GPT-6 Astra vs GPT-6.1 Sol: Frontier Reasoning or Near-Astra Value?

The deciding number is retries, not list price. On the illustrative coding task worked through below, one request costs $0.153 on GPT-6.1 Sol and $0.805 on Astra, so Astra pays for itself only when it saves a correction cycle that 6.1 Sol would need. GPT-6 Luna, at $0.10 input and $0.50 output, is the model for bulk extraction, classification and routing, not for either of these decisions.

What GPT-6.1 Sol changes

On 2026-09-29 OpenAI released GPT-6.1 Sol (gpt-6.1-sol), an upgrade to GPT-6 Sol at the same $2.00/$10.00 list price with cached input cut to $0.10 per million tokens, half of GPT-6 Sol's $0.20. OpenAI positions it as near-Astra intelligence at one-fifth of Astra's standard token prices. OpenAI reports that GPT-6.1 Sol matches GPT-6 Astra on DeepSWE v1.1 at roughly one-fifth of the cost and lands 6.4 percentage points above GPT-6 Sol's best result there. On OSWorld 2.0 (offline set) it comes within 2.1 points of Astra at maximum effort for roughly one-seventh of the cost per task, and on Terminal-Bench Science 0.1 it costs $5.47 per task against $23.80 for Astra, although Astra keeps the highest result at 68.1%. These are OpenAI's own evaluations, not independent measurements.

OpenAI did not release a GPT-6.1 Astra, so GPT-6 Astra remains its top model. GPT-6 Sol stays available at unchanged prices. GPT-6.1 Sol is in the API and in ChatGPT Work and Codex on Plus and Pro; it is not yet available in Chat. The benchmark table below compares GPT-6 Sol with Astra; test GPT-6.1 Sol on your own tasks before deciding that an Astra upgrade is still needed. If you already run GPT-6 Sol and only need to decide whether to move to the new version, see GPT-6.1 Sol vs GPT-6 Sol for the pricing, Codex credit and availability differences.

Read the table by the work you route to each model; the price column is the API's standard rate.

Astra leads GPT-6 Sol by 20 points on Terminal-Bench 4.0, not on bug fixes

OpenAI's own evaluations put the two models far apart on terminal work and close on repository bug fixes. On Terminal-Bench 4.0, which evaluates multi-step bash terminal problem solving, environment configuration, and command-line debugging, OpenAI reports that GPT-6 Astra achieves 57.9% compared to 37.3% for GPT-6 Sol. On DeepSWE v1.1, which benchmarks end-to-end bug resolution in real-world open-source repositories, Astra records 74.1% against Sol's 72.7%. The last column says what each row changes for a buyer.

Both models share an identical 1,050,000-token context window and a 128,000-token maximum output buffer. That is the API figure; inside ChatGPT the window depends on the plan and is much smaller, as ChatGPT context window by plan shows. Upgrading to Astra does not grant a larger context window.

API prices: Astra costs 5x Sol at every token type

Because OpenAI bills input, cache reads, cache writes and output at different rates, a request's real cost depends on how much context it reuses.

Direct OpenAI API rates for standard short-context requests (under 272,000 input tokens) establish a predictable pricing ratio. Standard input on Sol costs $2.00 per million tokens, while Astra costs $10.00 per million tokens. For outputs, Sol costs $10.00 per million tokens, whereas Astra costs $50.00 per million tokens. Both models bill prompt cache writes at a 25% surcharge over standard input ($2.50/M on Sol, $12.50/M on Astra) and offer a 90% discount on cache reads ($0.20/M on Sol, $1.00/M on Astra). Read the table across a row to price one model, and down the last column for when each mode fits.

When an API request exceeds 272,000 input tokens, OpenAI automatically activates long-context tier pricing across the entire transaction. Above this threshold, base input and cache rates double, while output token rates increase by 50%. Engineering teams processing massive codebases must implement context window pruning to prevent requests from crossing the 272,000-token boundary unnecessarily.

Consider an illustrative engineering task involving 10,000 standard input tokens, 80,000 cached repository tokens, 10,000 cache-write tokens, and 10,000 generated output tokens. On GPT-6 Sol Standard, the total compute cost equals $0.161 ($0.020 + $0.016 + $0.025 + $0.100). On GPT-6 Astra Standard, the exact same interaction costs $0.805 ($0.100 + $0.080 + $0.125 + $0.500). On GPT-6.1 Sol, the cheaper cache reads bring the same interaction to $0.153 ($0.020 + $0.008 + $0.025 + $0.100). To pay for itself, Astra must eliminate at least one subsequent correction cycle or resolve bugs that Sol fails to diagnose.

Where Astra earns its price: long agent runs in Codex

Astra's case is strongest in long agent loops that run shell commands, read the output and iterate until the tests pass.

Astra introduces native asynchronous tool execution. In legacy agent loops, when a model requests three shell commands—such as running a test suite, checking git status, and querying a database—the execution harness must execute each command sequentially, pausing model reasoning until all results return. Astra allows tools to execute concurrently in background threads, streaming partial outputs back into the reasoning context and reducing wall-clock latency during complex builds.

Furthermore, Astra supports mid-turn steering. If an autonomous agent begins refactoring an incorrect module or enters an infinite recursive loop during a test run, developer harnesses can inject steering directives into the active reasoning stream without aborting the conversation or losing the preceding context. This matters most for CI agents that run under strict time budgets.

In Codex, Astra pairs with an experimental context-management mode (features.context_management.experimental_mode). It keeps notes and summaries across successive context windows, so an agent working on a multi-hour migration can recall constraints set dozens of turns earlier. It does not remove token accounting: every window is still billed.

Moving an API integration from GPT-6 Sol to Astra

Changing the model ID is only part of the move. The table lists each setting that differs and what to change.

Before switching all traffic, run the move in three steps:

1. Shadow-test 5% of traffic: Mirror 5% of production Sol traffic to Astra, capturing side-by-side completions, unit test pass rates, and developer acceptance scores without exposing end users to latency variations.
2. Keep cache prefixes stable: Because Astra's cache write surcharge is $12.50 per million tokens, system prompts that change on every query inflate spend quickly. Put static repository summaries and tool definitions at the beginning of the prompt so they earn the 90% cache-read discount.
3. Track tokens and retries per task: Log thinking time, tool calls, retry rates and output tokens for every agent run, and rerun a fixed set of multi-turn tasks after the switch, so a regression or a runaway token loop shows up before it reaches production.