GPT-6.1 Sol
OpenAI updated frontier reasoning model with enhanced long-horizon planning, refined code synthesis, and reduced hallucination rates.
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Radar-Achsen sind aus Kategorie-Scores approximiert, wo direkte Benchmark-Messungen fehlen — siehe Methodik.
Stärken & Trade-offs
Wo das Modell gewinnt
- Near-Astra intelligence at a fraction of the cost
- Top SWE-bench-tier verified coding
- Cheapest cache reads in the GPT-6 family
Worauf achten
- Still below Astra on frontier reasoning
- Closed weights; usage-based pricing only
Killer-Anwendungsfälle
Developer-Quickstart & API
OPENAI_API_KEY — API-Schlüssel holencurl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "gpt-6.1-sol",
"input": "Refactor this function for O(n) time and explain the change."
}'from openai import OpenAI
client = OpenAI() # reads OPENAI_API_KEY
resp = client.responses.create(
model="gpt-6.1-sol",
input="Refactor this function for O(n) time and explain the change.",
)
print(resp.output_text)const res = await fetch("https://api.openai.com/v1/responses", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({ model: "gpt-6.1-sol", input: "Refactor this function for O(n) time and explain the change." }),
});
console.log((await res.json()).output_text);Offizielle Ressourcen & Links
Hosts & Latenz vergleichen
Durchsatz, TTFT und Cache-Preise bei Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock und Azure.
Ergebnisse nach Quelle
Kategorie-Punkte
Benchmark-Ergebnisse
Direkte Live-Duelle
Live-Spezifikationen & Architektur
- Near-Astra coding performance at roughly one-fifth the token price
- 95% discount on cached input tokens for repo-scale prompts
- Tuned for agentic coding and computer use
Geschätzte Monatsrechnung
Zitieren & Einbetten
Loading badge markdown...@misc{llmpodium2026gpt61sol,
title = {Benchmark Analysis and Intelligence Evaluation of GPT-6.1 Sol},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gpt-6-1-sol}
}Score-Badge einbetten
Fügen Sie Ihrem GitHub- oder Hugging-Face-README ein Live-Podium-Score-Badge hinzu — es verlinkt auf das Live-Ranking.
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