# OpenAI’s GPT-6.1 Sol Pressures High-End Model Pricing

> The efficiency-first model brings near-flagship agentic performance to commodity price points, with a 95% cache discount that reshapes the cost of long agentic runs.

*Published 2026-09-29 · By Marcus Vance*

GPT-6.1 Sol is OpenAI’s efficiency-first upgrade to GPT-6 Sol, a model that the company says nearly matches GPT-6 Astra’s intelligence on agentic coding, computer use, and professional work at one-fifth of Astra’s standard token prices.

OpenAI has released GPT-6.1 Sol, an efficiency-focused model that the company says nearly matches its flagship GPT-6 Astra on agentic coding, computer use, and professional work at one-fifth of Astra’s standard token prices. The model is available immediately through ChatGPT Work, Codex, and the API, and it introduces a cached-input price of $0.10 per million tokens, a 95% discount from standard input pricing. The launch lands as enterprise buyers increasingly tie model selection to cost-per-task rather than raw benchmark leadership.

The launch continues OpenAI’s practice of segmenting the GPT-6 family by price-performance rather than by capability alone. GPT-6 Astra remains the top-tier model, GPT-6 Sol was the previous efficiency option, and GPT-6.1 Sol now replaces that option with a model that narrows the gap to Astra on several benchmarks while undercutting both predecessors on cost. OpenAI describes GPT-6.1 as the latest model family in the GPT-6 series, with capabilities comparable to Astra and what the company calls an unmatched combination of speed and affordability.

## What is GPT-6.1 Sol and how does it fit into OpenAI’s model lineup?

GPT-6.1 Sol is an upgrade to GPT-6 Sol, the model OpenAI positioned as the cost-efficient tier of the GPT-6 generation. According to OpenAI’s announcement, the new model nearly matches GPT-6 Astra’s intelligence on agentic coding, computer use, and professional work at one-fifth of Astra’s standard input and output token prices. The system card addendum for GPT-6.1 Sol states that the model delivers capabilities comparable to those of GPT-6 Astra with an unmatched combination of speed and affordability.

The model naming reflects a broader pattern in OpenAI’s lineup: Astra is the flagship, while Sol variants carry the efficiency role. GPT-6.1 Sol is not a new frontier in raw capability, but it is a meaningful step in the cost-performance frontier. The release signals that OpenAI is competing on unit economics, not only on benchmark scores, as agentic applications multiply the number of tokens consumed per completed task.

## How does GPT-6.1 Sol compare with GPT-6 Astra on benchmarks?

OpenAI published benchmark results that position GPT-6.1 Sol close to Astra on agentic tasks while widening the gap over GPT-6 Sol. On DeepSWE v1.1, which measures software-engineering agents, GPT-6.1 Sol matches GPT-6 Astra’s score at roughly one-fifth the cost and exceeds GPT-6 Sol’s best score by 6.4 percentage points. On OSWorld 2.0’s offline set, GPT-6.1 Sol comes within 2.1 percentage points of Astra at roughly one-seventh the cost per task, and it outperforms GPT-6 Sol by seven percentage points at less than half the cost.

The science benchmark shows the largest relative gain. On Terminal-Bench Science 0.1, GPT-6.1 Sol more than doubles GPT-6 Sol’s score at maximum reasoning effort while costing less than half as much per task. OpenAI also reported a reduction in factual errors on difficult prompts: at low reasoning effort, the error rate falls from 11.4% for GPT-6 Sol to 7.7% for GPT-6.1 Sol, a reduction of approximately 32%.

| Benchmark | GPT-6.1 Sol result | Comparison to GPT-6 Astra | Comparison to GPT-6 Sol | Cost note |
| --- | --- | --- | --- | --- |
| DeepSWE v1.1 | Matches Astra's score | Match | +6.4 points vs Sol's best | Roughly 1/5 Astra's cost |
| OSWorld 2.0 offline | Within 2.1 points of Astra | -2.1 points | +7 points vs Sol | Roughly 1/7 Astra's cost per task |
| Terminal-Bench Science 0.1 | More than doubles Sol's score | Not disclosed | >2x Sol at max reasoning |  We’re introducing GPT‑6.1 Sol, an upgrade to GPT‑6 Sol that nearly matches GPT‑6 Astra’s intelligence on agentic coding, computer use, and professional work at one-fifth of Astra’s standard input and output token prices.OpenAI

The choice to publish cost-per-task comparisons alongside benchmark scores is itself a signal. OpenAI’s announcement includes cost multiples for DeepSWE, OSWorld 2.0, and Terminal-Bench Science 0.1, framing performance in terms of what a task costs to run, not just how often it succeeds. That framing aligns with the way enterprises budget for agentic workloads, where a 2-point benchmark gap is often less important than a 5x or 7x cost difference.

## What are the deployment options for GPT-6.1 Sol?

GPT-6.1 Sol is available today through three surfaces: ChatGPT Work, Codex, and the OpenAI API. The API model identifier is gpt-6.1-sol, and the pricing is $2 per million input tokens, $0.10 per million cached input tokens, and $10 per million output tokens, per the API documentation. OpenAI’s deployment-safety addendum indicates the model has gone through the same evaluation process as other GPT-6 models, which is relevant for enterprises with security review requirements.

- Choose a deployment surface: ChatGPT Work for professional use, Codex for coding agents, or the API for custom applications.
- Set the model identifier to gpt-6.1-sol in API calls to access the new tier.
- Design prompts and agent loops to reuse context so the 95% cached-input discount applies.
- Compare task-level cost using OpenAI’s benchmark data for DeepSWE v1.1, OSWorld 2.0, and Terminal-Bench Science 0.1.
- Monitor factual error rates on difficult prompts, which OpenAI reports at 7.7% for low-reasoning-effort runs.

The deployment options reflect OpenAI’s strategy of making the model available in both consumer-facing products and developer-facing APIs. The ChatGPT Work integration gives professional users access without requiring API engineering, while the Codex integration targets software engineering agents directly. For enterprises, the API route preserves control over caching, evals, and cost monitoring.

Developers should note that the cached-input discount applies only to cached tokens, not to the first read of a context. Agent frameworks that structure prompts to maximize cache reuse will see the largest savings. OpenAI’s API documentation does not specify cache-eviction policies in the release announcement, so teams with very long contexts should measure cache-hit rates in their own workloads.

## What are the safety and governance considerations for GPT-6.1 Sol?

The GPT-6.1 Sol system card addendum, published on OpenAI’s deployment-safety site, states that the model delivers capabilities comparable to GPT-6 Astra. That designation triggers the same safety-evaluation and monitoring framework used for the broader GPT-6 family. Enterprises subject to internal AI governance policies will likely treat GPT-6.1 Sol as a high-capability model even though its price is lower.

## What comes next for the GPT-6 family?

GPT-6.1 Sol’s release suggests that OpenAI is iterating on efficiency-focused variants between flagship launches. The GPT-6 generation now spans three tiers: GPT-6 Astra at the top, GPT-6.1 Sol in the middle, and GPT-6 Sol as the entry point. The gap between GPT-6.1 Sol and Astra is narrow enough on several benchmarks that the next efficiency release could plausibly erase it entirely on some tasks, at which point the flagship tier would need a new capability advantage to justify its price.

The 95% cache discount also sets a benchmark for the broader API market. Competitors offering cached input at higher prices will face pressure to match, particularly for agentic workloads where caching is the dominant cost lever. OpenAI’s API documentation lists the discount explicitly, making it easy for developers to calculate savings and for rivals to be measured against it.

For now, GPT-6.1 Sol is the model to beat on cost-adjusted agentic performance. The release provides a concrete data point: near-flagship capability at one-fifth the token price, with benchmark scores that quantify the remaining gap to Astra. The next stage of the frontier-model race may be less about who leads a single benchmark and more about who delivers the lowest cost per successful task.

## Sources

1. [Benchmark results for DeepSWE v1.1, OSWorld 2.0, Terminal-Bench Science 0.1, and the factual error rate reduction from 11.4% to 7.7%.](https://openai.com/index/introducing-gpt-6-1-sol/)
2. [GPT-6.1 is the latest model family in the GPT-6 series, delivering capabilities comparable to GPT-6 Astra with an unmatched combination of speed and affordability.](https://deploymentsafety.openai.com/gpt-6-1-sol)
3. [API pricing of $2.00 per million input tokens, $0.10 per million cached input tokens, and $10.00 per million output tokens, plus near-Astra performance positioning.](https://developers.openai.com/api/docs/models/gpt-6.1-sol)

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Source: https://aiintelreport.com/frontier-models/openai-gpt-6-1-sol-pricing-pressure
Index: https://aiintelreport.com/llms.txt · Full text: https://aiintelreport.com/llms-full.txt