Gemini 4 Argon vs GPT-6.1 Sol: 2026 comparison

Gemini 4 Argon and GPT-6.1 Sol in brief

Gemini 4 Argon and GPT-6.1 Sol both offer reasoning, tool calling and context windows around one million tokens at the same listed token prices. Their most practical differences involve supported inputs and maximum output length. Gemini also accepts audio and video and permits a much longer output. GPT has a higher Epoch capability index and stronger results on some supplied benchmarks. The useful comparison therefore starts with the media your application must handle and the capabilities that matter for your particular workload.

Gemini 4 Argon : Gemini 4 Argon (Google): $2 / $10 per million tokens, 1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

GPT-6.1 Sol : GPT-6.1 Sol (OpenAI): $2 / $10 per million tokens, 1.1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

They are often weighed against each other by people who hesitate. This page lines up their features point by point (11 criteria), with no partnership with either publisher.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Capabilities (Epoch AI index)

Epoch AI's capabilities index rolls many benchmarks into one number: the higher, the better the model does on hard tasks. Gemini 4 Argon : 166.5. GPT-6.1 Sol : 168.4. Edge: GPT-6.1 Sol.

Shared benchmarks

On the benchmarks both models took, we count each one's wins. Gemini 4 Argon : 2 of 6. GPT-6.1 Sol : 2 of 6. A tie on this point.

Input price

The price of tokens sent to the model weighs heavily as soon as you send long documents or chat history. Gemini 4 Argon : $2 / M tokens. GPT-6.1 Sol : $2 / M tokens. A tie on this point.

Output price

Generated tokens usually cost several times more; they dominate the bill for writing and code. Gemini 4 Argon : $10 / M tokens. GPT-6.1 Sol : $10 / M tokens. A tie on this point.

Cost of a typical task

For a request of 2,000 input and 500 output tokens, repeated 30,000 times a month (1,000 a day). Gemini 4 Argon : $270 per month. GPT-6.1 Sol : $270 per month. A tie on this point.

Context window

The context window sets how much text the model reads at once: useful for long documents and agents. Gemini 4 Argon : 1M tokens. GPT-6.1 Sol : 1.1M tokens. Edge: GPT-6.1 Sol.

Max output

The maximum answer length matters to generate long code files or reports. Gemini 4 Argon : 943.7K tokens. GPT-6.1 Sol : 128K tokens. Edge: Gemini 4 Argon.

Accepted inputs

Text, images, files, audio: the more formats accepted, the more cases covered without extra tools. Gemini 4 Argon : 5. GPT-6.1 Sol : 3. Edge: Gemini 4 Argon.

Tool calling

Essential for an agent: the model can call functions, APIs or MCP servers. Gemini 4 Argon : yes. GPT-6.1 Sol : yes. A tie on this point.

Open weights

Downloadable weights let you run the model yourself, without sending data to a provider. Gemini 4 Argon : no. GPT-6.1 Sol : no. A tie on this point.

Release date

A newer model often has fresher data and better training techniques. Gemini 4 Argon : October 13, 2026. GPT-6.1 Sol : September 29, 2026. Edge: Gemini 4 Argon.

In charts

Our analysis

Google released Gemini 4 Argon on October 13, 2026. It accepts text, images, files, audio and video. OpenAI released GPT-6.1 Sol on September 29, with text, image and file inputs listed. Gemini consequently covers a wider range of input media in the supplied profiles. Both models support reasoning and tool calling, making either relevant to applications that combine analysis with model-directed actions. The information does not specify the detailed handling of each medium or list output formats. In particular, accepting audio or video should not be read as evidence that Gemini can generate either of those media formats.

Both models cost $2 per million input tokens and $10 per million output tokens. Their benchmark results differ by test. Gemini scores 96.4% on GPQA and 76% on SimpleQA, compared with GPT’s 95.4% and 73.9%. GPT leads on FrontierMath, at 93.7% against 86%, and ARC2, at 94.2% against 91.3%. They tie at 100% on AIME and 98.5% on ARC1. The Epoch capability index is 168.37 for GPT and 166.45 for Gemini. These figures support a task-specific comparison rather than a blanket performance claim, since the supplied evaluations point in different directions depending on which test you prioritize.

Both publishers are identified as US companies, and neither model has open weights. The profiles do not specify licenses, so they do not establish particular deployment or reuse rights. They also leave out retention policies, any use of submitted content for training and the location of processing. Those omissions matter when assessing either model for sensitive material. They are especially relevant if your intended Gemini workflow includes recordings or video containing confidential information. Its broader input support expands the types of content you can consider, but says nothing by itself about the privacy conditions governing that content in practice.

The context windows are almost identical: 1,048,576 tokens for Gemini and 1,050,000 for GPT. Maximum output length creates a much larger distinction, at 943,718 tokens for Gemini versus 128,000 for GPT. That declared limit may be decisive if your application requires unusually long generation. It does not establish consistent quality across the full output or an acceptable completion time. Since output token rates are equal, generating more tokens also means paying for more output. Neither profile supplies latency, throughput or operational reliability figures, so those practical tradeoffs remain to be assessed separately using the workflows you actually intend to run.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (Gemini 4 Argon value / GPT-6.1 Sol value).

High request volume

Neither stands out on the criteria for this situation (cost of a typical task ($270 per month / $270 per month), input price ($2 / M tokens / $2 / M tokens), and output price ($10 / M tokens / $10 / M tokens)): decide with a trial.

Hard tasks (reasoning, code)

Neither stands out on the criteria for this situation (capabilities (epoch ai index) (166.5 / 168.4), shared benchmarks (2 of 6 / 2 of 6), and max output (943.7K tokens / 128K tokens)): decide with a trial.

Agents and long documents

Neither stands out on the criteria for this situation (tool calling (yes / yes), context window (1M tokens / 1.1M tokens), and accepted inputs (5 / 3)): decide with a trial.

Maximum privacy

Neither stands out on the criteria for this situation (open weights (no / no)): decide with a trial.

Which one to choose?

Choose Gemini 4 Argon if…

• you value max output (943.7K tokens)
• you value accepted inputs (5)
• you value release date (October 13, 2026)

Gemini 4 Argon is a logical candidate if you need audio or video inputs, neither of which appears in GPT’s supplied input list. It also merits attention when your application requires output beyond 128,000 tokens. Its GPQA and SimpleQA results provide further reasons to evaluate it, while leaving actual task performance and data handling conditions to be checked.

Choose GPT-6.1 Sol if…

• you value capabilities (epoch ai index) (168.4)
• you value context window (1.1M tokens)

GPT-6.1 Sol fits workflows involving text, images and files where the supplied FrontierMath or ARC2 results are especially relevant. Its Epoch capability index is also higher. If your outputs fit within 128,000 tokens, Gemini’s larger generation allowance may carry less weight. With identical listed token rates, you can focus your comparison on observed results for your own tasks.

Strengths and limits of each

Gemini 4 Argon

• + Among the top 20% of models according to Epoch AI (ECI 166.5).
• + Very large context (1M tokens): long documents or a whole codebase fit in one request.
• + Agent-ready: tool calling and structured JSON output.
• − Expensive: costlier than 84 % of paid models.

GPT-6.1 Sol

• + Among the top 20% of models according to Epoch AI (ECI 168.4).
• + Very large context (1.1M tokens): long documents or a whole codebase fit in one request.
• + Agent-ready: tool calling and structured JSON output.
• − Expensive: costlier than 84 % of paid models.

The best test is still yours: take three real tasks from your day and give them to both for a week. The price calculator works out the gap on your own volume.

What about the others?

If neither suits you, look at Claude Opus 5.5, GPT-6 Astra, Claude Fable 5.1, Claude Sonnet 5.5, Claude Opus 5, and GPT-6 Sol.

Related comparisons:

• Claude Opus 5.5 vs GPT-6.1 Sol
• Claude Opus 5.5 vs Gemini 4 Argon
• DeepSeek V4.1 Flash vs GPT-6.1 Sol
• Mistral Large 4 vs GPT-6.1 Sol
• GPT-6 Astra vs Gemini 4 Argon

Conclusion

Gemini’s distinguishing features are audio and video inputs and a substantially higher maximum output length. GPT leads on some supplied evaluations and has the higher Epoch index. Their prices are identical and their context sizes are very close. Base your initial choice on input media and required generation length, then assess representative tasks. The figures do not support treating either model as the stronger choice for every application.