GPT-6 Astra vs Gemini 4 Argon: 2026 comparison

GPT-6 Astra and Gemini 4 Argon in brief

GPT-6 Astra and Gemini 4 Argon both support very long contexts, reasoning and tool calls. Their context capacities are almost identical, while their prices, supported media and output limits differ substantially. Gemini accepts audio and video, offers a higher maximum output and charges one fifth of GPT's published token rates. GPT has a slightly higher Epoch capability index. Individual benchmarks produce a mixed picture, however, making the relevant question which measured strengths and practical constraints match your work rather than which model leads overall.

GPT-6 Astra : GPT-6 Astra (OpenAI): $10 / $50 per million tokens, 1.1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

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

Both meet the same need, which makes the comparison useful. We compare them on 11 verifiable criteria, using the same data as their profiles, collected from official sites, OpenRouter and Epoch AI.

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. GPT-6 Astra : 168.8. Gemini 4 Argon : 166.5. Edge: GPT-6 Astra.

Shared benchmarks

On the benchmarks both models took, we count each one's wins. GPT-6 Astra : 2 of 6. Gemini 4 Argon : 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. GPT-6 Astra : $10 / M tokens. Gemini 4 Argon : $2 / M tokens. Edge: Gemini 4 Argon.

Output price

Generated tokens usually cost several times more; they dominate the bill for writing and code. GPT-6 Astra : $50 / M tokens. Gemini 4 Argon : $10 / M tokens. Edge: Gemini 4 Argon.

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). GPT-6 Astra : $1,350 per month. Gemini 4 Argon : $270 per month. Edge: Gemini 4 Argon.

Context window

The context window sets how much text the model reads at once: useful for long documents and agents. GPT-6 Astra : 1.1M tokens. Gemini 4 Argon : 1M tokens. Edge: GPT-6 Astra.

Max output

The maximum answer length matters to generate long code files or reports. GPT-6 Astra : 128K tokens. Gemini 4 Argon : 943.7K tokens. Edge: Gemini 4 Argon.

Accepted inputs

Text, images, files, audio: the more formats accepted, the more cases covered without extra tools. GPT-6 Astra : 3. Gemini 4 Argon : 5. Edge: Gemini 4 Argon.

Tool calling

Essential for an agent: the model can call functions, APIs or MCP servers. GPT-6 Astra : yes. Gemini 4 Argon : yes. A tie on this point.

Open weights

Downloadable weights let you run the model yourself, without sending data to a provider. GPT-6 Astra : no. Gemini 4 Argon : no. A tie on this point.

Release date

A newer model often has fresher data and better training techniques. GPT-6 Astra : September 4, 2026. Gemini 4 Argon : October 13, 2026. Edge: Gemini 4 Argon.

In charts

Our analysis

OpenAI released GPT-6 Astra on September 4, 2026, with file, image and text inputs. Google released Gemini 4 Argon on October 13, 2026, supporting those formats alongside audio and video. Both models provide reasoning and tool calling, making them relevant to tasks that involve processing material and interacting with tools. Gemini's declared input coverage is broader, whereas GPT's distinctions in these records mainly concern evaluation results. Neither record identifies a professional specialization or describes the applications through which you would use the model. Your shortlist should therefore reflect the source material you handle and the requirements you need to verify in practice.

GPT's Epoch index is 168.81, versus 166.45 for Gemini. GPT leads on FrontierMath at 93.7% versus 86%, and on ARC2 at 95% versus 91.3%. Gemini leads on GPQA at 96.4% versus 95.8%, and on SimpleQA at 76% versus 75.6%. Both score 100% on AIME and 98.5% on ARC1. GPT charges USD 10 per million input tokens and USD 50 per million output tokens; Gemini charges USD 2 and USD 10. Their context windows are 1,050,000 and 1,048,576 tokens respectively. Maximum output is 128,000 tokens for GPT and 943,718 for Gemini, a separate consideration from the benchmark scores.

Both publishers are listed as US companies. Neither model has open weights, and neither record specifies a license. The supplied information also leaves data retention, secondary use of submitted content and processing locations unspecified. There is consequently no basis here for assigning either model a privacy advantage. Support for files, images or recordings tells you which material the model accepts, but it does not establish the terms that govern that material afterward. If your work involves sensitive content, you need the conditions of the service you intend to use. A capability index or a lower token price cannot answer those separate questions about data handling.

Gemini is a practical candidate when your work combines text, audio and video, because it lists all three input types. Its lower token rates also matter when you expect substantial usage. GPT remains relevant if its published FrontierMath or ARC2 results align with the tasks you need to evaluate. Its slightly larger context window is a small difference between otherwise similar context limits. Gemini's much higher output ceiling may support longer requested responses, but does not measure their quality. Both offer tool calling without detailing integrations, and neither record supplies latency measurements, leaving responsiveness in an interactive workflow unresolved by this comparison.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (GPT-6 Astra value / Gemini 4 Argon value).

High request volume

Here, Gemini 4 Argon has the edge: it wins 3 of the 3 criteria that matter in this situation, namely cost of a typical task ($1,350 per month / $270 per month), input price ($10 / M tokens / $2 / M tokens), and output price ($50 / M tokens / $10 / M tokens).

Hard tasks (reasoning, code)

Neither stands out on the criteria for this situation (capabilities (epoch ai index) (168.8 / 166.5), shared benchmarks (2 of 6 / 2 of 6), and max output (128K tokens / 943.7K tokens)): decide with a trial.

Agents and long documents

Neither stands out on the criteria for this situation (tool calling (yes / yes), context window (1.1M tokens / 1M tokens), and accepted inputs (3 / 5)): 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 GPT-6 Astra if…

• you value capabilities (epoch ai index) (168.8)
• you value context window (1.1M tokens)

GPT-6 Astra fits readers who give particular weight to the published FrontierMath and ARC2 results and primarily submit text, images or files. It also has a slightly higher Epoch index. That choice needs a concrete justification in your work: its token rates are five times Gemini's, and its maximum output is lower. Test whether the measured advantages matter for your tasks.

Choose Gemini 4 Argon if…

• you value input price ($2 / M tokens)
• you value output price ($10 / M tokens)
• you value cost of a typical task ($270 per month)
• you value max output (943.7K tokens)
• you value accepted inputs (5)

Gemini 4 Argon fits workloads that benefit from lower token prices, audio and video input, or a much higher output ceiling. It also leads on the supplied GPQA and SimpleQA scores. Check your priorities carefully, however: GPT leads on FrontierMath and ARC2. The near equality of their context windows means context size alone provides little reason to prefer one.

Strengths and limits of each

GPT-6 Astra

• + Among the top 20% of models according to Epoch AI (ECI 168.8).
• + 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 97 % of paid models.

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.

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-6.1 Sol, Claude Fable 5.1, Claude Sonnet 5.5, Claude Opus 5, and GPT-6 Sol.

Related comparisons:

• Gemini 4 Argon vs GPT-6.1 Sol
• Claude Opus 5.5 vs Gemini 4 Argon
• Claude Opus 5.5 vs GPT-6 Astra

Conclusion

Gemini combines lower prices, broader input support and a higher output limit. GPT leads on the Epoch index, FrontierMath and ARC2. These differences support a decision based on cost, source formats and relevant measured performance. Start with your actual workload. Where GPT's benchmark advantages appear relevant, compare both on representative tasks to determine whether those advantages justify its higher token rates. Privacy and speed remain unresolved in the supplied records.