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OpenAI
GPT-6 Sol
GPT-6 Sol is a chat model from OpenAI, measured by Artificial Analysis at roughly 126 output tokens per second with a notably long time to first token.
Input from
$1.00 / 1M tokens
across 2 providers
API Pricing
Cheapest on OpenRouter — 40% below avg| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $1.00 | $5.00 | $0.100 | |
| $2.00 | $10.00 | $0.200 | |
| $2.00 | $10.00 | $0.200 |
Prices updated daily. Last check: Sep 23, 2026
GPT-6 Sol pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Intelligence
47.5 / 100
Output Speed
126 t/s
Latency (TTFT)
99.2s
Reasoning & Knowledge
- Humanity's Last Exam47.9%
Coding
- SciCode57.6%
Instruction & Long Context
- Long-Context Reasoning83.7%
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- OpenAI
- Modalities
- Text
Capabilities
- Tool Calling
- No
- Open Source
- No
Strengths & Limitations
Strengths
- Measured throughput of about 125.67 output tokens per second once generation begins, per Artificial Analysis
- Independent third-party latency and throughput benchmarking is available rather than vendor-reported numbers only
- Latency profile is consistent with extended pre-generation processing, which suits problems where answer quality matters more than response speed
- Backed by OpenAI, which maintains a widely supported API surface and broad third-party SDK and framework integration
- Sustained generation rate means long-form outputs add relatively little time once the first token arrives
- Listed in our comparison catalog with live per-provider pricing, so hosting options can be compared directly
Limitations
- Time to first token measured at roughly 99 seconds — unsuitable for latency-sensitive or streaming-chat user experiences
- Context window length is not confirmed in our data, so maximum input size should be verified in OpenAI's documentation
- We do not track published accuracy benchmarks (reasoning, coding, math) for this model, making capability comparisons against peers difficult
- Modality support is not recorded in our metadata; do not assume image, audio, or file input without checking the provider
- Total end-to-end response time is dominated by upfront processing, which complicates per-request timeout and retry configuration
Key Features
•Chat/completions-style conversational interface
•Measured output throughput of ~125.67 tokens per second (Artificial Analysis)
•Measured time to first token of ~99,227 ms (Artificial Analysis)
•Part of OpenAI's GPT-6 model family
•Latency characteristics consistent with extended pre-response processing
•Available through providers listed in the pricing table on this page
•Token-metered billing with separate input and output rates typical of OpenAI-family endpoints
About GPT-6 Sol
GPT-6 Sol is a chat-oriented large language model created by OpenAI and listed in the GPT-6 family. Our catalog entry for it is intentionally narrow: beyond the creator, model type, and independent throughput measurements, we do not currently track confirmed figures for its context window, modality support, or published benchmark scores. Where those fields are blank here, treat them as unknown rather than absent — check OpenAI's own model documentation for authoritative specifications before building against them.
The one hard data point we do have comes from Artificial Analysis, which measured GPT-6 Sol at approximately 125.67 output tokens per second with a time to first token of roughly 99,227 ms (about 99 seconds). That combination — respectable sustained generation speed paired with a very long initial latency — is the signature of a model that performs substantial work before emitting its first visible token, as extended-thinking and deliberate-reasoning systems typically do. For application design, the practical consequence is that total response time is dominated by the pre-generation phase, not by how many tokens you ask for.
In practice, that latency profile shapes where GPT-6 Sol fits. It is poorly matched to interactive chat surfaces where a user is waiting on a blinking cursor, and better matched to asynchronous or batched work where a minute of upfront processing is acceptable in exchange for a more considered answer. Providers differ in how they host and meter models like this, so both measured latency and cost can vary; consult the pricing table on this page for current per-provider figures.
Common Use Cases
GPT-6 Sol's measured latency profile — roughly 99 seconds to first token followed by steady ~126 tokens/second generation — points it toward asynchronous and batch workloads rather than live conversational interfaces. Reasonable fits include offline document analysis, overnight code review or refactoring passes, research summarization queues, agentic pipelines where a planning step runs out of band, and evaluation or data-generation jobs where throughput per hour matters more than perceived responsiveness. It is a poor match for autocomplete, real-time customer-facing chat, voice agents, or any interaction where a user waits on screen. If you need sub-second first-token latency, pair GPT-6 Sol with a faster small model that handles the interactive layer and escalate only the hard requests. Because we do not track confirmed context window or modality data for this model, validate those requirements against OpenAI's documentation before committing it to a production pipeline.
Frequently Asked Questions
How much does GPT-6 Sol cost?
Pricing varies by provider and by pricing type — input tokens, output tokens, cached input, and batch or committed-capacity rates are all billed differently, and providers update their rates independently. See the pricing table on this page for current per-provider figures rather than relying on any fixed number.
What is GPT-6 Sol best used for?
Asynchronous and batch workloads where a long upfront processing delay is acceptable: offline document and codebase analysis, research synthesis, background agentic planning steps, and bulk generation or evaluation jobs. Its measured ~99 second time to first token makes it unsuitable for interactive chat, autocomplete, or voice applications.
Why is the time to first token so high?
Artificial Analysis measured roughly 99,227 ms before the first token appears, with generation then proceeding at about 125.67 tokens per second. That pattern typically indicates the model performs substantial processing before streaming output. Whatever the mechanism, the practical effect is that total latency is driven by the pre-generation phase, so budget timeouts accordingly.
What context window does GPT-6 Sol support?
We do not have a confirmed context window figure for GPT-6 Sol in our database. Check OpenAI's model documentation or your chosen provider's API reference for the authoritative limit before designing around large inputs.
Does GPT-6 Sol accept images or other non-text input?
Our metadata does not record modality support for this model, so we cannot confirm it either way. Verify supported input types with OpenAI or the specific provider you plan to use.
How do I decide between GPT-6 Sol and a faster model?
Compare on latency tolerance first. If your workload is user-facing and needs a response within a few seconds, the measured ~99 second first-token delay rules GPT-6 Sol out and a lower-latency model is the better choice. If the work runs in a queue and throughput per job matters more than responsiveness, GPT-6 Sol's ~126 tokens/second generation rate is competitive; compare the resulting cost per job using the pricing table above.