# ChatGPT vs Gemini

Compare the listed OpenAI and Google model rows on public task evidence and API rates, with separate limits for app access and workload testing.

As of October 10, 2026, this task reading compares GPT-6 Astra with Gemini 4 Argon using public model benchmarks. Test app routing, upload limits, tools, and finished work separately.

ChatGPT has higher coding and knowledge point estimates. Conditional ranges do not establish rank confidence. Writing and visual rows are not comparable.

## Gemini vs ChatGPT by task

| Task and public lane | ChatGPT model row | Gemini model row | Basis | Reading |
|---|---|---|---|---|
| [Coding](/coding) | [GPT-6 Astra](/models/gpt-6-astra): 76.1 · #4 · Supported | [Gemini 4 Argon](/models/gemini-4-argon): 72.4 · #5 · Supported | Like-for-like | GPT-6 Astra has the higher point estimate. Conditional ranges do not establish rank confidence. |
| [Agents and automation](/agentic) | [GPT-6 Astra](/models/gpt-6-astra): 70.7 · #7 · Supported | [Gemini 4 Argon](/models/gemini-4-argon): 75.3 · #4 · Estimated | Directional | Gemini 4 Argon has the higher point estimate. Conditional ranges do not establish rank confidence. |
| [Research and knowledge](/knowledge) | [GPT-6 Astra](/models/gpt-6-astra): 86.2 · #3 · Supported | [Gemini 4 Argon](/models/gemini-4-argon): 79.8 · #8 · Supported | Like-for-like | GPT-6 Astra has the higher point estimate. Conditional ranges do not establish rank confidence. |
| [Reasoning](/reasoning) | [GPT-6 Astra](/models/gpt-6-astra): 93.4 · #1 | [Gemini 4 Argon](/models/gemini-4-argon): Not publicly ranked | Not comparable | At least one model is not publicly ranked on this lane. No task winner is established. |
| [Writing: brief adherence](/instruction-following) | [GPT-6 Astra](/models/gpt-6-astra): Not publicly ranked | [Gemini 4 Argon](/models/gemini-4-argon): Not publicly ranked | Not comparable | At least one model is not publicly ranked on this lane. No task winner is established. |
| [Images, charts and PDFs](/multimodal-grounded) | [GPT-6 Astra](/models/gpt-6-astra): Not publicly ranked | [Gemini 4 Argon](/models/gemini-4-argon): Not publicly ranked | Not comparable | At least one model is not publicly ranked on this lane. No task winner is established. |

### Gemini vs ChatGPT for coding

GPT-6 Astra has the higher point estimate. Conditional ranges do not establish rank confidence. The [coding leaderboard](/coding) records benchmark tasks and their evidence. It does not measure the editor, prompt, repository access, or agent you put around the model. Test the change you need made in the setup you will actually use.

At the budget end, the GPT-5.6 Luna and Gemini 3.8 Flash comparison reads: GPT-5.6 Luna has the higher point estimate. Conditional ranges do not establish rank confidence. Their listed API rates are $0.20 / $1.20 and $0.75 / $3.75 per million input/output tokens. Those are model rates, not chat subscription prices.

### Gemini vs ChatGPT for writing

A brief-adherence score and a writing choice answer different questions. The instruction-following row for GPT-6 Astra and Gemini 4 Argon reads: At least one model is not publicly ranked on this lane. No task winner is established. It checks constraints represented by the benchmark, rather than whether a draft fits your reader, sounds right, or makes a useful argument.

Compare one real brief, its source material, and the same review criteria in both apps. Check factual support, tone, and the edits needed to accept the draft. A general human-preference rating is a reason to inspect examples, with its uncertainty intact. It does not turn the missing writing evaluation into a measured winner.

### Gemini vs ChatGPT for research and long documents

For GPT-6 Astra and Gemini 4 Argon, the knowledge reading is: GPT-6 Astra has the higher point estimate. Conditional ranges do not establish rank confidence. The reasoning reading is: At least one model is not publicly ranked on this lane. No task winner is established. Knowledge questions and reasoning tasks can screen a model. They do not establish whether a research answer uses the right document or whether its citations support the claim.

The budget knowledge rows for GPT-5.6 Luna and Gemini 3.8 Flash read: Gemini 3.8 Flash has the higher point estimate. Conditional ranges do not establish rank confidence. For document work, try a question answered deep in a file, a question the file cannot answer, and two conflicting passages. A listed context allowance is an input limit. Test retrieval, citation support, and the app's actual upload limits separately.

### Gemini vs ChatGPT for images, charts and PDFs

The current visual-understanding row for GPT-6 Astra and Gemini 4 Argon reads: At least one model is not publicly ranked on this lane. No task winner is established. A missing public rank is a coverage limit. It does not show that a model failed every visual task or cannot read your file.

Older measured rows provide context: GPT-5.6 Sol against Gemini 3.1 Pro reads: GPT-5.6 Sol scores higher, with Estimated evidence or a different benchmark basis. No task winner is established. Those are different models. For the app you intend to use, check a chart's units, a scanned page, and a PDF question against their source. The [multimodal evidence](/multimodal-grounded) concerns understanding; it does not compare generated images or videos.

## Access and price limits

Google limits [Gemini 4 Argon’s rollout](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/) to trusted cyber defenders through the Fairwind Program. Its ranking row does not establish access in the Gemini app. Check the model and plan available to you before using either lineup as a shortlist.

Listed API rates use the same unit; access, context tiers, caching, and tool charges can change your bill. Check the current [OpenAI API rates](/openai/api-pricing) and [Google API rates](/google/api-pricing). Compare chat subscription terms separately.

Last updated: October 10, 2026

Canonical page: https://benchlm.ai/compare/chatgpt-vs-gemini