AIREITERGPT-6.1 Sol launched through the API, Codex, and ChatGPT Work, but its main advantage is narrower than the launch slogan suggests. The standard API price is $2 per million input tokens and $10 per million output tokens; the meaningful change versus GPT-6 Sol is cached input at $0.10 per million tokens. OpenAI’s reported benchmarks make Sol 6.1 compelling for coding and computer-use pilots, while the evidence is still too vendor-controlled to justify an automatic production switch.
What GPT-6.1 Sol actually changes
GPT-6.1 Sol is OpenAI’s September 29, 2026 release with the API identifier gpt-6.1-sol. OpenAI positions it for agentic coding, computer use, document work, and other multi-step professional tasks. The official model documentation lists a 1.05-million-token context window, reasoning, tools, and image input.
The availability boundary matters. The model is listed for the API, Codex, and ChatGPT Work, but not ordinary ChatGPT chat at launch. That makes this primarily a developer and work-agent release, not a general ChatGPT upgrade.
Early user reports are mixed, so they should not substitute for fixed-task testing. In an r/OpenAI discussion, u/Foreign_Helicopter24 described a cheap successful result, while u/shuwatto reported a PCB task where Opus 5.5 outperformed Astra; these are individual experiences, not controlled tests.
GPT-6.1 Sol API pricing: the part that changed
The standard rates below are listed in OpenAI’s pricing documentation and reported consistently across launch-day coverage. They apply to requests within the short-context pricing tier.
| Billing item | GPT-6.1 Sol | GPT-6 Sol | GPT-6 Astra |
|---|---|---|---|
| Input / 1M tokens | $2.00 | $2.00 | $10.00 |
| Cached input / 1M tokens | $0.10 | $0.20 | $1.00 |
| Cache writes / 1M tokens | $2.50 | $2.50 | $12.50 |
| Output / 1M tokens | $10.00 | $10.00 | $50.00 |
The important comparison is not “Sol 6.1 is cheaper than Sol 6” across the whole invoice. Ordinary input and output prices are unchanged. Cached input is the exception: GPT-6.1 Sol halves GPT-6 Sol’s $0.20 rate and is 95% below its own standard input rate.
For example, a request using 100,000 cached input tokens, 10,000 ordinary input tokens, and 5,000 output tokens costs about $0.08 before cache writes, tools, retries, or human review:
- Cached input: 100,000 × $0.10 / 1,000,000 = $0.01.
- Ordinary input: 10,000 × $2 / 1,000,000 = $0.02.
- Output: 5,000 × $10 / 1,000,000 = $0.05.
The same illustrative mix is about $0.09 on GPT-6 Sol and $0.45 on GPT-6 Astra. It is a rate-card example, not a measured production run.
Long prompts change the calculation. OpenAI’s pricing page applies higher rates above 272,000 input tokens: $4 per million input tokens, $0.20 per million cached input tokens, and $15 per million output tokens. The higher rate applies to the request, so a 300,000-token prompt should not be budgeted using the headline $2/$10 numbers.
Caching only helps when the reusable prefix actually matches the provider’s caching rules. Stable system instructions, tool definitions, and repeated policy text are candidates; rewriting the beginning of every prompt can remove the expected saving.
What the published evidence supports—and what it does not
Use the vendor-reported benchmark evidence to choose a pilot, not to claim universal superiority.
| Evaluation | GPT-6.1 Sol result | Comparison that matters |
|---|---|---|
| DeepSWE v1.1 | 75.2% at high effort | Roughly level with GPT-6 Astra; 6.4 points above GPT-6 Sol in the reported table |
| OSWorld 2.0 offline | 71.4% at max effort | About 2.1 points behind Astra, at a much lower reported task cost |
| AutomationBench | 36.1% at max; 35.4% at medium | Below Astra’s 41.4% peak and below some higher-effort Claude results |
| Terminal-Bench Science 0.1 | 57.0% at max | Behind Astra’s 68.1%; still more than twice the reported GPT-6 Sol result |
The strongest case is coding. BitsMinds reported $0.65 per DeepSWE task for GPT-6.1 Sol versus $4.43 for Astra, alongside the 75.2% versus 74.1% figures in its selected comparison. That is a persuasive cost-performance signal, but the score and cost came from launch materials rather than an independent rerun.
Computer use is a similar value story, not a clean win: launch figures put GPT-6.1 Sol at 71.4% on OSWorld 2.0 versus Astra’s 73.5%, with reported task costs of $1.27 and $9.44. Business automation is the warning label. The model can beat a competitor at one effort setting and lose at another, so “near-Astra” should not be read as “best for every workflow.”
OpenAI reported a low-effort factual-error rate falling from 11.4% on GPT-6 Sol to 7.7% on GPT-6.1 Sol. That is a 3.7 percentage-point reduction, but the source does not by itself establish how the model performs on your documents, tools, or approval rules.
What the API integration requires
A model-name swap can fail even when the endpoint accepts the new identifier.
- Model ID:
gpt-6.1-sol. - Reasoning effort: OpenAI’s model documentation lists
low,medium,high,xhigh, andmax;noneandminimalare not supported. - Tool calls: OpenAI’s model documentation indicates that tool calling requires the Responses API. Chat Completions remains suitable for requests without tools.
- Context pricing: the 272K-token threshold changes the rate card.
- Output limit: The developer documentation lists up to 128,000 output tokens.
- Modalities: the same specification lists text and image input; it does not list native audio or video input.
Before changing a production default, replay a fixed task set with the same tools, permissions, acceptance criteria, and effort setting. Record accepted-result rate, retries, total token cost, elapsed time, and reviewer minutes. A model that is 80% cheaper per token can still be more expensive per accepted result if it creates additional repair work.
Who should switch first
Teams on GPT-6 Sol should run a canary. The base input and output prices do not increase, cached input becomes cheaper, and the reported coding and computer-use results improve. The migration case is strongest when the application reuses long prefixes and already supports the Responses API.
Teams paying for Astra should route routine work to Sol 6.1, not remove Astra. Coding tickets with tests, document extraction with clear validation, and repeatable browser tasks are sensible first candidates. Keep Astra for scientific research, exception-heavy workflows, or decisions that are difficult to verify.
Teams with no reliable acceptance test should wait. Benchmark proximity cannot replace a quality gate. If nobody can tell whether an agent completed the task correctly, lower token pricing is not a safe reason to change models.
GPT-6.1 Sol FAQ
Is GPT-6.1 Sol cheaper than GPT-6 Sol?
Only partly. Standard input and output remain $2 and $10 per million tokens. Cached input falls from $0.20 to $0.10, so cache-heavy agents get the clearest direct saving.
Is GPT-6.1 Sol as capable as GPT-6 Astra?
On reported launch benchmarks, it is close on coding and computer use but behind on some business-automation and science results. Treat “near-Astra” as a workload hypothesis to test, not a blanket replacement claim.
What is the exact GPT-6.1 Sol API model name?
Use gpt-6.1-sol in the API request.
Can regular ChatGPT users select GPT-6.1 Sol?
The launch availability reviewed here covers the API, Codex, and ChatGPT Work. It was not listed for ordinary ChatGPT chat at launch.
Does GPT-6.1 Sol support tool calling?
Launch documentation indicates that tool calling requires the Responses API. Test the endpoint and tool schema in staging before switching an existing integration.
The practical routing rule is simple: use GPT-6.1 Sol first where the task is repeatable, cache-heavy, and verifiable; keep Astra where a missed edge case costs more than the model call.