GPT-6.1 Sol Is in the API: Near-Astra at One-Fifth the Cost

OpenAI released GPT-6.1 Sol in its API on September 29, priced at $2/$10 per million input/output tokens versus Astra's $10/$50, with cached input at $0.10 per million against Astra's $1.00. The model scores 75.2% on DeepSWE v1.1, beating Astra's 74.1%, and cuts per-task cost from $4.43 on Astra to $0.65, though it trails Astra 54.5% to 57.3% on OSWorld 2.0 computer use. Migration requires auditing reasoning_effort values, moving tool calling to the Responses API, and accepting 63% slower time to first token.

OpenAI shipped GPT-6.1 Sol on September 29 — the same day as DevDay — and it quietly rewrites the cost math for most production AI workflows. The model scores 75.2% on DeepSWE v1.1, beating Astra’s 74.1% on that coding benchmark, at $2/$10 per million input/output tokens versus Astra’s $10/$50. Prompt cache is now $0.10 per million — half of what GPT-6 Sol charged a week earlier. If you’re running agentic coding loops on Astra today, there’s arithmetic to do. The Pricing Reality The headline number is 5x cheaper than Astra on standard input and output. What doesn’t make the headline: cached input just halved again. GPT-6 Sol was $0.20 per million cached tokens. GPT-6.1 Sol is $0.10. Astra is $1.00. For agent loops that pass large context repeatedly — which is most of them — that’s a 10x cache advantage over Astra and a meaningful additional cut from last week. | Model | Input | Cached Input | Output | |---|---|---|---| | GPT-6 Astra | $10.00 | $1.00 | $50.00 | | GPT-6.1 Sol | $2.00 | $0.10 | $10.00 | | GPT-6 Luna | $0.10 | — | $0.50 | OpenAI pricing page https://developers.openai.com/api/docs/pricing . On the DeepSWE v1.1 benchmark https://artificialanalysis.ai/articles/gpt-6-1-sol-replaces-gpt-6-sol-after-just-7-days-with-near-astra-intelligence , which runs real-world software engineering tasks across live codebases, the per-task cost drops from $4.43 on Astra to $0.65 on GPT-6.1 Sol — 6.8x cheaper per completed task. The model also supports Batch and Flex processing modes at 50% off standard rates for async workflows. What "Near-Astra" Actually Means OpenAI’s marketing says "near-Astra performance." On coding specifically, that’s underselling it: GPT-6.1 Sol scores 75.2% versus Astra’s 74.1% on DeepSWE v1.1. On computer use and broader agentic tasks OSWorld 2.0 , the gap widens to 2.8 percentage points — 54.5% for Sol versus 57.3% for Astra. That gap is real but narrow, and at one-fifth the cost, it rarely justifies using Astra for routine agent work. Compared to its predecessor, GPT-6 Sol, this upgrade is significant: +6.4 points on DeepSWE, +7 on OSWorld, and a factual error rate of 7.7% versus 11.4%. OpenAI pushed this within seven days of GPT-6 Sol’s launch — which is either a sign of rapid iteration or an admission that the original Sol was underbaked. Three Migration Catches Swapping gpt-6-sol for gpt-6.1-sol works for most setups. Watch for these three before deploying: Reasoning effort values changed. GPT-6.1 Sol dropped support for reasoning effort: "none" and "minimal" . The minimum is now "low" . This fails silently in some implementations — audit your code before switching. Tool calling requires the Responses API. If your application uses function calling through the Chat Completions endpoint, you need to migrate to the Responses API first. GPT-6.1 Sol does not support tool calling on Chat Completions. Latency increases. Time to first token is 63% slower than GPT-6 Sol; output speed is 13% slower. More reasoning depth per token produces better results but adds latency. For interactive applications, benchmark before switching in production. When to Keep Astra For most agentic coding workloads, GPT-6.1 Sol is the new default. Keep Astra for three scenarios: one-shot creative coding early community feedback puts Sol behind competitors on UI generation and game scaffolding ; latency-critical paths where Astra Ultrafast is already live at 300 tokens per second Sol Ultrafast is still "coming soon" ; and edge cases where that 2.8-point computer use gap in production metrics matters more than a 5x cost reduction. What to Do Now The model is live as gpt-6.1-sol across the API and ChatGPT Work https://openai.com/index/introducing-gpt-6-1-sol/ . The switch is a one-line model ID change for most setups. Check your reasoning effort values, confirm you’re on the Responses API if you use tool calling, and run a cost estimate against your current Astra spend. Read the full DevDay 2026 recap https://www.infoq.com/news/2026/10/openai-devday-2026/ for the rest of what shipped. The math on Sol is hard to argue with for coding-heavy workloads.