For difficult math in ChatGPT, GPT-6 Pro, powered by GPT-6 Astra, is a documented candidate to try—not a proven overall winner. OpenAI reports that Astra scored 98% on FrontierMath Tier 4 (v2), an evaluation of exceptionally difficult mathematics. That score is evidence about one benchmark, not proof that Astra is best for every math task or will solve a particular problem correctly. (openai.com)
For a production API workflow, evaluate the documented model gpt-6-astra if capability on demanding problems is your priority. The benchmark result alone does not establish that it is the best API choice for your workload. (epoch.ai)
Quick recommendation
| What you need | Model or approach | What the evidence supports |
|---|---|---|
| A ChatGPT candidate for difficult math | GPT-6 Pro, powered by GPT-6 Astra | OpenAI reports Astra’s 98% FrontierMath Tier 4 (v2) score. Treat it as evidence for a demanding-math candidate, not an overall ranking. GPT-6 Pro access depends on plan and workspace eligibility. (openai.com) |
| An API candidate for demanding math | Evaluate gpt-6-astra with the Responses API; consider Code Interpreter when calculations need executable code |
OpenAI’s API documentation lists Astra for the Responses API and Code Interpreter. Benchmark performance does not by itself predict results on your production workload. (epoch.ai) |
| A lower-cost API option | Test a lower-cost model against your own answer-keyed examples | Lower token prices do not establish comparable math accuracy. Choose only after testing your workload. |
| Routine arithmetic or calculations that must be exact | Use a calculator or executable code; use ChatGPT to explain or interpret the result | A model’s explanation is not a substitute for checking the computation. The API Code Interpreter can run Python in a sandbox. (epoch.ai) |
What Astra’s benchmark result does—and does not—show
OpenAI reports that GPT-6 Astra scored 98% on FrontierMath Tier 4 (v2). Epoch AI describes Tier 4 as 43 exceptionally difficult research-level problems. Its benchmark page says the v2 update was released on June 12, 2026; that is the benchmark update date, not necessarily the date Astra was evaluated. The page lists a highest score of 100% across 78 tested models, so Astra’s reported 98% should not be described as the top score on the benchmark. (openai.com)
The benchmark’s evaluation setup allows models to reason and run Python, with a token limit of up to one million. That differs from simply asking a question in ChatGPT, and the public passages cited here do not establish the exact test-time configuration behind Astra’s 98% result. The score supports considering Astra for difficult math; it does not establish that it is the best model across all competitors, tasks, or public ChatGPT settings. (epoch.ai)
In ChatGPT, the relevant option is called GPT-6 Pro and is powered by GPT-6 Astra. OpenAI says access is available on eligible Pro, Business, and Enterprise plans; Enterprise access can depend on workspace permissions. (openai.com)
Which API model should you use for a math product?
If capability on hard problems matters more than token price, start by evaluating gpt-6-astra. OpenAI’s API documentation lists it for the Responses API and supports Code Interpreter, which lets a model write and run Python in a sandbox.
Standard API token prices are $10 per million input tokens and $50 per million output tokens. Cached input is listed at $1 per million tokens. These are token rates, not a guaranteed per-problem cost: prompts, reasoning, output length, caching, and tools affect the total.
For a lower-cost workflow, compare candidate models using the problems your product actually receives. The benchmark result cited above does not show that a lower-cost model matches Astra on your use case. ChatGPT names and API identifiers are not interchangeable; for API use, check the documented identifier and billing separately from your ChatGPT subscription.
The following Python example illustrates a tool-assisted calculation request. It is not a reported model test:
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-6-astra",
tools=[
{
"type": "code_interpreter",
"container": {"type": "auto", "memory_limit": "4g"},
}
],
instructions=(
"For calculations, use Python to compute and check the result. "
"Explain the method and state any assumptions."
),
input="A batch has 240 items. If 7.5% are defective, how many are defective?",
)
print(response.output_text)
The arithmetic result is 18 defective items. OpenAI’s documentation describes Code Interpreter as running Python in a sandbox and shows it being used through the Responses API.
Use a tool when the answer needs computation, and verify the result
- Need an explanation or solution strategy? Ask the model to state its assumptions and give a concise derivation.
- Need exact arithmetic, repeated calculations, statistics, or a simulation? Use a calculator or executable code, and check that the code matches the problem you intended to solve.
- Need a proof or research-level claim? Treat the response as a proposed argument, not a certified proof. Check each inference; where suitable, formalize the proof in a proof assistant. OpenAI describes Lean as a language that allows mathematical proofs to be checked by a computer.
For API selection, build a small test set from the problems your product actually receives. Include known answers, edge cases, and representative harder problems. Run each candidate under the same instructions and tool setup; score correctness separately from explanation quality, then compare latency and total cost. This is a suggested evaluation procedure, not a test performed for this article.
Bottom line: For hard math in ChatGPT, GPT-6 Pro (GPT-6 Astra) is a documented candidate, not a proven universal best. For an API deployment, evaluate gpt-6-astra when capability is the priority, and test alternatives on your own answer-keyed workload. Use executable tools for precise computation and independently verify consequential answers.
Sources
GPT-6 Astra: A new generation of intelligence
FrontierMath Tier 4 (v2) | Epoch AI
GPT-6 Astra Model | OpenAI API