TITLE: OpenAI's GPT-6.1 Sol Delivers Astra Performance at a Fifth of the Cost DATE: 2026-10-05 COMPANY: OpenAI TOPIC: Model Releases SUMMARY: OpenAI launched GPT-6.1 Sol at DevDay 2026 on September 29, matching the intelligence of its flagship GPT-6 Astra model for agentic coding, computer use, and professional work at $2 per million input tokens, compared to Astra's $10. The model is available through the API and ChatGPT Work immediately, with a 1.05 million token context window and a cached input price of $0.10 per million tokens. WHAT CHANGED: OpenAI held its annual DevDay developer conference on September 29, 2026, seven days after releasing GPT-6 Sol. The headline release was GPT-6.1 Sol, a model the company describes as delivering near-Astra intelligence for agentic coding, computer use, and professional work at one-fifth of GPT-6 Astra's token costs. The pricing is the central claim. GPT-6 Astra, OpenAI's most capable model, costs $10 per million input tokens and $50 per million output tokens. GPT-6.1 Sol costs $2 and $10 respectively, placing Astra-class performance within reach of workflows that were economically unfeasible at Astra's pricing. Cached inputs drop further to $0.10 per million, which is 50 percent below the cached rate for GPT-6 Sol. On DeepSWE v1.1, an industry benchmark measuring performance on complex software engineering tasks in real codebases rather than synthetic tests, GPT-6.1 Sol matches GPT-6 Astra's score and exceeds GPT-6 Sol's best result by 6.4 percentage points at lower reasoning effort. The model supports a 1.05 million token context window and can return up to 128,000 tokens per call. An Ultrafast variant with up to 8x faster generation in Codex is expected within days of launch. Access opened immediately on September 29 via the API and through ChatGPT Work and Codex. It is not available in the standard ChatGPT Chat interface. All Plus, Pro, Business, Enterprise, and Edu accounts have access. WHY IT MATTERS: Agentic work becomes affordable for small teams. The 5x cost reduction on Astra-class performance means that multi-step agent tasks such as autonomous code review, iterative document analysis, and research synthesis move from premium experiments to routine deployments for businesses with modest AI budgets. Cached input pricing resets architecture decisions. At $0.10 per million cached tokens, any architecture that repeatedly injects a large system prompt or knowledge base becomes dramatically cheaper. Retrieval-augmented applications, persistent agent contexts, and compliance check pipelines all benefit without code changes. The 1.05 million token context window removes chunking overhead. For operators processing long documents, including contracts, board papers, regulatory submissions, and large codebases, a context window of this size means entire document sets can be processed in a single call. The operational complexity of splitting, tracking, and reassembling chunks disappears. GPT-6.1 Sol introduces a new tier in the OpenAI model family. OpenAI now has two distinct price and performance points at the high end: Astra for tasks that require maximum capability, and Sol at 80 percent lower cost for work where Astra-level performance is sufficient. Operators can route tasks between tiers rather than paying Astra prices for everything. The benchmark result shifts competitive positioning. A model that matches Astra on DeepSWE v1.1 at one-fifth the cost creates pressure across the market. Competing providers running Astra-class equivalents at higher prices will face a measurable cost disadvantage in developer and enterprise sales. DAVID & GOLIATH ANALYSIS: OpenAI's DevDay 2026 delivered the most commercially significant pricing shift in agentic AI this year. The gap between what top-tier models can do and what operators can afford to run them on has been the practical constraint on AI deployment for most businesses. GPT-6.1 Sol does not close that gap entirely, but it narrows it substantially and immediately. The implication for businesses considering agentic automation is direct. Workflows that were previously modelled at Astra pricing and found too expensive should be revisited this week. The numbers changed. A research loop that cost $500 per run at Astra rates costs $100 at Sol rates. That is the difference between a quarterly experiment and a weekly production process. The cached input pricing deserves particular attention. At $0.10 per million tokens, building systems with persistent context, large instruction sets, or knowledge bases loaded at every call stops being a budget concern and becomes an architecture choice. Operators who have been keeping their system prompts lean for cost reasons now have room to build richer, more capable agents without the token maths fighting against them. RELEVANT SYSTEMS: AI Growth Engine, Employee Amplification Systems SOURCE URL: https://davidandgoliath.ai/daily-ai-briefing/openai-gpt-61-sol-astra-performance-fifth-price-devday FEED URL: https://davidandgoliath.ai/daily-ai-briefing/feed --- Published by David & Goliath | https://davidandgoliath.ai Daily AI Briefing: one AI development per day, decoded for business operators. This is a structured companion file optimised for LLM retrieval and citation.