GPT-6 Astra for Coding: Why Is Its Index Below GPT-5.6 Sol?
In this article
GPT-6 arrives, but ModelRank shows 112 beside its name and 113 beside GPT-5.6 Sol. Did the new model go backwards?
The available evidence does not establish that. Both are in ModelRank’s S tier. The displayed integers come from two benchmark measurements, not evidence that Sol consistently beats GPT-6 in real development.
Where 112 comes from
In the September 7, 2026 snapshot, AA’s GPT-6 Astra max entry records 88.4% on Terminal-Bench 2.1 and 56.5% on SciCode.
ModelRank divides each result by the corresponding DeepSeek V4 Flash 0731 result, takes the geometric mean and multiplies by 100. GPT-6’s rounded index is 112; Sol’s is 113.
Different test combinations can produce different orders. The saved Artificial Analysis Coding Agent Index v1.4 comparison records Codex + GPT-6 Astra at about 67. That evaluates a model-and-agent system. Its 67 cannot be compared directly with ModelRank’s 112.
This is why the site uses tiers. There is support for putting GPT-6 and Sol in a strong candidate group, without turning one displayed point into a stable capability difference. A newer release also needs evidence rather than an automatic promotion.
The Fable comparison is more informative
| Saved measurement, September 7 | GPT-6 Astra | Fable 5.1 |
|---|---|---|
| AA Terminal-Bench 2.1 | 88.4% | 91.4% |
| AA SciCode | 56.5% | 63.1% |
| LiveBench Coding mean | 80.36 | 86.38 |
| ModelRank editorial tier | S | S+ |
These measurements favor Fable. Trying it first in a new coding workflow is a defensible starting point. They still do not establish a win in every repository, language or agent setup.
GPT-6’s AA configuration is max effort. Fable’s is Adaptive Reasoning, Max Effort, Default Fallback. Keeping those labels matters: stripping a result down to a model name can hide part of what was tested.
Already using Codex? Keep records before changing it
Choose a familiar repository issue with failing tests, more than one relevant file and an API that must remain compatible. Fix the starting commit, tool permissions, time limit and token budget.
Run GPT-6 with your current configuration, then run the alternative from the same starting point. Inspect whether the patch applies, whether tests were weakened and whether unrelated files changed. Record the minutes you spend reviewing the result.
A longer explanation need not produce a better patch. One successful run is not a reliable conclusion. A small representative trial can eliminate obvious problems before you expand it.
If GPT-6 continues to complete your daily work reliably, a one-point index gap gives no compelling migration instruction. If a particular task class repeatedly needs human repair, compare Fable on that class. That makes the decision much more concrete.
Which GPT-6 price does this article use?
ModelRank’s saved September 6 OpenRouter quote is $10 per million input tokens and $50 per million output tokens. A worked example of 100,000 input plus 20,000 output tokens costs $2, before caching, tools and extra retries.
Official OpenAI GPT-6 documentation returned HTTP 403 during this review. We could not recheck standard pricing or product limits. This is explicitly a channel record, not a statement about ChatGPT allowances or Codex subscription charges.
Before integrating it, confirm the actual API model ID, supplier, available reasoning settings and bill. GPT-6 here specifically means GPT-6 Astra. A different configuration or product interface is not automatically the same evaluated object.
Keep a stable default; investigate the difficult work
GPT-6 remains an S-tier editorial candidate. A workflow you have already validated can keep running. Use failure and repair records to decide which tasks need a different model.
Read the Fable 5.1 vs GPT-6 comparison or inspect GPT-6’s source measurements. For mostly small jobs, the three-Flash-model guide is a better place to investigate default-call costs.
Sources
AA GPT-6, AA Fable, AA Claude Code/Codex comparison, LiveBench data, GPT-6 channel record.
This is a public-evidence analysis. The repository tasks are suggested acceptance checks. Unpublished evaluation dates, budgets and sample sizes remain unknown.