra via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "gpt-6-astra" and your LLM.API key. See the code snippet on this page.

  • Which providers serve GPT-6 Astra?

    LLM.API currently lists: azure, openai. Availability can vary by region and account.

  • What is GPT-6 Astra?

    GPT-6 Astra is OpenAI's most capable model for complex reasoning, coding, computer use, research, and document creation. On LLM.API it is addressed as `gpt-6-astra`.

  • When should I choose GPT-6 Astra?

    Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need GPT-6 Astra.

  • What are limitations of GPT-6 Astra?

    Like other API models, GPT-6 Astra can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Is GPT-6 Astra a chat model?

    Yes—GPT-6 Astra is exposed as a chat/completions-style endpoint on LLM.API.

  • What is the context length for GPT-6 Astra?

    Reported context for GPT-6 Astra is 1.1M tokens. Always verify the active provider row if multiple providers are listed.

  • Does GPT-6 Astra support streaming?

    Yes—at least one listed provider advertises streaming.

  • What modalities does GPT-6 Astra support?

    GPT-6 Astra accepts text, image and produces text according to its architecture metadata on LLM.API.

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