OpenAI GPT-6 Astra on OpenRouter – pricing, performance, and community reactions
TL;DR – What GPT‑6 Astra is and why it matters
GPT‑6 Astra is OpenAI’s flagship model for demanding end‑to‑end workloads, offering a 1 050 000 token context window, tool‑calling, and strong long‑horizon agentic abilities. It costs $10 per M input tokens and $50 per M output tokens (with cache discounts), delivers the highest throughput (57 tok/s) and lowest latency (3.38 s) among providers on OpenRouter, and ranks in the top 5 % of models on several Artificial Analysis benchmarks.
Model overview
- Release date: September 4 2026
- Context window: 1 050 000 tokens (≈1 M) with up to 128 000 completion tokens
- Supported modalities: Text, PDFs, images; tool calling via
tools/tool_choice - Primary use cases: Advanced analysis, software engineering, deep research, scientific work, and long‑horizon agentic tasks that involve browsers or computers.
Pricing details (OpenRouter listings)
| Provider | Listed input price | Listed output price | Cache read price | Weighted‑average input | Weighted‑average output |
|---|---|---|---|---|---|
| OpenAI Flex | $5 / M | $25 / M | $0.50 / M | $2.44 / M | $50.08 / M |
| Azure | $10 / M | $50 / M | $1.00 / M | $2.48 / M | $51.12 / M |
| OpenAI (standard) | $10 / M | $50 / M | $1.00 / M | — | — |
| Azure (US) | $11 / M | $55 / M | $1.10 / M | $2.52 / M | $57.63 / M |
| OpenAI Fast | $20 / M | $100 / M | $2.00 / M | $3.65 / M | $105 / M |
Weighted averages reflect actual usage after caching and discounts, which are often substantially lower than the listed rates.
Performance metrics (median over the last 3 days)
- Throughput: 57 tokens / s (OpenAI Flex, best across providers)
- Latency (P50): 3.38 s (OpenAI Flex)
- End‑to‑end latency: 8.73 s (OpenAI Fast) – 10.14 s (OpenAI Flex)
- Tool‑call error rate: 0.30 % (Azure US) – 4.05 % (OpenAI standard)
- Structured‑output error rate: 1.54 % (OpenAI) – 8.62 % (Azure)
- Cache hit rate: ~90 % across providers
- Uptime (3 d): 100 % overall; availability 98.36 % (OpenRouter routes to the next healthy provider on error).
Benchmark rankings (Artificial Analysis)
| Benchmark | Score % | Percentile rank |
|---|---|---|
| Artificial Analysis Intelligence Index | 54.7 % | >95 % |
| Artificial Analysis Coding Index | 76.9 % | >96 % |
| Artificial Analysis Agentic Index | 51.6 % | >91 % |
| GPQA Diamond (graduate‑level reasoning) | 96.1 % | |
| HLE (Humanity’s Last Exam) | 54.7 % | |
| AA‑LCR (long‑context reasoning) | 80.7 % | |
| CritPt (research‑level physics) | 31.7 % | |
| SciCode (scientific Python) | 56.5 % | |
| AA‑Omniscience Accuracy | 62.6 % | |
| AA‑Omniscience Non‑Hallucination | 48.7 % |
All metrics are sourced from the Artificial Analysis platform.
Real‑world usage signals (top apps)
- Hermes Agent (Nous Research) – 2.24 B tokens; persistent AI agent with 40+ tools, memory across sessions, and scheduled automations.
- Codex – 1.05 B tokens; coding assistant for building and shipping software.
- Cursor – 1 B tokens; code‑centric IDE integration.
- omp – 861 M tokens; unspecified workflow.
- Claude Code – 828 M tokens; Anthropic‑style agentic coding tool.
These high‑volume apps suggest Astra is being used for heavy‑duty coding, agentic automation, and document‑intensive pipelines.
Community feedback on Hacker News (selected insights)
simonw: “Astra may be more expensive, but with a budget of 10 cents per M tokens you get something so much better than other models. Astra also uses fewer tokens for better results.”
jjcm: “The vision capabilities are outstanding – the model recreated complex SVG designs with high fidelity, far surpassing Opus 5.”
XCSme: “Astra’s SVG generation is impressive; see the side‑by‑side comparison on aibenchy.com.”
MisterMunchkin: “$10 / $50 is incredibly expensive compared to Chinese models that cost cents; many companies are cutting back on usage.”
friendlypenguin: “I was hoping Astra would be cheaper than Opus, but the price is a barrier for most workloads.”
vb‑8448: “In the Codex app Astra feels faster than SOL even though its TPS is lower.”
d2p: “Tool‑call failure rate is high (≈5 %) for OpenAI’s provider versus <0.5 % on Azure.”
frenchtoast8: “OpenRouter support is poor – accounts get suspended without recourse, and refunds are denied.”
Overall sentiment: users praise Astra’s capabilities (especially vision and agentic reasoning) but criticize its high price and occasional reliability issues (tool‑call errors, provider‑specific quirks, and support responsiveness).
Practical considerations for developers
- Cost‑efficiency: Even though the listed price is high, the weighted‑average cost ($2.44 / $50.08 per M tokens) can be lower if you benefit from cache hits (≈90 %). Evaluate token usage carefully; Astra often requires fewer tokens to achieve the same quality.
- Provider choice: Azure offers slightly higher latency (5.66 s) but a lower tool‑call error rate (0.39 %) than OpenAI’s standard endpoint (4.05 %). Use OpenRouter’s routing modes (Balanced, Nitro, Exacto) to prioritize cost, speed, or tool‑calling accuracy.
- Tool calling: Astra fully supports function calling and structured JSON outputs. For mission‑critical pipelines, monitor the tool‑call error metric and consider fallback to Azure if errors become frequent.
- Vision tasks: Community reports indicate Astra’s image‑to‑SVG generation outperforms prior models; leverage this for design automation, UI prototyping, or scientific diagram synthesis.
- Access tiers: Astra is available to Plus, Pro, and Business users on OpenRouter, but some users report limited resets or throttling on Plus plans.
How to get started (quick‑start snippet)
export OPENROUTER_API_KEY=sk-or-v1-...
curl -N https://openrouter.ai/api/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-d '{
"model": "openai/gpt-6-astra",
"stream": true,
"messages": [{"role": "user", "content": "Explain quantum entanglement to a 10‑year‑old"}]
}'
Add "reasoning": {"enabled": true} to receive step‑by‑step thinking tokens.
Outlook
GPT‑6 Astra represents a clear step up in long‑context, tool‑augmented reasoning, positioning it as the go‑to model for complex agentic workflows. Its high price point may limit widespread adoption, especially for cost‑sensitive startups, but the token‑efficiency gains and superior vision capabilities could justify the expense for high‑value applications such as automated code review, scientific research assistants, and design generation. Continued monitoring of provider reliability, tool‑call error rates, and OpenRouter’s routing policies will be essential for production deployments.
Sources
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