The least useful claim in an AI comparison is that one model can do everything. Fixing code, sorting a stack of documents, and finding a moment in a video ask for different things. This shortlist compares GPT-6 Astra, Claude Opus 5.5, and Gemini 3.1 Pro Preview using official information checked on September 30, 2026. It is not a head-to-head benchmark. Gemini 3.1 Pro Preview is the documented model used here; this guide does not assume a Gemini 4.0 release.
A chat app and a model are different choices
ChatGPT is a product; GPT-6 Astra is a model. The same distinction matters when you use Claude or Gemini through a chat interface rather than an API. A file-upload button in a product does not tell you which formats a particular API model accepts directly. The app can provide tools around the model.
This comparison starts with model documentation, then asks what work to try. It does not mix a monthly chat subscription with usage-based API charges. Those are different purchases. Before paying, decide whether you want a convenient chat workspace or a model inside your own workflow. The right answer for one does not automatically settle the other.
GPT-6 Astra: Documents, code, and demanding tasks
OpenAI positions Astra for complex reasoning, coding, computer use, research, and document creation. The model page lists a 1,050,000-token context window and a maximum output of 128,000 tokens. It accepts text and images; direct audio and video input are not supported. Tasks handled through additional tools should not be confused with those native input capabilities.
That makes it a reasonable first candidate when a job mixes substantial documents and code. A large context window, though, does not promise perfect recall. For a comparison of five reports, I would ask for the source location beside every date and figure. An answer you can audit is more valuable than a long answer you have to trust. That is a way to evaluate the model, not a performance claim about it.
Claude Opus 5.5: Put coding claims to work
Anthropic released Opus 5.5 on September 22, 2026, emphasizing agentic coding, computer use, and knowledge work. Its introduction discusses substantial engineering tasks and multi-step work. Those are useful clues for choosing a trial task, but a manufacturer's examples do not establish that it wins on every codebase.
For development, I would give Astra and Opus the same bug, repository, and tools. Then compare the changed files, whether the fix runs, and how clearly each reports unresolved problems. A polished explanation is not enough if the code still fails. For review work, look for a reproducible problem and a precise location rather than pages of suggestions that never become actionable.
Gemini 3.1 Pro Preview: Start with the input
Google's model page lists text, images, video, audio, and PDF as inputs, with text output. It specifies up to 1,048,576 input tokens and 65,536 output tokens. Code execution, function calling, structured output, and search grounding are among the supported capabilities. Audio or video as source material gives it a clear place on this shortlist.
Accepting video does not mean generating video, and accepting audio does not mean producing speech. The same support table lists image generation, audio generation, and the Live API as unsupported for this model. A meeting recording to summarize and a new voice track to create are different jobs. Preview status also matters when building a lasting integration: account for changes in the model and its availability instead of treating the name as a permanent guarantee.
Use one real task to choose
A prompt like 'write something good' mixes your taste with factual accuracy. A tighter comparison is easier to judge: extract dates and numbers from the same files, or fix the same error in the same repository. Match tool access as well. Giving only one model web search changes the experiment; it no longer isolates the model choice.
For a blog workflow, I would count unsupported claims, check whether the comparison keeps the same criteria, and see whether its table agrees with its prose. Then I would look at the editing still needed. A convincing first draft that takes an hour to fact-check may be less useful than a plainer draft whose evidence is easy to follow. My shortlist is about where to begin that trial, not an invented first-to-third score.
Our three picks
- Documents and codeGPT-6 Astra
Try it first for demanding work involving long text, images, and code, with explicit checks on evidence and execution.
- Agentic codingClaude Opus 5.5
Compare it with Astra on the same code change and tools; judge the working result and the remaining review effort.
- Mixed source materialGemini 3.1 Pro Preview
Start here when audio, video, or PDF is central to the model input and the desired result is text.
Side by side
| What to compare | GPT-6 Astra | Claude Opus 5.5 | Gemini 3.1 Pro Preview |
|---|---|---|---|
| Source of this shortlist | Official OpenAI model documentation | Anthropic's official model introduction | Official Gemini API model documentation |
| Published emphasis | Reasoning, coding, research, document creation | Agentic coding, computer use, knowledge work | Multimodal input and reasoning |
| Input or capacity boundary | Text and images; no direct audio or video | This guide does not assign unverified input-limit figures | Text, images, video, audio, PDF; text output |
| First task I would try | Compare a document set with traceable evidence | Fix the same repository bug as Astra | Summarize or locate information in audio/video/PDF |
| What to keep separate | ChatGPT tools and API model capabilities | Product tools and the model's demonstrated results | Input understanding and media generation |
| What would decide the trial | Correct evidence and working output | Reproduction, fix, and verification | Accurate extraction from the source material |
| Before relying on it | Measure review work on your own tasks | Test on your codebase, not just release examples | Review Preview availability and integration conditions |
The final pick
For documents and code, I would put Astra and Opus through the same task. When audio or video is the core input, I would start with Gemini. Then choose the result that takes less work to verify and repair. The most confident answer is not necessarily the most useful one; give the shortlist a job you actually do and see what survives review.
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